HK1228037A1

HK1228037A1 - Biomarker signature method, and apparatus and kits therefor

Info

Publication number: HK1228037A1
Application number: HK17101354.9A
Authority: HK
Inventors: 理查德．布鲁斯．布兰登; 里奥．查尔斯．麦克休
Original assignee: 因姆内克斯普雷斯私人有限公司
Priority date: 2014-02-06
Filing date: 2015-02-06
Publication date: 2017-10-27

Description

Biomarker identification method, and device and kit therefor

The present application claims priority from australian provisional application No. 2014900363 entitled "Biomarker signature method, and andapparatus and kits heredor" filed on 6.2.2014, the entire contents of which are incorporated herein by reference.

Background

The present invention relates to methods, kits and devices, and reagents and compositions related thereto, for deriving an indicator for use in diagnosing the presence, absence or extent of at least one condition in a biological subject or prognosing at least one condition in a biological subject, to biomarker signatures for use in diagnosing the presence, absence or extent of at least one condition in a biological subject or prognosing at least one condition in a biological subject, and to methods, kits and devices, and reagents and compositions related thereto, for identifying biomarkers for use in biomarker signatures.

Description of the prior art

The reference in this specification to any prior publication (or information derived from it), or to anything that is known, is not, and should not be taken as an acknowledgment or admission or any form of suggestion that prior publication (or information derived from it) or known matter forms part of the common general knowledge in the field of endeavour to which this specification relates.

Analysis of gene expression products for diagnostic purposes is known. Such analysis requires the identification of one or more genes that can be used to generate signatures for distinguishing different conditions. However, such identification may require analysis of many gene expression products, which may be mathematically complex, computationally expensive, and thus difficult. Many biomarker discovery processes are directed to identifying a subset of data that may have relevant inputs, from which an identity is derived using a combination of these values to generate a model for diagnostic or prognostic use.

WO2004/044236 describes a method of determining the status of a subject. In particular, this is achieved by obtaining subject data (comprising respective values for each of a number of parameters) which is indicative of a current biological state of the subject. The subject data is compared to predetermined data, which includes values indicative of at least some of the parameters and conditions. The state of the subject and in particular the presence and/or absence of one or more conditions may then be determined from the results of the comparison.

US2010/0028876 describes methods for diagnosing a biological state or condition by differentiating cell types (including cancer cell types) based on the ratio of gene expression data from a cell or tissue sample, such as a cancer cell or cancer tissue sample. The invention provides a collection of genes differentially expressed in normal and cancer lung cells and tissues that can distinguish between these cells and tissues. Such cell differentiation is important in diagnosing cancer and cancer types. The set of genes was identified by the degree of up-or down-regulation (fold change). The collection of these genes can be used to distinguish normal cells or tissues from malignant cells or tissues, and to distinguish the type of malignant cells or tissues. Thus, diagnostic assays for tumor classification, tumor outcome prediction, selection and monitoring of treatment regimens, and monitoring of tumor progression/regression are also provided.

However, traditional methods for biomarker identification and traditional combinations of markers use a relatively large number of biomarkers, which in turn makes the test expensive to perform, limiting its use in practice. In addition, the prior art does not describe the use of immune system biomarker ratios, or methods of identifying a minimal set of immune system biomarker ratios useful in determining the presence, absence, extent, or prognosis of immune system-mediated medical conditions (medical conditions).

Summary of The Invention

In one broad form, it is an object of the present invention to provide a method for determining an indicator for use in assessing the likelihood of a biological subject having the presence, absence, extent or prognosis of at least one medical condition, the method comprising:

a) determining a pair of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of the concentration of the immune system biomarker in a sample taken from the subject;

b) determining a derived biomarker value using the pair of biomarker values, the derived biomarker value indicating a ratio of concentrations of the pair of immune system biomarkers; and the number of the first and second groups,

c) determining the indicator using the derived biomarker value.

Generally, the method comprises:

a) determining a first derived biomarker value using the first pair of biomarker values, the first derived biomarker value being indicative of a ratio of concentrations of the first and second immune system biomarkers;

b) determining a second derived biomarker value using the second pair of biomarker values, the second derived biomarker value indicating a ratio of concentrations of a third immune system biomarker and a fourth immune system biomarker; and the number of the first and second groups,

c) Determining the indicator by combining the first derived biomarker value with the second derived biomarker value.

Typically, the method comprises combining the derived biomarker values using a combining function, the combining function being at least one of:

a) additive model;

b) a linear model;

c) support vector machine (support vector machine);

d) a neural network model;

e) a random forest model;

f) a regression model;

g) a genetic algorithm;

h) an annealing algorithm;

i) a weighted sum;

j) nearest neighbor model (nearest neighbor model); and the number of the first and second groups,

k) and (4) a probability model.

Typically the method is performed at least in part using electronic processing means.

In general, the method includes, in at least one electronic processing device:

a) obtaining at least two pairs of measured biomarker values, each measured biomarker value being a measured value of a corresponding immune system biomarker of a biological subject;

b) determining a first derived biomarker value indicative of a ratio of concentrations of the first and second immune system biomarkers;

c) determining a second derived biomarker value indicative of a ratio of a third immune system biomarker and a fourth immune system biomarker; and the number of the first and second groups,

d) Determining the indicator by combining the first derived biomarker value with the second derived biomarker value.

Generally, the method includes, in at least one processing device, generating a representation (representation) of the pointing object.

Typically, the representation includes:

a) an alphanumeric indication of the indicator;

b) a graphical indication of a comparison of the indicator to one or more indicator references;

c) an alphanumeric indication of the likelihood that the subject has at least one medical condition.

Generally, the method comprises:

a) comparing the indicator to an indicator reference; and the number of the first and second groups,

b) the likelihood is determined based on the result of the comparison.

Typically, the indicator reference is based on at least one of:

a) an indicator threshold range;

b) an indicator threshold; and the number of the first and second groups,

c) the indicator distribution.

Typically, the indicator reference is derived from indicators determined for a number of individuals in a reference population.

Typically, the indicator reference is based on a distribution of indicators determined for a group of reference populations, the group consisting of individuals diagnosed as having a medical condition or lacking a medical condition.

Typically, the reference population includes:

a) a plurality of individuals of different genders;

b) A plurality of individuals of different ethnicities;

c) a plurality of healthy individuals;

d) a plurality of individuals having at least one diagnosed medical condition;

e) a plurality of individuals lacking at least one of the diagnosed medical conditions;

f) a plurality of individuals exhibiting clinical signs of at least one medical condition;

g) a first group of individuals and a second group of individuals, the individuals of each group having a respective diagnosed medical condition; and the number of the first and second groups,

h) a first group of individuals and a second group of individuals, the first group of individuals having a diagnosed medical condition and the second group lacking the diagnosed medical condition.

Typically, the indicator is for determining a likelihood that the biological subject has at least one medical condition, and wherein the reference population comprises:

a) an individual exhibiting clinical signs of the at least one medical condition;

b) an individual diagnosed as having the at least one medical condition;

c) an individual diagnosed as lacking the at least one medical condition; and the number of the first and second groups,

d) a healthy individual.

Typically, the indicator reference is retrieved from a database.

Generally, the likelihood is based on a probability generated using the results of the comparison.

Typically, the indicator is for determining a likelihood that the subject has a first condition or a second condition, and wherein the method comprises:

a) Comparing the indicator to a first indicator reference and a second indicator reference, the first indicator reference and the second indicator reference indicating a first condition and a second condition; and the number of the first and second groups,

b) the likelihood is determined based on the result of the comparison.

Generally, the method comprises:

a) determining a first indicator probability and a second indicator probability using the result of the comparison; and the number of the first and second groups,

b) combining the first indicator probability and the second indicator probability to determine a condition probability indicating a likelihood.

Typically, the first indicator reference and the second indicator reference are distributions of indicators determined for a first set of reference populations and a second set of reference populations, the first set and the second set consisting of individuals diagnosed with a first condition and individuals of a second condition, respectively.

Generally, the method comprises:

a) obtaining a sample collected from a biological subject, the sample comprising a polynucleotide expression product; and the number of the first and second groups,

b) quantifying at least some of said polynucleotide expression products within said sample to determine a pair of biomarker values.

Typically, the method comprises determining the indicator at least in part using a ratio of the concentrations of the polynucleotide expression products.

Generally, the method comprises:

a) quantifying the polynucleotide expression product by:

b) amplifying at least some polynucleotide expression products in the sample; and the number of the first and second groups,

c) determining an amount of amplification representing the degree of amplification required to obtain a defined level of each of a pair of polynucleotide expression products; and the number of the first and second groups,

d) determining the indicator by determining a difference between the amounts of amplification.

Typically, the amplification amount is at least one of:

a) the cycle time;

b) the number of cycles;

c) a cycling threshold;

d) the amplification time; and the number of the first and second groups,

e) relative to the amount of amplification of another amplified product.

Generally, the method comprises:

a) determining a first derived biomarker value by determining a difference between amplified amounts of a first pair of polynucleotide expression products;

b) determining a second derived biomarker value by determining a difference between the amplified amounts of the second pair of polynucleotide expression products;

c) determining the indicator by adding the first derived biomarker value and the second derived biomarker value.

Typically, the immune system biomarker is a biomarker of the immune system of the biological subject that is altered as part of an inflammatory response to a lesion or pathogenic insult (pathogenic insult), or the expression level of the biomarker of the immune system of the biological subject is altered as part of an inflammatory response to a lesion or pathogenic insult.

In general:

a) at least two immune system biomarkers have a cross-correlation for at least one condition within a cross-correlation range, the cross-correlation range being between ± 0.9; and the number of the first and second electrodes,

b) the indicator has a performance value greater than or equal to a performance threshold representing the indicator's ability to diagnose the presence, absence, extent or prognosis of at least one condition, the performance threshold indicating an explained variance (extended variance) of at least 0.3.

Typically, the cross-correlation range is at least one of:

a)±0.8；

b)±0.7；

c)±0.6；

d)±0.5；

e)±0.4；

f)±0.3；

g) plus or minus 0.2; and the number of the first and second groups,

h)±0.1。

typically, each immune system biomarker has a condition correlation (condition correlation) with the presence, absence, extent, or prognosis of at least one condition outside of a condition correlation range, which is between ± 0.3.

Typically, the condition relevance range is at least one of:

a)±0.9；

b)±0.8；

c)±0.7；

d)±0.6；

e) plus or minus 0.5; and the number of the first and second groups,

f)±0.4。

typically, the performance threshold indicates an interpretation variance of at least one of:

a)0.4；

b)0.5；

c)0.6；

d)0.7；

e) 0.8; and the number of the first and second groups,

f)0.9。

typically, the immune system biomarker value is indicative of the level or abundance of a molecule selected from one or more of a nucleic acid molecule and a proteinaceous molecule.

In some embodiments, the indicator is for determining a likelihood of the subject having inSIRS or ipSIRS, and wherein the method comprises:

a) determining a first pair of biomarker values indicative of the concentration of polynucleotide expression products of the PLA2G7 gene and the PLAC8 gene;

b) determining a second pair of biomarker values indicative of the concentration of polynucleotide expression products of the CEACAM4 gene and the LAMP1 gene; and the number of the first and second groups,

c) determining the indicator using the first pair of biomarker values and the second pair of biomarker values.

In some embodiments, the indicator is for determining a likelihood of the subject having inSIRS or a healthy condition, and wherein the biomarker value is determined from at least one Inflammatory Response Syndrome (IRS) immune system biomarker in each of a first IRS immune system biomarker group and a second IRS immune system biomarker group, wherein:

a) the first IRS immune system biomarker group consists of polynucleotide expression products and/or polypeptide expression products from the following IRS immune system biomarker genes: NUMB, RAB27, USP// LOC100130855, HIF1, LBXCOR// PIAS// CALML, SQRDL, C20orf, IL10, PARP, DNTTIP, MTMR// LOC 6482, LAMP, MAPK, SERINC// TTPAL, IGSF// METTL, RP, C18orf, LOC284757, MTMR// MTMR, SLC12A, LCP, CHP, PRR, C20orf177, ZFP106, DICER, PHFF, IFNAR, BNIP, UBE2, NIN, MBD// SNORA, TM9, RAB8, CLIP, WAS, DNAJC, CDADC, KIAA0317, MED13, SNS, PDK, MYO5, NUPL, VEZF, CUL4, USP 9/USP 9, RPS6, IL 17/CR, TEAL/CR, ZN 28K, LAA 5, LAD// TARD 1, LAK 2, LAK, TARG// SALT 2, TARG, TAAA, TARG, TAA, TARG, ZYM 5// ZYM 2, FNDC3A, NUFIP2, STRADA, SPG11// ISLR, SPATA13// C1QTNF9, BRWD3, BACH1, CLTC, LIG4, C21orf41// BACH1, KPNB1, DHRS7, USP8, LACTB, SYNE2, ZDHHC20// LOC728099, EAPP, MED13// LOC 129100112, TAOK3, NLGN3, CIT, RIPK3, CP110, ABHD2, GNA13, GGNBP2, PXN, and PTPN1 (hereinafter referred to interchangeably as "group A IRS immune system biomarker genes" or "group A IRS biomarker genes"); and is

b) The second IRS immune system biomarker group consists of polynucleotide expression products and/or polypeptide expression products from the following IRS immune system biomarker genes: AGFG, BMX// HNRPDL, MCM, TRIM, GRWD, ZNF574, ARRDC, PELP, SHPK, GPS, FAM38, FBXO, C16 orf// LOC100128371, NLRC, JMJD, CDK, TRAPPC2, PRMT, BRF, MTA// LOC647310// LOC100128343, PLD, DDX// CCDC42, PLBD, IRAK, FGD, ARG, RANGAP, UNC84, SAMSN// LOC388813, PFKL, S100A, KIF, LRRN, CCDC134, LZTR, GZMM, ICAM, TMC, LAT// SPNS 7234/NPIPL// ZC 8741// LOC730153// SPIPL// SPP 33, SAM// LOC 8826/OCL, LASL 7288// PDL 9169/SBAD, LAPR, TAMPD, TAP, LAP, TAMPD, TAP, TAMPD, TAP, TAB, TAP, U2AF2, PYGL, SOS2, ANKRD22, MEGF9, MGAM// LOC100124692, IL1R2, IL2RB, FCAR, IL27RA, DHX37, PATZ1, PRDM15, NOSIP, RPTOR, SPG7, DNAJA3, VNN1, SEPT9, THAP11, LPCAT2, PRAM 2C1, PITPNM2, ZN 2L// SAMD 11/LOC401010, TRPMYM 3, FTP 1, KCNE1, ACTR5, FAM110A, FAM134C, LLGL C, INF C, KDM 2C, ACSL C, GALT 72, CD 79C, BCL 3611C, ERGL C, ERPG C, SLN C, SLP 10011, SLP C, SLP 2 OCHRTF C, SLP C, PHR C, SLCP C, PHR 2C C, PHR 36K C, PHR 36363636363672, PHR C, PHR 363672, PHR C, PHR 3636363672, PHR 36363672, PHR 363636363636363636363636363636363672, PHR 363672, PHR C, PHR 36, USP36, ZBTB4, TSC2, KIAA0195, KIAA0182, ALOX5AP, TGIF2, ST20// C15orf37, FN3KRP, ABCD4, ZFP64, NEO1, PPIL2// YPEL1, RNPS1, NF2, SERPINB1, DDX51, PRPF6, TIMM22, SYS1// SYS1-DBNDD2// DBNDD2, RAB31, KRI1, SMARCA KRI1, CLUAP KRI1, C16orf KRI1, C20orf KRI1, CHTF KRI 1/KRI 1, NPTN, CSRP2 KRI1, AES, ODZ KRI1, MTMR KRI 1// MTMTP KRI1, SIRPD, SIRET KRI 1/SNORF KRI1, SNORP KRI 1/SNORP KRI1, SNAK KRI 1/SNOR KRI1, SNOR KRI 1/SND KRI1, SNOR KRI 1/SNOR KRI1, SND KRI 1/SNOR KRI1, SNOR KRI 1/SND KRI1, SNOR KRI 1/SNOR KRI1, SNOR KRI 1/SND KRI 1/SNOR KRI1, SND KRI1, SNOR KRI 1/SND KRI1, SNOR KRI1, SND KRI 1/SNOR KRI 1/SND KRI1, SNOR KRI1, SND KRI1, SNOR KRI 1/SND KRI1, SNOR 2/SNOR KRI1, PSN KRI 1/SND KRI1, SNOR KRI 1/SND KRI1, PSN KRI1, SNOR KRI1, PSN KRI 1/SNOR KRI1, SNOR KRI 1/SNOR 2/SNOR 36 19// PAR5// PAR-SN// SNORD116-2// SNORD116-25// SNORD116-26// SNORD107// SNORD115-12// SNORD115-5// SNORD115-6// SNORD115-9// SNORD116-11// SNORD116-12// SNORD116-13// SNORD116-28// SNORD116-4// SNORD64// PAR1// SNORD109A// SNORD109B// SNORD116-6// SNORD116-3// SNORD116-9// SNORD115-13// SNORD115-1// SNORD115-14// SNORD115-15// SNORD115-21// SNORD 115-10/SNORD 115-16// SNORD115-6// SNORD115-42// SNORD115-11// SNORD115-29// SNORD115-34// SNORD115-36// SNORD115-4// SNORD115-43// HBII-52-24// SNORD116-5// SNORD116-7// SNORD115-26// SNORD115-30// SNORD116-15// SNORD116-8// SNORD115-2// SNORD115-39// SNORD116-14// SNORD116-20// SNORD115-8// SNORD115-3// SNORD115-38// SNORD115-41// SNORD115-22// SNORD115-44// SNORD116-1// SNORD115-17// SNORD 115-115// SNORD115-18// SNORD115-2// SNORD115-44// SNORD115-7// SNORD -20// SNORD116@, SLC9A, RPA, ADARB, AFG3L, MCTP, DACH, SEH1, RRP1, ZNF335, WDR, TAF, MOSPD, WIPI// ARSG, ARRB, PLIN// LRG, SNRPD// C22orf, CTNNBL, ZNF175, NCF, DDX, FBXO, TDP, ATXN2, LOCF, VAPA, DDX 19// DDX19, NCOR, MTKL, LOCS, TOM 1L// LOC 2415, APOBEC3, EXD, CDR// RRN// LOC 100998// LOC653390, ADCY, DHX, CKLF, PRKCSH 3C, DHX, HSPH, CCDC, MCM OR, CCPG// GB// PIX 1311, SAF 3, SAFP 460, SAFR, SAPR// SAF, SAPR 4, HARP 294, HARPA// ROPI 3, HAIR, HALF, HARP 138, HALRP// HAP, HALRP, HARP, HALF, HALRP, HAPR// HALF, HAPR// HAP, HAPR, HAP 4, HAP, HAPR// HAP, HAS, SERPINA6, SMG6// C17orf6, INO80, C16orf62, RAB35, PEF1, C14orf101, TMEM185A, LIMK2, CTCF, DIABLO// B3GNT4, VPS33A, UNQ1887, TBCB// POLR2I, ABHD13, SLC24A6, EDNRB, CA12, ANAPC5, TMC3, TRIAP1, ABHD12 LOC 12B, TDRD9, CXEIF 2B1, CXorf59, LRRC37A 3/LRRC 37A 2/LRRC 37A/LRRC 37/ARL 17P1// LRRC37A 1// ZN 100294335// LOC644397, SDS, SYCP 1, PFD 1D 1, ZN 72, ZN 24/LOC 1, ZN 643672// LOC1, and ABCC 1// 1 genes (hereinafter referred to as "ABIRCC 1/1 gene marker sets" ".

In some embodiments, the indicator is for determining a likelihood of the subject having ipSIRS or a healthy condition, and wherein the biomarker value is determined from at least one IRS immune system biomarker in each of the first and second IRS immune system biomarker groups, wherein:

a) the first IRS immune system biomarker group consists of polynucleotide expression products and/or polypeptide expression products from the following IRS immune system biomarker genes: LTBP, LPHN, NR 1D// THRA, METTL, PLD, MAPK, FAM102, MYBBP 1// SPNS, FLJ10232, SEMA4, LMF, PLBD, MAN1C, B4GALT, ENGASE, NDRG, TLR, WDR, PATZ, CD177, LILRA, SIRPD, ADAMTS, TCF, GGT, GYG, CDAN, BRF, GPR, TMC// LOC100131096, PGAP, GRAP// SNORD 3-1// SNORD 3-2// LOC400581, SHPK, NOG, PVRIG// PILRB/STAG, TDRD, TMC, C14orf, NLRC, APAR, TBL, ATCC 1, C16 orf// SNR/KLHL, IRAK, JMJD-2G 4/THRA, JJJJJJV/THRA, SLC 2/SLC, SLC/SLC, SLC 2/SLC, SLC/SND 3-2// SND 3, SLC 3-2, SLC, SL, PRMT, AGFG, CMTM// CKLF, MCTP, ARL, FMNL// PRPF40, FAM151, BAHD, OSBPL, ZAP, TUBGCP, MAP2K, NPTN, C3AR, CD247, S100A, TBCD, LMNB// PCIF, PFKL, GRWD, PYGL, UPP, OMG, SAMSN// LOC388813, BLCAP, PTPRS, FAM20, OMD, SPPL2, IL2, SORT, BST, TAF 1// ADAD, SEMA4, NCH, APTP 544, ZAK, CCR, GALMAN 2C, NEURL// GPS/D4S 234, X/RPDL, TRAPPC6, LPCAT, SLC 19/orf, SLC4A, C14orf101, TP53I, IL1, PNM, UBE2R, KPPT KP, ZNF P, TMCP, SLCP, SLC 19, SLC, SAVER, SARG, SALT, SLC, SALT, SARC, SALT, SARC, SALT, SARC, PTPRJ// OR4B, NAT, RHOT// FBXL, PNPLA, DNAJC, GNG, FAM129, PXK, C10orf119, BATF, LMO, KLF, NRD, CLCN, GLA, CFLAR, SYCP, IMAGE5303689, LPGAT, PTGDR, LAMP, ZNF607, INSL// JAK, DUSP, PCNX, CD79, IRAK, ZNF550// ZNF549, LOC100130950, SPTLC, CTSA, RAP2, ADCY, MED12, MTHFD, CAP, TOR1 AIP// TOR 1P// RG, CHP, TSEN, LYRM, UBE2, NURRMD, YIPF, FRFIFOL, KAFLAFVCR// VCRS, HPWSB, KIAA0, JAG, GPR183, N6AMT, ZNF 3/ZNF 3, ZNF, NL, BHPT// FG, BHT, BHPT, BHT, SHC, SHT, SHB, SHT, SH, ARMCX5, ALAS1, NFKBIA, STXBP2// LOC554363// LOC100131801, C15orf24, SRI, ASGR2, NSF// LOC728806, TRIM69, SEC23A, PLAUR, RAB3GAP2// AURKAPS1// AURKA// SNORA36B, MAOB// NAT13, DEDD, SEC23B, COPA, EGF, STRADA, SIAE, C5, SLC30A 5, ANXA 5, NKG 5, ABHD12 5, TESK 5, LONRF 5, PIKFYVE, BGRL 5, ARMCX5, NEU 5, SPAST, STX 5// KIAA1614, TADA 35, LONRF 5, ENEN, ENSH 3BGRL 5, RTM 5, and PSUBC 5 genes are referred to herein interchangeably as the biomarker panel IRIRS 5or the following biological marker gene panel "; and the number of the first and second electrodes,

b) The second IRS immune system biomarker group consists of polynucleotide expression products and/or polypeptide expression products from the following IRS immune system biomarker genes: CNNM, SLC25A, UNC84, ARHGEF// LOC100128573, PIK3IP, EPHX, SEPT, ITPKB, TSPYL// GPR173, GALT, USP, CBX// LOC100128400, MAP 3K// LOC100133991, CDK5RAP, KLHL, SPG, ZNF574, RASA, KLHL// KRT, PYGO, USP, LCK, SKI, C5 orf// SQSTM, PIK3C 2// LOC100130573, ANGEL, ZCCHHC, BP, ZMIZ, TMEM 120// RHOOF, CIR 2/NOCD 401010, PPP1R13, ZNF416, PBXIP, SMYD// NOTE, ZNF529, LENG, CX 1D 22/LOC 289878, SAM/NOF, SAM/130010, SLC/1307, SLC 1007, SLC// LOC 1007, SLC// OCR, SLC// NOTE, SLC 1007, SLC7, SLC, NLRP// LOC728392, TMEM63, TMEM208, ZNF362, GNG, CASS, ZNF287, DGKE, CEP, ASXL, CLUAP, WDR, CD40, MGC57346// C17orf, PPIE// CCDC, FCHO, TNPO// SNORD, CSNK 1// LOC400927, CLYBL, XAB, METT 10// LOC284009, PRPF, AKAP, SH3 BP// PDXP, TOM 1L// LOC246315, ZNF329, ZNF274, FAM119, SMYD, UNC 84// C7orf, ZNF256, PSD, CCDC130, LIMD// MAP3K, MSELP, ZNF 3L, TXK, DDX, FBXL, TNRC6, TADA1, KIAA/KIJ 69, TMAF 0355/ZNF 11, ZNF 11/ZC, TMAF 611/ZC, TMCP// TMCP, TMAS 11, TMCP// CLC, TMCP, TMAS 11/TMCP, TMCP// CLC 19, TMCP, TMAK, TMCP, TMAK// PDXP, TMCP 2, TMCP, TMAK// CLC 11, TMCP// TMCP, TMAK, TMCP, TMAS 11/TMCP, TMNACK, TRIM62, TDRKH, COG1, POLR1B, AFG3L1, TYK2, RBM3, UBTF, RP11-94I2.2// NBPF16// NBPF16// NBPF16// NBPF16// NBPF16// LOC100288142// NBPF16// KIAA1245// LOC100290137, ZNF 16, ZNF461, PI4 16// PI4KAP 16// PI4KAP 16// LOC100293141, THEM 16, BCL11 16, CC2D1 SNSN3672, WDR 16, BBS 16// FOD 16, RRN 336590/LOC/KO 16/SU 16/OCR 16, SAORN 72/OCR 16, SAORN 72/OCR 16, SAR 16/OCR 16, SAR 72/OCR 16/OCR 72, SAR 72/OCR 16, SAR 16/OCR 16, SAR 72/OCR 2/OCR 16, SAR 2/OCR 16, SAR 2/OCR 72/OCR 16/OCR 72, SAR 72/OCR 72, SAR 72/OCR 2R 72/OCR 16/OCR 2/OCR 72, SA PAR-SN// SNORD116-2// SNORD116-25// SNORD116-26// SNORD107// SNORD115-12// SNORD115-5// SNORD115-6// SNORD115-9// SNORD116-11// SNORD116-12// SNORD116-13// SNORD116-28// SNORD116-4// SNORD64// PAR1// SNORD109A// SNORD109B// SNORD116-6// SNORD116-3// SNORD 116-9/SNORD 115-13/SNORD 115-1// SNORD115-14// SNORD115-15// SNORD115-21// SNORD 115-10/SNORD 115-7// SNORD115-16// SNORD115-6// SNORD115 SNORD115-11// SNORD115-29// SNORD115-34// SNORD115-36// SNORD115-4// SNORD115-43// HBII-52-24// SNORD116-5// SNORD116-7// SNORD115-26// SNORD115-30// SNORD116-15// SNORD116-8// SNORD115-2// SNORD115-39// SNORD116-14// SNORD116-20// SNORD115-8// SNORD115-3// SNORD115-38// SNORD 115-41/SNORD 115-22// SNORD115-44// SNORD116-1// SNORD115-17// SNORD115-18// SNORD 115-115// SNORD115-19// SNORD 116// 20// SNORD116, C17 orf// ASB, ZNF317, SNRNP200, CXorf, MTBP, NOL// SNORA38, CCNL, ALDOC, PIPTNC, FASTKD, ZZZ, PIK3R, WDR, GLDN, CHML, C15orf, DIDO, CLCC// GPSM// C1orf, SLC35D, SCRN, C15 orf// SERF, ZNF460, SAFB// SAFB, C16orf, DDX, CTPS, ZNF382, ZNF101, LIPT// MRPL, ITGA, KIF21, PP5, 3A// CCDC157, ODF2, NUAK, CHCHCHCHCHCHHD, AHSA// USP, YLPM, TERF, ZNF830, MAN 2B// MORG, GPH, SHC, SERT, SFRS, TMC, OTTRC, OTTRG, NASARG// USP, YLPA 1659, MULA, MURA, MURD gene set of genes (hereinafter referred to as "biomarker".

In some embodiments, the indicator is for determining a likelihood of the subject having inSIRS or ipSIRS, and wherein the biomarker values are determined from at least one IRS immune system biomarker in each of the first and second IRS immune system biomarker groups, wherein:

a) the first IRS immune system biomarker group consists of polynucleotide expression products and/or polypeptide expression products from the following IRS immune system biomarker genes: ALKBH5// FLJ13773, RPS19BP1, RFXANK// MEF2B// LOC729991, NA, CDC6, C19orf56, NA, ABCC2, THAP11, RTN2, MAZ, TAX1BP3, NUTF2, MPZL3, FBXW5, HIST1H2BM, CETP, PQLC1, H2AFX, KIAA0101// CSNK1G1, STK17B, SMARCD B, LOC 100934// CDK B, LPCAT B, LPP, MPP B, ANKRD B, PRR B// PCBP B, MDS B, RBM B, GATAD2B// PLMCM B, PPTC B, BL B, MYPT B, CRR B, PCBTORP B/PCBTORP B, CRI 72, CRI B, CRI 72, CRI B, CRI 72, CRI B, and CR; and the number of the first and second electrodes,

b) The second IRS immune system biomarker group consists of polynucleotide expression products and/or polypeptide expression products from the following IRS immune system biomarker genes: PIWIL4, C11orf82, ACRC, CLPTM1L, MAGED2, PLAC8, ZDHHC4, OTX1, INSIG1, BATF, MFSD11, DNASE1L1// RPL10, C15orf 10, CDS 10, KEAP 10, ARD1 10, POLR2 10, AHCY, SLC39A 10, CUGBP 10, FAM96 10, TM7SF 10, CTSZ, CD 10, SPPL2 10, GAST 3 KR 10, SEC 10, TM9SF 10, IRAK 10, GOSR 10, ADIPOR 10, TG, GABRR 10, TPST 10, DERD 10, CCDC101// LOC388242, CALA 5/10, CATCAT 10// 10, CATCAT 10, CANCTAN 10, CANTC 10// 10, CANTC 363672, CANTC 10, CANTC 3636363636363636363672, CANTC 10, CANTC 363672, CANTC 10, CANTC 36363672, CANTC 10, CANTC 3636363672, CANTC 10, CANTC 3636363672, CANTC 10, CANTC 363636363636363672, CANTC 3636363672, CANTC 3636363636363636363636363636363636363636363636363636363636363636363672, CANTC 10, CANTC 36363672, CANTC 10, CANTC 36363636363636363672, CANTC 10, CANT, KLHL6, PTPN6, GALK2, DAD1// OR6J1, PDLIM5, TMEM147, TRAM1// LOC286190, LZTR1, TNPO1, ACSL 1, C22orf 1, PLK1, SYNE 1, PSMD 1, FLJ27255, PRKCD, RAB1, RPN 1// EEF1A1, SLC35B1, KCNIP 1, PDE3 PDE 1, PXMP 1// PGAM 1, SDF 1, HIST1H 31, LOC284757, TMEM 1// DCAF4L1, CSNK2A 1, LSM1, PT1 1, ADCARB 1// GCSHCP 1, SALG 1, SALG 1, SALG 1, SALG 1, SALG 1, DENND3// C8orf60, CLDN3, F11, CCDC93, FLJ46365, CYP21A2, ETV5, TRPM2, IL20, NBL1// C1orf151, NGEF, POU6F2, PTEN// PTENP1, NPC1L1, CYP4B1, NFIC, PPARGC1A, PLIN3, THPO, TIMP4, CELSR2, DMBX1, CAMK2B, PPFIA1, HCLS1, SLC6A20, C17orf 66/RSL 24D1, PIWIL 1, DADADROCRJDAC 1// DAZ 1// DAX 1, 363672, PGSAGL 1, PSN 1// 1, PSN 1// SSC 1, PSN 1, KDR, ENAM, UNKL, SPRR2C, GPR17// LOC100291428// LIMS2, C1orf175// TTC4, CACNA1D, C2orf62, LOC100132686, UNQ6126, TRIM15, GPR113// SELI, IL22, SCN10A, FAAH, MBOAT7, C7orf51, KIAA1530, TRPM8, C1orf95, DDC// LOC 129100427, GABRA1, HCRTR1, DCST2, CHODL, PAN3// EEF1A1// CHCHCHCHCHCHHD 2, LYPD6B, UGT3A1 and SERPINA6 (hereinafter interchangeably referred to as "group IRS immune system biomarker genes" or "group IRS biomarker genes").

In some embodiments, the indicator is for determining a likelihood of the subject having inSIRS or ipSIRS, and wherein the biomarker values are determined from at least one IRS immune system biomarker in each of a first IRS immune system biomarker group, a second IRS immune system biomarker group, a third IRS immune system biomarker group, and a fourth IRS immune system biomarker group, wherein:

a) the first IRS immune system biomarker group consists of polynucleotide expression products and/or polypeptide expression products from the following IRS immune system biomarker genes: RPS19BP1, RFXANK// MEF2B// LOC729991, C19orf56, RTN2, HIST1H2BM, CETP, PQLC1, H2AFX, KIAA0101// CSNK1G1, LOC100134934// CDK3, LPCAT4, LPP, MPZL2, ANKRD9, RBM33, MYBL2, PLA2G7, OIP5, CRIPT, RNF186, C3orf35, HXZ 2, NDUFB7 and DUSP11 (hereinafter interchangeably referred to as "group IRS immune system biomarker gene" or "group IRS biomarker gene";

b) the second IRS immune system biomarker group consists of polynucleotide expression products and/or polypeptide expression products from the following IRS immune system biomarker genes: PIWIL4, C11orf82, ACRC, PLAC8, ZDHHC4, OTX1, INSIG1, BATF, MFSD11, C15orf24, CDS2, POLR2G, SLC39A9, FAM96B, TM7SF3, SPPL2A, ADIPOR2, GOSR2, DERL2, TPST2, VWA5A// OR10D 1A, CCDC101// LOC388242, MAPK A, PSMA A, JKAMP, HALD A, DIA 1AP A, SLC30A A, PDGFC, ATOX A, TMEM205// hCG A, FAM108B A, UBQLN A, RAPH A, CANDP 3670/A, GALVD A, SALT A, SLC A/A, SLC A, SLC A, 36, MYBPH, MFAP, RCHY, CDH, TMEM184, CTRB// CTRB, SLBP, CRNN, YIPF, CHRNG, SLC35F, METTL, CLDN, CCDC, CYP21A, NBL// C1orf151, NGEF, POU6F, NPC1L, PPARGC1, THPO, CELSR, DMBX, SLC6A, C17 orf// RSL24D, GAL3ST, C19orf, BAALC// FLJ10489, CLDN, TAS1R, CCDC, OSBPL, SDK, TMEM// CLTC// MIR, TMEM, NDB// DFFB, NCRNA00085, NTRK 4A, SLC5A, RTCD, FLJ14100// C1orf, PROC, SERC 17orf, NM, UNC 9, 6161N, LY 6128, RHH// C26, SUK, SUN, SUCH, SUN, SUCH, SUN;

c) The third IRS immune system biomarker group consists of polynucleotide expression products and/or polypeptide expression products from the following IRS immune system biomarker genes: ALKBH5// FLJ13773, RNA243, ARD1A, CDC6, AHCY, MRPS34, CYBASC3, HIST1H4L, RNF114, TRIM28, CSTF1, CEACAM4, GAB2, GNB5// LOC100129973, THAP11, SSR4// IDH3G, STT3A, NUTF2, MPTF 3, TMEM147, RAB34, PDE3B, PXMP2// PGAM5, HIST1H3I, LOC284757, TMEM106C, STK17B, IMP B, HCG B, CUBN, GIERC B, ELMO B, GBCUP B, COL9A B, MEGF B, ME36Z B, FAM 123/PRPHS B, PRPHR B, ADAMP B// B, ADAMP 36363672, ADAMTP B, GATAD 2// PLIN, SLC11A, PPTC, PHC, BAT2, DENND// C8orf, FLJ46365, ETV, CCDC125, PTEN// PTENP, TLE, NFIC, TIMP, PPFIA, HCLS, SAP130, MXD, NR 4A// FLJ46875, SKI, ARID1, ENTPD// C10orf131, POGZ, DOK, REG// NBPF, MCM, SLC7A, NUP, C10orf, TMEM, SH2D, SNRK, SPRR2, CACNA1, TRIM, CLPS, MBOAT, KIAA1530, C1orf, GABRA, HCRTR, CHODL, and PAN// EEF 1A// CHCHCHCHCHHD (hereinafter interchangeably referred to as "group IRS immune system biomarker genes" or "group IRS biomarker genes"); and the number of the first and second electrodes,

d) The fourth IRS immune system biomarker group consists of polynucleotide expression products and/or polypeptide expression products from the following IRS immune system biomarker genes: CLPTM1L, MAGED2, DNASE1L1// RPL10, KEAP1, CUGBP1, CTSZ, CD63, ST3GAL2, SEC13, TM9SF1, IRAK2, GABRR2, TG, CD300 2, NEU 2, ACSS2, AGTRAP, UPF0627, COX 2, ABCC2, ACTR 12, AIP, YIF 12, VPS26 2, JAG 2, VAT 2, RAP 72, DNASE 2, MAZ, TAX1BP 2, MGAT4 2// SQ3672, FBXW 2, PTPN 2, LZTR 2, PLK 2, CAORF 2, PSMD 2, FLJ 3655, PRD 2, SDF2, SADDP 2, CALD 2, c1orf175// TTC4, IL17F, C2orf62, GPR113// SELI, SCN10A, TRPM8, DDC// LOC100129427, and UGT3A1 (hereinafter interchangeably referred to herein as "group J IRS immune system biomarker genes" or "group J IRS biomarker genes").

In particular embodiments, the first IRS immune system biomarker is a PLA2G7 expression product, the second IRS immune system biomarker is a PLAC8 expression product, the third IRS immune system biomarker is a CEACAM4 expression product, and the fourth IRS immune system biomarker is a LAMP1 expression product.

In some embodiments, the indicator is for determining the likelihood of the subject having mild sepsis or severe sepsis, and wherein the biomarker values are determined from at least one IRS immune system biomarker in each of the first and second IRS immune system biomarker groups, wherein:

a) the first IRS immune system biomarker group consists of polynucleotide expression products and/or polypeptide expression products from the following IRS immune system biomarker genes: n4BP2L2// CG030, FAM96A, MINPP1, MORC3// DOPEY2, LSM8, PLEKHA3, MITD1, ATF4, B2M, TMX3, ZNF273, PLEKHF2, UNQ2999, DPM1, OCLM, NADK, GPR65, SFRS3, ZNF28, PFDN 28, COQ10 28, SLC30A 28// DDX 28, KPNA 28, ATP6V1G 28, HAUS 28, hCG _ 28, RNF 33 28, BET 28, UBE2V 28, ATP6AP 28, SUB 28// EM 36188, ABSK 28, LAPTMEN 4, RNF 36138, RNF 28, XPDC 28, XP 28, ACT PAD 28, CTC 28// CTC 28, CALCF 28, CALNT 28, CALN 28// 28, CALCF 28, CALN 28, CDC37L, FNTA, SHFM, JKAMP, TMEM126, MRPL, DENND1, ATP6V 0E// SNORA74, LIN7, HAUS// POLN, SLC30A, VAMP, OBFC2, MAGT, STARD3, C5orf, PSMD, RERE, RNF139, SFT2D, SKP, RNPC// AMY2, MYOM, TIPRL, HPRT, TRIM, VRK, CDKN1, FAANKRA, RAP2, FAM 127// FAM 127// FAM127, FAM126, TMEM161, UNQ1887, FANCF, SELT, CYP20A, RWDD, ARPP19, SC5DL, TiCAM2// TMED7// TMED7-TiCAM2, STAM, LEPROTL1, RNF44, DCP1B, TNNT3, UCHL5, UPRT, SON, PIK3C3, SFRS1// FLJ44342, FBXO22// FBXO22OS, SCFD1, C11orf31, TMTC3, CCDC132, TMBIM4// LOC100133322, ATAD1, APH1A "), MYNN, HADHB, PIGN, RNA243, SLC38A9, C10orf84, CDKN1A, ATP7A, RPAP2, ZNF451, GSK3B// LOC100129275, TMSB 36 10, TMTD 5/PRO 4472, VPK 1// VPS 1, and the following biological marker gene set" IRRS 1 "; and the number of the first and second electrodes,

b) The second IRS immune system biomarker group consists of polynucleotide expression products and/or polypeptide expression products from the following IRS immune system biomarker genes: c13orf, PRKCB, APOBEC 3// APOBEC3, SFRS, NCAPD// SCARNA// FADS, GATS, LOC284757, TSHZ, JAK, MAPK, RPN, GNAS, CYTSA, TRPM, C22orf, PCMTD// PXDNL, CCDC, ARSD, MLL// BAGE, NCOR// C20orf191// LOC 100704, MRPS, VEZF, GSR, POU2F, VPS4, SMG, PTP4A, OSBP, GLCCI// tcag, DOCK, PCNX, GLTP, XO, YY, TCF, NR 2C/MRPS, TEX, BAI, WHSC1L, UBTF, GEH, GEMIN, DDEF1IT, FAM50, VPS13, FBB/SAT 1D, 149, PHAHP/MRPS, PHASK 2817// VCR, ZC, SACK 13, VPS13, SHCK 13, SACK 13, SHCK 2O, SHCK 2, SHCK 2, SHCK, ZNF346, AP3M2, CD14, clamp 1, ABCC2, ATXN7L 2// RINT 2// EFCAB 2, INO80 2, CTPS, LRRC37a2// LRRC37 2// ARL17P 2// LRRC37a2// LOC100294335// LOC644397, DNASE 2, LRRN 2, ZNF318, PRKAR 22, MRPS2, ANKHD 2-EIF 4EBP 2// andhd 2// EIF4EBP 2, BTF3L 2, DGKA, C10orf119, C11 mborf 2, CDC2L 2, DPP 2, dctp 2, imptp 2, GOT 72, gldh 2, and tn 2 are interchangeably referred to herein as the biomarker panel "irl 2" or "irl 2 gene panel".

In some embodiments, the indicator is for determining a likelihood that the subject has mild sepsis or septic shock, and wherein the biomarker values are determined from at least one IRS immune system biomarker in each of the first and second IRS immune system biomarker groups, wherein:

a) the first IRS immune system biomarker group consists of polynucleotide expression products and/or polypeptide expression products from the following IRS immune system biomarker genes: EEF1DP, GIMAP, ZNF839, PYGL, TNFAIP8L, SFRS, VIM, GLTP, WDFY, APPL, C4orf, PLD, LIN7, ELP, ZDHHC, UBAP// KIF, C20orf177, FAM 149B// FAM149B, E2F, SPATA, DACH, FAM 47// STBD, SVIL// hCG _, METTL, LRRC, NUPL, UPP, AFF, SLC16A, SET, CA, HCK, C16orf, EXT, NOP, FRZB, C9 orf// BKAP, VASP, ASB// PHB, GTDC, SLC39A, FBX// KIAA0831, RABGNNAGc// tcag// tcag// KC/33, FORG 28A, WITSAS// SSG, TSC 2A, PSXO// AGR 1, SHC, SARG// AGR 2, SARG// SHC, SACK// TAB, SACK// TAB, SACK// TAG// TAB, SACK// TAG, SACK// TAG// TAB, SACK// TAG, SACK// TAB//, RAB11FIP2, LARS, PLP2, EIF4E2, DNASE1L1// RPL10, AFTPH, TMCO3, RPA2, UQCRC1, ZDHHC3, and ACTR1A (hereinafter interchangeably referred to as "group M IRS immune system biomarker gene" or "group M IRS biomarker gene"); and the number of the first and second electrodes,

b) The second IRS immune system biomarker group consists of polynucleotide expression products and/or polypeptide expression products from the following IRS immune system biomarker genes: CD, ITPA, PVRIG// PILRB// STAG, FLT3, IL12RB, MAP 3K// LOC100133991, FAM102, TMC, TMEM208, TMEM109, C1orf, NADK, SEPT, UBA, CD, C12orf, C20orf112, FOXP, EIF 4A// SNORA, ZNF487// LOC439911, KCTD, IL 18// NUMA, KPNA, EDC, ZNF587// ZNF417, NBR, RPL, 738F, SHFM, CNO, C9orf, RPL// SNORA/SNORD// FAM69, VAMP, SIT, SFRS, OPK, IRF, MRPS, NEFM// ZN 100129N, BET, C19orf, SH2D1, ACTS/C1, GLLRB// ZNF 11, TMCP 10, TMADM// CARD 3, TMCP 6, TMRD// CARD 3, TMCP 3K, TMRD, TMCP 3, TMRD, TMCP, TMRD, TMCP 3, TMCP 7// NARD, TMCP, FNTA, GRIA, N4BP 2L// CG030, ENOSF// TYMS, THBS, LUC7L, MOCS, ZNF383, AKNA, UBE2, FLJ34077, SH3KBP, POLR 2// LOC100131530, IL1, UBE2V, KIAA1919, PRKCB, SHOC, RBM, GRPEL, KCNG, PCDH, XAB, VPS, MCCC, NSMCE4, PTP4A, SNX, COQ10, C6orf182, RNF, MOGS, DIRAS, Mitochondrial, KIAA1826, SGK196, NSUN// NSUN5, Mitochondrial, MORF4L, NNMAK// C8orf, PIB// PVRIG, SAAL, TMBATX, MAPK, TMADA// BTAC 5, MTN 5, Mitochondri, MTPR// FO, TFC, TFR 3, TFC// FORD, TFC, BTAC, TFD, TFC, TFS 4, BTAC, BTD, BTAC, BTD 6, BTAC// BTAC, BTD, BTAC, BTD, BTAC, BTD 6, BTAC, BTD 6, BTD, BTAC, BTD, BTAC, BTD, BTAC, BTD, ZNHIT6, RGP1// GBA2, SMCP, ZC3H15, TRAPPC4, SARNP// DNAJC14, GNAS, C14orf104, IL20RA, WAC, SLIT RA, C4orf RA, TSEN RA, PPP1R13 RA, TRIM RA, HGC6.3, YIPF RA, HQ0644/PRO0644, FAM13 RA// PHYHIPL, BCCIP// DHX RA, ACADM, SUB RA// TMEM183 RA, KPNA RA, SPAG RA// WDR RA, KIAA 9, DIABLO// B3GNT RA, ZBTB RA, EIF1 RA, RUNX1T RA, TRIL, PTPLB, UPK1 RA, UPK 36182, DIAOTB// B3GNT RA, DHTB RA, BIRUB RA, BIRUBM RA, BIRUB RA, BIRJRB RA, and the following biological marker gene set (referred to as "IRIRIRIRIRS RA" gene RA "or" gene set RA "heninas RA" herein).

In some embodiments, the indicator is for determining a likelihood that the subject has severe sepsis or septic shock, and wherein the biomarker values are determined from at least one IRS immune system biomarker in each of the first and second IRS immune system biomarker groups, wherein:

a) the first IRS immune system biomarker group consists of polynucleotide expression products and/or polypeptide expression products from the following IRS immune system biomarker genes: SIRPG// SIRPA, GATA3, FAM102A, UPF3A, ATP13A5, CACNA1I and RANBP17// USP12 (hereinafter interchangeably referred to as "group O IRS immune system biomarker genes" or "group O IRS biomarker genes") in a mammal; and the number of the first and second electrodes,

b) the second IRS immune system biomarker group consists of polynucleotide expression products and/or polypeptide expression products from the following IRS immune system biomarker genes: GABRA6, HAPLN1, YSK4, FOXL2, TLL1, MECOM, COL3a1, HRG, SLC22A3, C8orf45, SCN7A, and SNTG1 (hereinafter interchangeably referred to as "group P IRS immune system biomarker genes" or "group P IRS biomarker genes").

In another broad form, it is an object of the invention to provide an apparatus for determining an indicator for use in assessing the likelihood of a biological subject having the presence, absence, extent or prognosis of at least one medical condition, the apparatus comprising at least one electronic processing device that:

c) determining the indicator using the derived biomarker value.

In another broad form, it is an object of the invention to provide a composition comprising at least one pair of reverse transcribed mrnas comprising a first pair of reverse transcribed mrnas and a second pair of reverse transcribed mrnas, wherein the first pair comprises PLAC8 reverse transcribed mRNA and PLA2G7 reverse transcribed mRNA, and wherein the second pair comprises CEACAM4 reverse transcribed mRNA and LAMP1 reverse transcribed mRNA, and at least one oligonucleotide primer or probe that hybridizes to a separate one of the reverse transcribed mrnas.

In another broad form, it is an object of the invention to provide a composition comprising at least one pair of reverse transcribed mrnas comprising reverse transcribed mRNA from a first IRS immune system biomarker gene selected from group a IRS immune system biomarker genes and reverse transcribed mRNA from a second IRS immune system biomarker gene selected from group B IRS immune system biomarker genes and at least one oligonucleotide primer or probe that hybridizes to a single one of said reverse transcribed mrnas.

In another broad form, it is an object of the invention to provide a composition comprising at least one pair of reverse transcribed mrnas comprising reverse transcribed mRNA from a first IRS immune system biomarker gene selected from group C IRS immune system biomarker genes and reverse transcribed mRNA from a second IRS immune system biomarker gene selected from group D IRS immune system biomarker genes and at least one oligonucleotide primer or probe that hybridizes to a single one of said reverse transcribed mrnas.

In another broad form, it is an object of the invention to provide a composition comprising at least one pair of reverse transcribed mrnas comprising reverse transcribed mRNA from a first IRS immune system biomarker gene selected from group E IRS immune system biomarker genes and reverse transcribed mRNA from a second IRS immune system biomarker gene selected from group F IRS immune system biomarker genes and at least one oligonucleotide primer or probe that hybridizes to a single one of said reverse transcribed mrnas.

In another broad form, it is an object of the invention to provide a composition comprising at least two pairs of reverse transcribed mRNA comprising a first pair of reverse transcribed mRNA and a second pair of reverse transcribed mRNA, wherein the first pair comprises reverse transcribed mRNA from a first IRS immune system biomarker gene and reverse transcribed mRNA from a second IRS immune system biomarker gene, and wherein the second pair comprises reverse transcribed mRNA from a third IRS immune system biomarker gene and reverse transcribed mRNA from a fourth IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group G IRS immune system biomarker genes, wherein the second IRS immune system biomarker gene is selected from group H IRS immune system biomarker genes, wherein the third IRS immune system biomarker gene is selected from group I IRS immune system biomarker genes, and wherein the fourth IRS immune system biomarker gene is selected from group J IRS immune system biomarker genes.

In another broad form, it is an object of the invention to provide a composition comprising at least one pair of reverse transcribed mrnas comprising reverse transcribed mRNA from a first IRS immune system biomarker gene selected from the group K IRS immune system biomarker genes and reverse transcribed mRNA from a second IRS immune system biomarker gene selected from the group L IRS immune system biomarker genes and at least one oligonucleotide primer or probe that hybridizes to a single one of said reverse transcribed mrnas.

In another broad form, it is an object of the invention to provide a composition comprising at least one pair of reverse transcribed mrnas comprising reverse transcribed mRNA from a first IRS immune system biomarker gene selected from the group M of IRS immune system biomarker genes and reverse transcribed mRNA from a second IRS immune system biomarker gene selected from the group N of IRS immune system biomarker genes and at least one oligonucleotide primer or probe that hybridizes to a single one of said reverse transcribed mrnas.

In another broad form, it is an object of the invention to provide a composition comprising at least one pair of reverse transcribed mrnas comprising reverse transcribed mRNA from a first IRS immune system biomarker gene selected from the group O IRS immune system biomarker genes and reverse transcribed mRNA from a second IRS immune system biomarker gene selected from the group P IRS immune system biomarker genes and at least one oligonucleotide primer or probe that hybridizes to a single one of said reverse transcribed mrnas.

At least one oligonucleotide primer or probe may hybridize to a separate one of the reverse transcribed mrnas.

The reverse transcribed mRNA may be derived from a component of the immune system.

The reverse transcribed mRNA may be derived from leukocytes.

The reverse transcribed mRNA may be derived from blood cells.

The reverse transcribed mRNA may be derived from peripheral blood cells.

The composition may further comprise a labeled reagent for detecting reverse transcribed mRNA.

The labeled reagent may be the at least one oligonucleotide or probe that is labeled.

The labeled reagent may be the reverse transcribed mRNA that is labeled.

The labeled reagent may be a labeled oligonucleotide linker or tag for labeling the reverse transcribed mRNA.

In another broad form, it is an object of the invention to provide a kit for determining an indicator indicative of the likelihood of the presence or absence of at least one condition selected from the group consisting of inSIRS and ipSIRS, the kit comprising at least one pair of reagents comprising a first pair of reagents and a second pair of reagents, wherein the first pair of reagents comprises (i) reagents allowing quantification of a polynucleotide expression product of a PLA2G7 gene; and (ii) reagents that allow quantification of the polynucleotide expression product of the PLAC8 gene, wherein the second pair of reagents comprises: (iii) reagents allowing the quantification of the polynucleotide expression product of the CEACAM4 gene; and (iv) reagents that allow quantification of the polynucleotide expression product of the LAMP1 gene.

In another broad form, it is an object of the invention to provide a kit for determining an indicator indicative of the likelihood of the presence or absence of at least one condition selected from the group consisting of inSIRS and a health condition, the kit comprising at least one pair of reagents comprising (i) reagents allowing quantification of a polynucleotide expression product of a first IRS immune system biomarker gene; and (ii) an agent that allows quantification of a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group a IRS immune system biomarker genes, and wherein the second IRS immune system biomarker gene is selected from group B IRS immune system biomarker genes.

In another broad form, it is an object of the invention to provide a kit for determining an indicator indicative of the likelihood of the presence or absence of at least one condition selected from the group consisting of ipSIRS and a health condition, the kit comprising at least one pair of reagents comprising (i) reagents allowing quantification of a polynucleotide expression product of a first IRS immune system biomarker gene; and (ii) an agent that allows quantification of a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group C IRS immune system biomarker genes, and wherein the second IRS immune system biomarker gene is selected from group D IRS immune system biomarker genes.

In another broad form, it is an object of the invention to provide a kit for determining an indicator indicative of the likelihood of the presence or absence of at least one condition selected from the group consisting of inSIRS and ipSIRS, the kit comprising at least one pair of reagents comprising (i) reagents allowing quantification of a polynucleotide expression product of a first IRS immune system biomarker gene; and (ii) an agent that allows quantification of a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group E IRS immune system biomarker genes, and wherein the second IRS immune system biomarker gene is selected from group F IRS immune system biomarker genes.

In another broad form, it is an object of the invention to provide a kit for determining an indicator indicative of the likelihood of the presence or absence of at least one condition selected from the group consisting of inSIRS and ipSIRS, the kit comprising at least two pairs of reagents comprising a first pair of reagents and a second pair of reagents, wherein the first pair of reagents comprises (i) reagents allowing quantification of a polynucleotide expression product of a first IRS immune system biomarker gene; and (ii) an agent that permits quantitation of a polynucleotide expression product of a second IRS immune system biomarker gene, and wherein the second pair of agents comprises (i) an agent that permits quantitation of a polynucleotide expression product of a third IRS immune system biomarker gene; and (ii) an agent that allows quantification of a polynucleotide expression product of a fourth IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from a group G IRS immune system biomarker gene, wherein the second IRS immune system biomarker gene is selected from a group H IRS immune system biomarker gene, wherein the third IRS immune system biomarker gene is selected from a group I IRS immune system biomarker gene, and wherein the fourth IRS immune system biomarker gene is selected from a group J IRS immune system biomarker gene.

In another broad form, it is an object of the invention to provide a kit for determining an indicator indicative of the likelihood of the presence or absence of at least one condition selected from the group consisting of mild sepsis and severe sepsis, the kit comprising at least one pair of reagents comprising (i) reagents allowing quantification of a polynucleotide expression product of a first IRS immune system biomarker gene; and (ii) an agent that allows quantification of a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from the group K IRS immune system biomarker genes, and wherein the second IRS immune system biomarker gene is selected from the group L IRS immune system biomarker genes.

In another broad form, it is an object of the invention to provide a kit for determining an indicator indicative of the likelihood of the presence or absence of at least one condition selected from the group consisting of mild sepsis and septic shock, the kit comprising at least one pair of reagents comprising (i) reagents allowing quantification of a polynucleotide expression product of a first IRS immune system biomarker gene; and (ii) an agent that allows quantification of a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from the group M IRS immune system biomarker genes, and wherein the second IRS immune system biomarker gene is selected from the group N IRS immune system biomarker genes.

In another broad form, it is an object of the invention to provide a kit for determining an indicator indicative of the likelihood of the presence or absence of at least one condition selected from the group consisting of severe sepsis and septic shock, the kit comprising at least one pair of reagents comprising (i) reagents allowing quantification of a polynucleotide expression product of a first IRS immune system biomarker gene; and (ii) an agent that allows quantification of a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from a group O IRS immune system biomarker gene, and wherein the second IRS immune system biomarker gene is selected from a group P IRS immune system biomarker gene.

In another broad form, it is an object of the invention to provide a method for inhibiting the development or progression of at least one condition selected from the group consisting of inSIRS and ipSIRS in a subject, the method comprising: exposing the subject to a treatment regimen for treating at least one condition based on an indicator obtained from an indicator determination method, wherein the indicator indicates the presence of the at least one condition in the subject, the indicator determination method comprising: (a) determining at least one pair of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject, (b) determining at least one derived biomarker value using the at least one pair of biomarker values, the derived biomarker value being indicative of a ratio of the concentrations of the at least one pair of immune system biomarkers; and (c) determining the indicator based on the at least one derived biomarker value, wherein the pair of biomarker values includes at least one of:

a) A first pair of biomarker values comprising a first biomarker value and a second biomarker value corresponding to a first biomarker and a second biomarker, wherein the first immune system biomarker represents a polynucleotide expression product of the PLA2G7 gene and wherein the second immune system biomarker represents a polynucleotide expression product of the PLAC8 gene, and,

b) a second pair of biomarker values comprising a third biomarker value and a fourth biomarker value corresponding to a third immune system biomarker and a fourth immune system biomarker, respectively, wherein the third immune system biomarker represents a polynucleotide expression product of the CEACAM4 gene and wherein the fourth immune system biomarker represents a polynucleotide expression product of the LAMP1 gene.

Generally, the indicator determination method comprises: determining the first pair of biomarker values and the second pair of biomarker values, and determining a first derived biomarker value calculated using the first pair of biomarker values and a second derived biomarker value calculated using the second pair of biomarker values; and determining the indicator based on a combination of the first derived biomarker value and the second derived biomarker value.

In another broad form, it is an object of the invention to provide a method for inhibiting the development or progression of inSIRS in a subject, the method comprising: exposing the subject to a treatment regimen for treating inSIRS based on an indicator obtained from an indicator determination method, wherein the indicator indicates the presence of inSIRS in the subject, the indicator determination method comprising: (a) determining at least one pair of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject, (b) determining at least one derived biomarker value using the at least one pair of biomarker values, the derived biomarker value being indicative of a ratio of the concentrations of the pair of immune system biomarkers; and (c) determining the indicator based on the at least one derived biomarker value, wherein the at least one pair of biomarker values comprises a first biomarker value and a second biomarker value corresponding to a first immune system biomarker and a second immune system biomarker, respectively, wherein the first immune system biomarker represents a polynucleotide expression product of a first IRS immune system biomarker gene, and wherein the second immune system biomarker represents a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from a group a IRS immune system biomarker genes, and wherein the second IRS immune system biomarker gene is selected from a group B IRS immune system biomarker genes.

In another broad form, it is an object of the invention to provide a method for inhibiting the development or progression of ipSIRS in a subject, the method comprising: exposing the subject to a treatment regimen for treating ipSIRS based on an indicator obtained from an indicator determination method, wherein the indicator indicates the presence of ipSIRS in the subject, the indicator determination method comprising: (a) determining at least one pair of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject, (b) determining at least one derived biomarker value using the at least one pair of biomarker values, the derived biomarker value being indicative of a ratio of the concentrations of the at least one pair of immune system biomarkers; and (C) determining the indicator based on the at least one derived biomarker value, wherein the at least one pair of biomarker values comprises a first biomarker value and a second biomarker value corresponding to a first immune system biomarker and a second immune system biomarker, respectively, wherein the first immune system biomarker represents a polynucleotide expression product of a first IRS immune system biomarker gene, and wherein the second immune system biomarker represents a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from a group C IRS immune system biomarker gene, and wherein the second IRS immune system biomarker gene is selected from a group D IRS immune system biomarker gene.

In another broad form, it is an object of the invention to provide a method for inhibiting the development or progression of at least one condition selected from the group consisting of inSIRS and ipSIRS in a subject, the method comprising: exposing the subject to a treatment regimen for treating the at least one condition based on an indicator obtained from an indicator determination method, wherein the indicator indicates the presence of the at least one condition in the subject, the indicator determination method comprising: (a) determining at least one pair of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject, (b) determining at least one derived biomarker value using the at least one pair of biomarker values, the derived biomarker value being indicative of a ratio of the concentrations of the at least one pair of immune system biomarkers; and (c) determining the indicator based on the at least one derived biomarker value, wherein the at least one pair of biomarker values comprises a first biomarker value and a second biomarker value corresponding to a first immune system biomarker and a second immune system biomarker, respectively, wherein the first immune system biomarker represents a polynucleotide expression product of a first IRS immune system biomarker gene, and wherein the second immune system biomarker represents a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from a group E IRS immune system biomarker gene, and wherein the second IRS immune system biomarker gene is selected from a group F IRS immune system biomarker gene.

In another broad form, it is an object of the invention to provide a method for inhibiting the development or progression of at least one condition selected from the group consisting of inSIRS and ipSIRS in a subject, the method comprising: exposing the subject to a treatment regimen for treating at least one condition based on an indicator obtained from an indicator determination method, wherein the indicator indicates the presence of the at least one condition in the subject, the indicator determination method comprising: (a) determining at least two pairs of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of the concentration of the immune system biomarker in a sample taken from the subject, (b) determining at least two derived biomarker values using the at least two pairs of biomarker values, the derived biomarker values being indicative of a ratio of the concentrations of each pair of immune system biomarkers; and (c) determining the indicator based on the at least two derived biomarker values, wherein the at least one pair of biomarker values comprises a first pair of biomarker values and a second pair of biomarker values, the first pair of biomarker values comprising a first biomarker value and a second biomarker value corresponding to a first immune system biomarker and a second immune system biomarker, respectively, wherein the first immune system biomarker represents a polynucleotide expression product of a first IRS immune system biomarker gene, and wherein the second immune system biomarker represents a polynucleotide expression product of a second IRS immune system biomarker gene, and the second pair of biomarker values comprising a third biomarker value and a fourth biomarker value corresponding to a third immune system biomarker and a fourth immune system biomarker, respectively, wherein the third immune system biomarker represents a polynucleotide expression product of a third IRS immune system biomarker gene And wherein the fourth immune system biomarker represents a polynucleotide expression product of a fourth IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from a group G IRS immune system biomarker gene, wherein the second IRS immune system biomarker gene is selected from a group H IRS immune system biomarker gene, wherein the third IRS immune system biomarker gene is selected from a group I IRS immune system biomarker gene, and wherein the fourth IRS immune system biomarker gene is selected from a group J IRS immune system biomarker gene.

In another broad form, it is an object of the invention to provide a method for inhibiting the development or progression of at least one condition selected from the group consisting of mild sepsis and severe sepsis in a subject, the method comprising: exposing the subject to a treatment regimen for treating at least one condition based on an indicator obtained from an indicator determination method, wherein the indicator indicates the presence of the at least one condition in the subject, the indicator determination method comprising: (a) determining at least one pair of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject, (b) determining at least one derived biomarker value using the at least one pair of biomarker values, the derived biomarker value being indicative of a ratio of the concentrations of the at least one pair of immune system biomarkers; and (c) determining the indicator based on the at least one derived biomarker value, wherein the at least one pair of biomarker values comprises a first biomarker value and a second biomarker value corresponding to a first immune system biomarker and a second immune system biomarker, respectively, wherein the first immune system biomarker represents a polynucleotide expression product of a first IRS immune system biomarker gene, and wherein the second immune system biomarker represents a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from a K group of IRS immune system biomarker genes, and wherein the second IRS immune system biomarker gene is selected from a L group of IRS immune system biomarker genes.

In another broad form, it is an object of the invention to provide a method for inhibiting the development or progression of at least one condition selected from the group consisting of mild sepsis and septic shock in a subject, the method comprising: exposing the subject to a treatment regimen for treating at least one condition based on an indicator obtained from an indicator determination method, wherein the indicator indicates the presence of the at least one condition in the subject, the indicator determination method comprising: (a) determining at least one pair of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject, (b) determining at least one derived biomarker value using the at least one pair of biomarker values, the derived biomarker value being indicative of a ratio of the concentrations of the at least one pair of immune system biomarkers; and (c) determining the indicator based on the at least one derived biomarker value, wherein the at least one pair of biomarker values comprises a first biomarker value and a second biomarker value corresponding to a first immune system biomarker and a second immune system biomarker, respectively, wherein the first immune system biomarker represents a polynucleotide expression product of a first IRS immune system biomarker gene, and wherein the second immune system biomarker represents a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from a group M IRS immune system biomarker genes, and wherein the second IRS immune system biomarker gene is selected from a group N IRS immune system biomarker genes.

In another broad form, it is an object of the invention to provide a method for inhibiting the development or progression of at least one condition selected from the group consisting of severe sepsis and septic shock in a subject, the method comprising: exposing the subject to a treatment regimen for treating at least one condition based on an indicator obtained from an indicator determination method, wherein the indicator indicates the presence of the at least one condition in the subject, the indicator determination method comprising: (a) determining at least one pair of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject, (b) determining at least one derived biomarker value using the at least one pair of biomarker values, the derived biomarker value being indicative of a ratio of the concentrations of the at least one pair of immune system biomarkers; and (c) determining the indicator based on the at least one derived biomarker value, wherein the at least one pair of biomarker values comprises a first biomarker value and a second biomarker value corresponding to a first immune system biomarker and a second immune system biomarker, respectively, wherein the first immune system biomarker represents a polynucleotide expression product of a first IRS immune system biomarker gene, and wherein the second immune system biomarker represents a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from a group O IRS immune system biomarker gene, and wherein the second IRS immune system biomarker gene is selected from a group P IRS immune system biomarker gene.

In some embodiments, the method comprises collecting a sample from the subject and obtaining the indicator according to an indicator determination method.

In some embodiments, the method comprises: a sample taken from the subject is sent to a laboratory where an indicator is determined.

Typically, the sample comprises cells obtained from a subject or a nucleic acid sample thereof.

In another broad form, it is an object of the invention to provide a method for distinguishing inSIRS from ipSIRS in a biological subject, the method comprising:

a) obtaining a sample collected from a biological subject exhibiting clinical signs of SIRS, the sample comprising a polynucleotide expression product;

b) quantifying polynucleotide expression products within the sample to determine a pair of biomarker values selected from the group consisting of:

i) a first pair of biomarker values indicative of the concentration of polynucleotide expression products of the PLA2G7 gene and the PLAC8 gene;

ii) a second pair of biomarker values indicative of the concentration of the polynucleotide expression product of the CEACAM4 gene and the LAMP1 gene;

c) determining an indicator indicative of a ratio of concentrations of the polynucleotide expression products using the pair of biomarker values; and the number of the first and second groups,

d) Comparing the indicator to a first indicator reference and a second indicator reference, the first indicator reference and the second indicator reference indicating inSIRS and ipSIRS, respectively; and the number of the first and second groups,

e) determining a likelihood that the subject has insiRS or ipsIRS based on the result of the comparison.

In another broad form, it is an object of the invention to provide a method for distinguishing inSIRS from a health condition in a biological subject, the method comprising:

b) quantifying a polynucleotide expression product within the sample to determine a pair of biomarker values indicative of a concentration of the polynucleotide expression product of a first IRS immune system biomarker gene and a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from a group a IRS immune system biomarker gene, and wherein the second IRS immune system biomarker gene is selected from a group B IRS immune system biomarker gene;

d) Comparing the indicator to a first indicator reference and a second indicator reference, the first indicator reference and the second indicator reference being indicative of inSIRS and a health condition, respectively; and the number of the first and second groups,

e) determining a likelihood that the subject has insiRS or the health condition based on a result of the comparison.

In another broad form, it is an object of the invention to provide a method for distinguishing ipSIRS from a health condition in a biological subject, the method comprising:

b) quantifying a polynucleotide expression product within the sample to determine a pair of biomarker values indicative of a concentration of the polynucleotide expression product of a first IRS immune system biomarker gene and a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from a group C IRS immune system biomarker gene, and wherein the second IRS immune system biomarker gene is selected from a group D IRS immune system biomarker gene;

d) Comparing the indicator to a first indicator reference and a second indicator reference, the first indicator reference and the second indicator reference indicating ipSIRS and a health condition, respectively; and the number of the first and second groups,

e) determining a likelihood that the subject has ipSIRS or the health condition based on a result of the comparing.

b) quantifying a polynucleotide expression product within the sample to determine a pair of biomarker values indicative of a concentration of the polynucleotide expression product of a first IRS immune system biomarker gene and a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from a group E IRS immune system biomarker gene, and wherein the second IRS immune system biomarker gene is selected from a group F IRS immune system biomarker gene;

i) a first pair of biomarker values indicative of a concentration of a polynucleotide expression product of a first IRS immune system biomarker gene and a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from a group G IRS immune system biomarker gene, and wherein the second IRS immune system biomarker gene is selected from a group H IRS immune system biomarker gene;

ii) a second pair of biomarker values indicative of a concentration of a polynucleotide expression product of a third IRS immune system biomarker gene and a fourth IRS immune system biomarker gene, wherein the third IRS immune system biomarker gene is selected from group I IRS immune system biomarker genes, and wherein the fourth IRS immune system biomarker gene is selected from group J IRS immune system biomarker genes;

In another broad form, it is an object of the invention to provide a method for distinguishing between mild sepsis and severe sepsis in a biological subject, the method comprising:

b) Quantifying a polynucleotide expression product within the sample to determine a pair of biomarker values indicative of a concentration of the polynucleotide expression product of a first IRS immune system biomarker gene and a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from the group K IRS immune system biomarker genes, and wherein the second IRS immune system biomarker gene is selected from the group L IRS immune system biomarker genes;

d) comparing the indicator to a first indicator reference and a second indicator reference, the first indicator reference and the second indicator reference indicating mild sepsis and severe sepsis, respectively; and the number of the first and second groups,

e) determining the likelihood that the subject has mild sepsis or severe sepsis based on the results of the comparison.

In another broad form, it is an object of the invention to provide a method for distinguishing between mild sepsis and septic shock in a biological subject, the method comprising:

b) Quantifying a polynucleotide expression product within the sample to determine a pair of biomarker values indicative of a concentration of the polynucleotide expression product of a first IRS immune system biomarker gene and a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from a group M IRS immune system biomarker gene, and wherein the second IRS immune system biomarker gene is selected from a group N IRS immune system biomarker gene;

d) comparing the indicator to a first indicator reference and a second indicator reference, the first indicator reference and the second indicator reference indicating mild sepsis and septic shock, respectively; and the number of the first and second groups,

e) determining the likelihood that the subject has mild sepsis or septic shock based on the results of the comparison.

In another broad form, it is an object of the invention to provide a method for distinguishing between severe sepsis and septic shock in a biological subject, the method comprising:

b) Quantifying a polynucleotide expression product within the sample to determine a pair of biomarker values indicative of a concentration of the polynucleotide expression product of a first IRS immune system biomarker gene and a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from a group O IRS immune system biomarker gene, and wherein the second IRS immune system biomarker gene is selected from a group P IRS immune system biomarker gene;

d) comparing the indicator to a first indicator reference and a second indicator reference, the first indicator reference and the second indicator reference indicating severe sepsis and septic shock, respectively; and the number of the first and second groups,

e) determining the likelihood that the subject has severe sepsis or septic shock based on the results of the comparison.

In general, the method comprises:

a) determining a first derived biomarker value indicative of a ratio of concentrations of the polynucleotide expression products using the first pair of biomarker values;

b) determining a second derived biomarker value indicative of a ratio of concentrations of the polynucleotide expression product using the first pair of biomarker values; and the number of the first and second groups,

Typically, the first and second indicator references are distributions of indicators determined for a first and second set of reference populations consisting of individuals diagnosed as having inSIRS and ipSIRS, respectively.

In another broad form, it is an object of the invention to provide a method for determining an indicator for use in assessing the likelihood of a biological subject having at least one medical condition, the method comprising:

a) obtaining a sample collected from a biological subject, the sample comprising a polynucleotide expression product;

b) amplifying at least some polynucleotide expression products in the sample;

c) determining an amount of amplification representing the degree of amplification required to obtain a defined level of each of a pair of polynucleotide expression products selected from the group consisting of:

i) a first pair of polynucleotide expression products of the PLA2G7 gene and the PLAC8 gene;

ii) a second pair of polynucleotide expression products of the CEACAM4 gene and the LAMP1 gene;

d) determining the indicator by determining the difference between the amounts of amplification; and the number of the first and second groups,

e) Using the indicator to assess a likelihood that the biological subject has a medical condition.

b) amplifying at least some polynucleotide expression products in the sample;

c) determining an amount of amplification representing the degree of amplification required to obtain a defined level of each of a pair of polynucleotide expression products selected from the group consisting of: a polynucleotide expression product of a first IRS immune system biomarker gene and a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group a IRS immune system biomarker genes, and wherein the second IRS immune system biomarker gene is selected from group B IRS immune system biomarker genes;

b) amplifying at least some polynucleotide expression products in the sample;

c) determining an amount of amplification representing the degree of amplification required to obtain a defined level of each of a pair of polynucleotide expression products selected from the group consisting of: a polynucleotide expression product of a first IRS immune system biomarker gene and a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group C IRS immune system biomarker genes, and wherein the second IRS immune system biomarker gene is selected from group D IRS immune system biomarker genes;

b) amplifying at least some polynucleotide expression products in the sample;

c) determining an amount of amplification representing the degree of amplification required to obtain a defined level of each of a pair of polynucleotide expression products selected from the group consisting of: a polynucleotide expression product of a first IRS immune system biomarker gene and a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group E IRS immune system biomarker genes, and wherein the second IRS immune system biomarker gene is selected from group F IRS immune system biomarker genes;

b) Amplifying at least some polynucleotide expression products in the sample;

i) a first pair of polynucleotide expression products of a first IRS immune system biomarker gene and a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from a group G IRS immune system biomarker gene, and wherein the second IRS immune system biomarker gene is selected from a group H IRS immune system biomarker gene;

ii) a second pair of polynucleotide expression products of a third IRS immune system biomarker gene and a fourth IRS immune system biomarker gene, wherein the third IRS immune system biomarker gene is selected from group I IRS immune system biomarker genes, and wherein the fourth IRS immune system biomarker gene is selected from group J IRS immune system biomarker genes;

b) amplifying at least some polynucleotide expression products in the sample;

c) determining an amount of amplification representing the degree of amplification required to obtain a defined level of each of a pair of polynucleotide expression products selected from the group consisting of: a polynucleotide expression product of a first IRS immune system biomarker gene and a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group K IRS immune system biomarker genes, and wherein the second IRS immune system biomarker gene is selected from group L IRS immune system biomarker genes;

b) amplifying at least some polynucleotide expression products in the sample;

c) determining an amount of amplification representing the degree of amplification required to obtain a defined level of each of a pair of polynucleotide expression products selected from the group consisting of: a polynucleotide expression product of a first IRS immune system biomarker gene and a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from a group M IRS immune system biomarker gene, and wherein the second IRS immune system biomarker gene is selected from a group N IRS immune system biomarker gene;

b) Amplifying at least some polynucleotide expression products in the sample;

c) determining an amount of amplification representing the degree of amplification required to obtain a defined level of each of a pair of polynucleotide expression products selected from the group consisting of: a polynucleotide expression product of a first IRS immune system biomarker gene and a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from the group O IRS immune system biomarker genes, and wherein the second IRS immune system biomarker gene is selected from the group P IRS immune system biomarker genes;

In general, the method comprises:

a) comparing the indicator to a first indicator reference and a second indicator reference, wherein the first indicator reference and the second indicator reference are distributions of indicators determined for a first set of reference populations and a second set of reference populations, one of the first set and the second set consisting of individuals diagnosed as having the medical condition; and the number of the first and second groups,

b) determining a likelihood that the subject has a medical condition based on the result of the comparison.

Typically, the amplification amount is at least one of:

a) the cycle time;

b) the number of cycles;

c) a cycling threshold;

d) the amplification time; and the number of the first and second groups,

e) relative to the amount of amplification of another amplified product.

In another broad form, it is an object of the invention to provide a method for use in assessing the likelihood of a biological subject having a medical condition, the method comprising, in one or more processing devices:

a) determining a pair of biomarker values selected from the group consisting of:

b) determining an indicator indicative of a ratio of concentrations of the polynucleotide expression products using the pair of biomarker values;

c) retrieving from a database previously determined first and second indicator references, the first and second indicator references determined based on indicators determined by a first and second set of reference populations, one of the sets consisting of individuals diagnosed as having a medical condition;

d) comparing the indicator to the first indicator reference and the second indicator reference;

e) using the results of the comparison to determine a probability that the subject is indicated as having the medical condition; and the number of the first and second groups,

f) generating a representation of the probability, the representation being presented to a user to allow the user to assess a likelihood that a biological subject has at least one medical condition.

In general, the method comprises:

a) determining a first derived biomarker value using the first pair of biomarker values, the first derived biomarker value being indicative of a ratio of concentrations of the polynucleotide expression products;

b) Determining a second derived biomarker value using the first pair of biomarker values, the second derived biomarker value being indicative of a ratio of concentrations of the polynucleotide expression products; and the number of the first and second groups,

In another broad form, it is an object of the invention to provide apparatus for determining an indicator for use in determining the likelihood of a biological subject having at least one medical condition, the apparatus comprising:

a) a sampling device that obtains a sample collected from a biological subject, the sample comprising a polynucleotide expression product;

b) a measurement device that quantifies a polynucleotide expression product within the sample to determine a pair of biomarker values selected from the group consisting of:

c) At least one processing device, the at least one processing device:

i) receiving an indication of the pair of biomarker values from the measurement device;

ii) determining an indicator using the biomarker value using a ratio of the concentrations of the first polynucleotide expression product and the second polynucleotide expression product; and the number of the first and second groups,

iii) comparing the indicator with at least one indicator reference; and the number of the first and second groups,

iv) determining a likelihood that the subject has the at least one medical condition using the results of the comparison; and the number of the first and second groups,

v) generating a representation of the indicator and the likelihood for presentation to the user.

b) in the measuring device:

i) amplifying at least some polynucleotide expression products in the sample;

ii) determining an amplification level representing the degree of amplification required to obtain a defined level of polynucleotide expression product, comprising:

(1) (ii) the amount of amplification of the first pair of polynucleotide expression products for the PLA2G7 gene and the PLAC8 gene;

(2) Amplification amounts for the second pair of polynucleotide expression products of CEACAM4 gene and LAMP1 gene;

c) in a processing system:

i) retrieving the augmentation quantity;

ii) determining the indicator by:

(1) determining a first derived biomarker value indicative of a ratio of concentrations of the first pair of polynucleotide expression products by determining a difference between the amplified amounts for the first pair;

(2) determining a second derived biomarker value indicative of a ratio of concentrations of the second pair of polynucleotide expression products by determining a difference between the amplified amounts for the second pair;

(3) determining the indicator by adding the first derived biomarker value and the second derived biomarker value;

iii) retrieving from a database previously determined first and second indicator references, wherein the first and second indicator references are distributions of indicators determined for a first and second set of reference populations, the first and second set consisting of individuals diagnosed as having inSIRS and ipSIRS, respectively;

iv) comparing the indicator to the first indicator reference and the second indicator reference;

v) using the results of the comparison to determine a likelihood that the subject is classified within the first group or the second group;

vi) generating a representation indicative at least in part of the indicator and the probability; and the number of the first and second groups,

vii) providing the representation to a user to allow the user to assess a likelihood that the biological subject has at least one medical condition.

In another broad form, it is an object of the invention to provide a method for determining an indicator for use in assessing the likelihood of a biological subject having the presence, absence, extent or prognosis of at least one medical condition, the method comprising:

a) determining a plurality of biomarker values, each biomarker value being indicative of at least one corresponding immune system biomarker measured or derived value for the biological subject and being at least partially indicative of the concentration of the immune system biomarker in a sample taken from the subject;

b) determining the indicator using a combination of the plurality of biomarker values, wherein:

i) the at least two biomarkers have a cross-correlation with respect to at least one condition within a cross-correlation range, the cross-correlation range being between ± 0.9; and the number of the first and second electrodes,

ii) the indicator has a performance value greater than or equal to a performance threshold representing the indicator's ability to diagnose the presence, absence, degree or prognosis of at least one condition, the performance threshold being indicative of an interpretation variance of at least 0.3.

In general, the method comprises:

c) determining a plurality of measured biomarker values, each measured biomarker value being a measured value of a corresponding biomarker for a biological subject; and the number of the first and second groups,

d) determining the indicator by applying a function to at least one of the measured biomarker values to determine at least one derived biomarker value, the at least one derived biomarker value being indicative of a value of a corresponding derived biomarker.

Typically, the function includes at least one of:

a) multiplying the two biomarker values;

b) dividing the two biomarker values;

c) adding the two biomarker values;

d) subtracting the two biomarker values;

e) a ratio of two biomarker values;

f) a weighted sum of at least two biomarker values;

g) a logarithmic sum of at least two biomarker values; and the number of the first and second groups,

h) a sigmoidal function (sigmoidal function) of at least two biomarker values.

Typically, the method comprises determining at least one derived biomarker value corresponding to a ratio of two measured biomarker values.

Typically, the method comprises combining at least two biomarker values to determine an indicator value representative of the indicator.

Typically, the method comprises combining at least two biomarker values using a combination function, the combination function being at least one of:

a) an additive model;

b) a linear model;

c) a support vector machine;

d) a neural network model;

e) a random forest model;

f) a regression model;

g) a genetic algorithm;

h) an annealing algorithm;

i) a weighted sum;

j) a nearest neighbor model; and the number of the first and second groups,

k) and (4) a probability model.

Typically, at least one of the at least two biomarkers is a derived biomarker.

Generally, the method comprises:

a) determining a first derived biomarker value indicative of a ratio of concentrations of a first immune system biomarker and a second immune system biomarker;

b) determining a second derived biomarker value indicative of a ratio of concentrations of a third measured immune system biomarker and a fourth measured immune system biomarker; and the number of the first and second groups,

c) adding the first derived biomarker value and the second derived biomarker value to generate an indicator value.

In general, the method includes, in an electronic processing device:

a) receiving a plurality of measured biomarker values, each measured biomarker value being a measured value of a corresponding immune system biomarker;

b) applying a function to the at least one measured biomarker value to determine at least one derived biomarker value, the at least one derived biomarker value being indicative of a value of a corresponding derived biomarker; and the number of the first and second groups,

c) combining the at least one derived biomarker value and the at least one other biomarker value to determine the indicator.

Typically, the cross-correlation range is at least one of:

a)±0.8；

b)±0.7；

c)±0.6；

d)±0.5；

e)±0.4；

f)±0.3；

g) plus or minus 0.2; and the number of the first and second groups,

h)±0.1。

typically, each biomarker has a condition correlation to the presence, absence, degree, or prognosis of at least one condition outside of a condition correlation range, the condition correlation range being between ± 0.3.

Typically, the condition relevance range is at least one of:

a)±0.9；

b)±0.8；

c)±0.7；

d)±0.6；

e) plus or minus 0.5; and the number of the first and second groups,

f)±0.4。

a)0.4；

b)0.5；

c)0.6；

d)0.7；

e) 0.8; and the number of the first and second groups,

f)0.9。

typically, the biomarker value is indicative of the level or abundance of a molecule selected from one or more of a nucleic acid molecule and a proteinaceous molecule.

Generally, the method includes generating a representation of the indicator.

Typically, the representation includes:

a) an alphanumeric indication of the indicator;

Generally, the method comprises:

b) the likelihood is determined based on the result of the comparison.

Typically, the indicator reference is based on at least one of:

a) an indicator threshold range;

b) an indicator threshold; and the number of the first and second groups,

c) the indicator distribution.

Typically, the indicator reference is based on a distribution of indicators determined for a group of reference populations, the group consisting of individuals diagnosed with a medical condition.

Typically, the reference population includes:

a) a plurality of individuals of different genders;

b) a plurality of individuals of different ethnicities;

c) a plurality of healthy individuals;

d) a plurality of individuals having at least one diagnosed medical condition;

e) a plurality of individuals exhibiting clinical signs of at least one medical condition; and the number of the first and second groups,

f) A first group of individuals and a second group of individuals, each group of individuals having a respective diagnosed medical condition.

Typically, the indicator is for use in determining a likelihood that the biological subject has at least one medical condition, and wherein the reference population comprises:

a) an individual exhibiting clinical signs of at least one medical condition;

b) an individual diagnosed as having at least one medical condition; and the number of the first and second groups,

c) a healthy individual.

Typically, the indicator reference is retrieved from a database.

b) the likelihood is determined based on the result of the comparison.

Generally, the method comprises:

Typically, the first indicator reference and the second indicator reference are distributions of indicators determined for a first set of reference populations and a second set of reference populations, the first set and the second set consisting of individuals diagnosed as having a first condition or a second condition, respectively.

Generally, the method comprises:

b) quantifying at least some of said polynucleotide expression products within said sample to determine at least one pair of biomarker values;

c) an indicator is determined using, at least in part, the pair of biomarker values.

Generally, the method comprises:

a) quantifying the polynucleotide expression product by:

d) determining the indicator by determining the difference between the amounts of amplification.

Typically, the amplification amount is at least one of:

a) the cycle time;

b) the number of cycles;

c) a cycling threshold;

d) the amplification time; and the number of the first and second groups,

e) relative to the amount of amplification of another amplified product.

Generally, the method comprises:

Typically, the immune system biomarker is a biomarker of the immune system of the biological subject that is altered as part of the inflammatory response to the injury or pathogenic damage, or the expression level of the biomarker of the immune system of the biological subject that is altered as part of the inflammatory response to the injury or pathogenic damage.

Typically, the indicator is for determining a likelihood that the subject has at least one of inSIRS and ipSIRS, and wherein the method comprises:

Typically, the indicator is for determining the likelihood of the subject having inSIRS or ipSIRS, and wherein the method comprises:

Typically, the indicator is for determining the likelihood of the subject having inSIRS or a healthy condition, and wherein the biomarker value is determined from at least one IRS immune system biomarker in each of the first and second IRS immune system biomarker groups, wherein:

a) the first IRS immune system biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from group a IRS immune system biomarker genes; and is

b) The second IRS immune system biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from group B IRS immune system biomarker genes.

Typically, the indicator is for determining the likelihood of the subject having ipSIRS or a healthy condition, and wherein biomarker values are determined from at least one IRS immune system biomarker in each of a first IRS immune system biomarker group and a second IRS immune system biomarker group, wherein:

a) the first IRS immune system biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from group C IRS immune system biomarker genes; and the number of the first and second electrodes,

b) the second IRS immune system biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from group D IRS immune system biomarker genes.

Typically, the indicator is for determining the likelihood of the subject having inSIRS or ipSIRS, and wherein biomarker values are determined from at least one IRS immune system biomarker in each of the first and second IRS immune system biomarker groups, wherein:

a) the first IRS immune system biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from group E IRS immune system biomarker genes; and the number of the first and second electrodes,

b) The second IRS immune system biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from group F IRS immune system biomarker genes.

Typically, the indicator is for determining the likelihood of the subject having inSIRS or ipSIRS, and wherein biomarker values are determined from at least one IRS immune system biomarker in each of a first IRS immune system biomarker group, a second IRS immune system biomarker group, a third IRS immune system biomarker group, and a fourth IRS immune system biomarker group, wherein:

a) the first IRS immune system biomarker group consists of polynucleotide expression products and/or polypeptide expression products from group G IRS immune system biomarker genes;

b) the second IRS immune system biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from group H IRS immune system biomarker genes;

c) the third IRS immune system biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from group I IRS immune system biomarker genes; and the number of the first and second electrodes,

d) the fourth IRS immune system biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from group J IRS immune system biomarker genes.

Typically, the first IRS immune system biomarker is a PLA2G7 expression product, the second IRS immune system biomarker is a PLAC8 expression product, the third IRS immune system biomarker is a CEACAM4 expression product, and the fourth IRS immune system biomarker is a LAMP1 expression product.

Typically, the indicator is for determining the likelihood of the subject having mild sepsis or severe sepsis, and wherein the biomarker values are determined from at least one IRS immune system biomarker in each of the first and second IRS immune system biomarker groups, wherein:

a) the first IRS immune system biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from the group K IRS immune system biomarker genes; and the number of the first and second electrodes,

b) the second IRS immune system biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from the L-group IRS immune system biomarker genes.

Typically, the indicator is for determining the likelihood of the subject having mild sepsis or septic shock, and wherein the biomarker values are determined from at least one IRS immune system biomarker in each of the first and second IRS immune system biomarker groups, wherein:

a) The first IRS immune system biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from group M IRS immune system biomarker genes; and the number of the first and second electrodes,

b) the second IRS immune system biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from group N IRS immune system biomarker genes.

Typically, the indicator is for determining the likelihood of the subject having severe sepsis or septic shock, and wherein the biomarker values are determined from at least one IRS immune system biomarker in each of the first and second IRS immune system biomarker groups, wherein:

a) the first IRS immune system biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from group O IRS immune system biomarker genes; and the number of the first and second electrodes,

b) the second IRS immune system biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from the P-group IRS immune system biomarker genes.

In another broad form, it is an object of the invention to provide an apparatus for determining an indicator for use in assessing the likelihood of a biological subject having the presence, absence, extent or prognosis of at least one medical condition, the apparatus comprising electronic processing means for:

In one broad form, it is an object of the present invention to provide a method for determining an indicator for use in diagnosing the presence, absence, extent or prognosis of at least one condition in a biological subject, the method comprising:

a) determining a plurality of biomarker values, each biomarker value being indicative of a value measured or derived for at least one corresponding biomarker of the biological subject;

b) Determining the indicator using a combination of the plurality of biomarker values, the at least one indicator being at least partially indicative of the presence, absence, extent, or prognosis of the at least one condition, wherein:

i) at least two markers have a cross-correlation for at least one condition within a cross-correlation range, the cross-correlation range being between ± 0.9; and the number of the first and second electrodes,

Generally, the method comprises:

a) determining a plurality of measured biomarker values, each measured biomarker value being a measured value of a corresponding biomarker for a biological subject; and the number of the first and second groups,

b) applying a function to the at least one measured biomarker value determines at least one derived biomarker value indicative of a value of a corresponding derived biomarker.

Typically, the function includes at least one of:

a) multiplying the two biomarker values;

b) dividing the two biomarker values;

c) Adding the two biomarker values;

d) subtracting the two biomarker values;

e) a weighted sum of at least two biomarker values;

f) a logarithmic sum of at least two biomarker values; and the number of the first and second groups,

g) a sigmoid function of at least two biomarker values.

a) an additive model;

b) a linear model;

c) a support vector machine;

d) a neural network model;

e) a random forest model;

f) a regression model;

g) a genetic algorithm;

h) an annealing algorithm;

i) a weighted sum;

j) a nearest neighbor model; and the number of the first and second groups,

k) and (4) a probability model.

Typically, at least one of the at least two biomarkers is a derived biomarker.

Generally, the method comprises:

a) determining a first derived biomarker value, the first derived biomarker value being a ratio of the first measured biomarker value and the second measured biomarker value;

b) Determining a second derived biomarker value, the second derived biomarker value being a ratio of the third measured biomarker value and the fourth measured biomarker value; and the number of the first and second groups,

Generally, the method comprises:

a) determining an indicator value;

b) comparing the indicator value to at least one indicator value range; and the number of the first and second groups,

c) using the results of said comparison to diagnose the presence, absence, extent or prognosis of at least one condition.

Typically, the method is performed at least in part using an electronic processing device.

In general, the method includes, in an electronic processing device:

a) receiving a plurality of measured biomarker values, each measured biomarker value being a measured value of a corresponding biomarker for a biological subject;

c) combining the at least one derived biomarker value and the at least one other biomarker value to determine an indicator value.

Typically, the method comprises generating the representation from at least one indicator value.

Generally, the method comprises:

a) comparing the indicator value to at least one indicator value range; and the number of the first and second groups,

b) displaying the result of the comparison.

Typically, the cross-correlation range is at least one of:

a)±0.8；

b)±0.7；

c)±0.6；

d)±0.5；

e)±0.4；

f)±0.3；

g) plus or minus 0.2; and the number of the first and second groups,

h)±0.1。

Typically, the condition relevance range is at least one of:

a)±0.9；

b)±0.8；

c)±0.7；

d)±0.6；

e) plus or minus 0.5; and the number of the first and second groups,

f)±0.4。

a)0.4；

b)0.5；

c)0.6；

d)0.7；

e) 0.8; and the number of the first and second groups,

f)0.9。

typically, the biomarker value is indicative of the level or abundance of a molecule or entity selected from one or more of the following:

a) a nucleic acid molecule;

b) a proteinaceous molecule;

c) amino acids

d) A carbohydrate;

e) a lipid;

f) a steroid;

g) an inorganic molecule;

h) ions;

i) a drug;

j) a chemical;

k) a metabolite;

l) a toxin;

m) nutrients;

n) a gas;

o) cells;

p) pathogenic organisms; and the number of the first and second groups,

q) non-pathogenic organisms.

In another broad form, the invention aims to provide an apparatus for determining an indicator for use in diagnosing the presence, absence, extent or prognosis of at least one medical condition in a biological subject, the apparatus comprising electronic processing means which:

i) the at least two biomarkers have a cross-correlation with respect to at least one condition within a cross-correlation range, the cross-correlation range being between ± 0.9; and the number of the first and second groups,

In another broad form, it is an object of the invention to provide a diagnostic marker for diagnosing the presence, absence, extent or prognosis of at least one condition in a biological subject, the diagnostic marker defining a combination of at least two biomarker values corresponding to values of a biomarker measurable or derivable with the biological subject, wherein:

a) The at least two biomarkers have a cross-correlation with respect to at least one condition within a cross-correlation range, the cross-correlation range being between ± 0.9; and the number of the first and second electrodes,

b) said combination of at least two biomarker values has a performance value greater than or equal to a performance threshold representing the ability of said combination of said at least two biomarkers to diagnose the presence, absence, extent or prognosis of at least one condition, said performance threshold being indicative of an interpretation variance of at least 0.3.

Typically, the diagnostic identifier defines a function that is applied to at least one measured biomarker value to determine at least one derived biomarker value that is indicative of a value of a corresponding derived biomarker.

Typically, the function includes at least one of:

a) multiplying the two biomarker values;

b) dividing the two biomarker values;

c) adding the two biomarker values;

d) subtracting the two biomarker values;

e) a weighted sum of at least two biomarker values;

g) a sigmoid function of at least two biomarker values.

Typically, the at least one derived biomarker value corresponds to a ratio of two measured biomarker values.

Typically, the diagnostic identifier defines a combination of at least two biomarker values for determining an indicator value representative of said indicator.

Typically, the diagnostic identifier defines a combination function for combining at least two biomarker values, the combination function being at least one of:

a) an additive model;

b) a linear model;

c) a support vector machine;

d) a neural network model;

e) a random forest model;

f) a regression model;

g) a genetic algorithm;

h) an annealing algorithm; and the number of the first and second groups,

i) a weighted sum.

Typically, at least one of the at least two biomarkers is a derived biomarker.

Generally, a diagnostic identifier defines:

a) a first derived biomarker value that is a ratio of the first measured biomarker value and the second measured biomarker value;

b) a second derived biomarker value that is a ratio of the third measured biomarker value and the fourth measured biomarker value; and the number of the first and second groups,

c) a combination of the first derived biomarker value and the second derived biomarker value to generate an indicator value.

Typically, the diagnostic identifier defines at least one indicator value range, and wherein the presence, absence, degree, or prognosis of the at least one condition is diagnosed using a comparison of the at least one indicator value to the at least one indicator value range.

Typically, the cross-correlation range is at least one of:

a)±0.8；

b)±0.7；

c)±0.6；

d)±0.5；

e)±0.4；

f)±0.3；

g) plus or minus 0.2; and the number of the first and second groups,

h)±0.1。

Typically, the condition relevance range is at least one of:

a)±0.9；

b)±0.8；

c)±0.7；

d)±0.6；

e) plus or minus 0.5; and the number of the first and second groups,

f)±0.4。

a)0.4；

b)0.5；

c)0.6；

d)0.7；

e) 0.8; and the number of the first and second groups,

f)0.9。

a) a nucleic acid molecule;

b) a proteinaceous molecule;

c) amino acids

d) A carbohydrate;

e) a lipid;

f) a steroid;

g) an inorganic molecule;

h) ions;

i) a drug;

j) a chemical;

k) a metabolite;

l) a toxin;

m) nutrients;

n) a gas;

o) cells;

p) pathogenic organisms; and the number of the first and second groups,

q) non-pathogenic organisms.

In another broad form, it is an object of the invention to provide a method of identifying a biomarker for use in diagnosing the presence, absence, extent or prognosis of at least one condition in a biological subject, the method comprising:

a) For a plurality of candidate biomarkers, ranking the candidate biomarkers according to each biomarker's ability to distinguish the presence, absence, extent, or prognosis of at least one condition in a biological subject;

b) selecting at least two candidate biomarkers according to the ranking, the at least two biomarkers having a cross-correlation for the at least one condition within a range of cross-correlations, the range of cross-correlations being between ± 0.9;

c) determining a performance value for a combination of the at least two candidate biomarkers; and the number of the first and second groups,

d) defining a diagnostic marker from said combination of said at least two biomarkers if said performance value is greater than or equal to a performance threshold representative of the ability of said indicator to diagnose the presence, absence, extent or prognosis of said at least one condition, said performance threshold being indicative of an interpretation variance of at least 0.3.

Generally, the method includes determining a combination of at least two candidate biomarkers using a combination function, the combination function being at least one of:

a) an additive model;

b) a linear model;

c) a support vector machine;

d) a neural network model;

e) a random forest model;

f) a regression model;

g) a genetic algorithm;

h) An annealing algorithm; and the number of the first and second groups,

i) a weighted sum.

Generally, the method comprises:

a) selecting a next combinatorial function;

b) determining whether a performance value of a combination of at least two candidate biomarkers determined by a next combination function is greater than or equal to a performance threshold; and the number of the first and second groups,

c) if the performance value is not greater than or equal to the performance threshold, repeating steps a) and b) for successive combinatorial functions.

Generally, the method comprises:

a) selecting two candidate biomarkers;

b) determining whether a performance value of a combination of two candidate biomarkers is greater than or equal to a performance threshold; and the number of the first and second groups,

c) if the performance value is not greater than or equal to the performance threshold, then:

i) combining the selected candidate biomarker with at least one additional candidate biomarker; and the number of the first and second groups,

ii) repeating steps a) and b) with at least one further candidate biomarker.

Typically, the method comprises combining a number of candidate biomarkers up to a limit.

Generally, the method comprises:

a) selecting the highest ranked candidate biomarker;

b) selecting the next highest ranked candidate biomarker;

c) for a selected candidate marker, determining whether the cross-correlation of the candidate biomarker is within a cross-correlation range; and the number of the first and second groups,

d) If not, repeating steps b) and c) until two candidate biomarkers having cross-correlations within the range of cross-correlations are selected.

Generally, the method comprises:

a) defining at least two sets of candidate biomarkers, the candidate biomarkers in different sets having a cross-correlation within a cross-correlation range;

b) ranking the candidate biomarkers in each group; and the number of the first and second groups,

c) candidate biomarkers were selected from different groups.

Generally, the method comprises:

a) defining a plurality of groups indicative of the presence, absence, degree or prognosis of at least one condition using reference data for at least one individual; and the number of the first and second groups,

b) a number of candidate biomarkers potentially useful for the discriminatory grouping are identified using at least one analytical technique.

In general, the method includes identifying a candidate biomarker using a reference value measured for a reference biomarker for at least one individual.

Typically, the method comprises filtering the reference biomarker using the reference value to determine the candidate biomarker.

Typically, at least one of the candidate biomarkers is a derived biomarker derived from at least one reference biomarker using a function.

Typically, the derived biomarker is derived from the filtered biomarker.

Generally, the method comprises:

a) applying a function to the at least one reference value to determine at least one derived reference biomarker value, the at least one derived reference biomarker value being indicative of a value of a corresponding derived reference biomarker; and the number of the first and second groups,

b) determining at least one candidate biomarker using the at least one derived reference biomarker value.

Generally, the method comprises:

b) for each group, the ranges of at least two reference biomarker values are combined to determine the range of indicator values for that group.

Typically, the cross-correlation range is at least one of:

a)±0.8；

b)±0.7；

c)±0.6；

d)±0.5；

e)±0.4；

f)±0.3；

g) plus or minus 0.2; and the number of the first and second groups,

h)±0.1。

typically, each biomarker has a condition correlation with respect to the presence, absence, extent, or prognosis of at least one condition that is outside of a condition correlation range, the condition correlation range being between ± 0.3.

Typically, the condition relevance range is at least one of:

a)±0.9；

b)±0.8；

c)±0.7；

d)±0.6；

e) plus or minus 0.5; and the number of the first and second groups,

f)±0.4。

a)0.4；

b)0.5；

c)0.6；

d)0.7；

e) 0.8; and the number of the first and second groups,

f)0.9。

in another broad form, the invention aims to provide an apparatus for identifying a marker for use in a diagnostic marker for use in diagnosing the presence, absence, extent or prognosis of at least one condition in a biological subject, the apparatus comprising electronic processing means which:

In another broad form, it is an object of the invention to provide a method for diagnosing the presence or absence of inSIRS or a health condition in a biological subject, the method comprising: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, extent or prognosis of at least one condition selected from insiRS and a health condition, wherein: (i) at least two IRS biomarkers have a cross-correlation for the at least one condition within a cross-correlation range, the cross-correlation range being between ± 0.9; and, (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or extent of at least one condition, or to provide a prognosis of at least one condition, the performance threshold indicating an interpretation variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker panel, and wherein at least another one of the at least two IRS biomarkers is selected from a second IRS biomarker panel, wherein the first IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from group a IRS biomarker genes, and wherein the second IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from group B IRS biomarker genes.

In another broad form, it is an object of the invention to provide a method for diagnosing the presence or absence of ipSIRS or a health condition in a biological subject, the method comprising: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, extent, or prognosis of at least one condition selected from ipsIRS and a health condition, wherein: (i) at least two IRS biomarkers have a cross-correlation for at least one condition within a cross-correlation range, the cross-correlation range being between ± 0.9; and, (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or extent of at least one condition, or to provide a prognosis of at least one condition, the performance threshold indicating an interpretation variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker panel, and wherein at least another one of the at least two IRS biomarkers is selected from a second IRS biomarker panel, wherein the first IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from group C IRS biomarker genes, and wherein the second IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from group D IRS biomarker genes.

In another broad form, it is an object of the invention to provide a method for diagnosing the presence or absence of inSIRS or ipSIRS in a biological subject, the method comprising: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, extent or prognosis of at least one condition selected from insiRS and ipsIRS, wherein: (i) at least two IRS biomarkers have a cross-correlation for the at least one condition within a cross-correlation range, the cross-correlation range being between ± 0.9; and, (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or extent of at least one condition, or to provide a prognosis of at least one condition, the performance threshold indicating an interpretation variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker panel, and wherein at least another one of the at least two IRS biomarkers is selected from a second IRS biomarker panel, wherein the first IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from group E IRS biomarker genes, and wherein the second IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from group F IRS biomarker genes.

In another broad form, it is an object of the invention to provide a method for diagnosing the presence or absence of inSIRS or ipSIRS in a biological subject, the method comprising: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, extent or prognosis of at least one condition selected from insiRS and ipsIRS, wherein: (i) at least four IRS biomarkers have a cross-correlation for at least one condition within a cross-correlation range, the cross-correlation range being between ± 0.9; and, (ii) the indicator has a performance value greater than or equal to a performance threshold representing the indicator's ability to diagnose the presence, absence or extent of at least one condition, or to provide a prognosis of at least one condition, the performance threshold indicating an interpretation variance of at least 0.3, wherein at least one of the at least four IRS biomarkers is selected from a first IRS biomarker panel, wherein at least another of the at least four IRS biomarkers is selected from a second IRS biomarker panel, wherein at least another of the at least four IRS biomarkers is selected from a third IRS biomarker panel, and wherein at least another of the at least four IRS biomarkers is selected from a fourth IRS biomarker panel, wherein the first IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products selected from a G group IRS biomarker gene, wherein the second IRS biomarker panel consists of polynucleotide expression products and polypeptide expression products selected from a H group IRS biomarker gene And/or polypeptide expression products, wherein the third IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from group I IRS biomarker genes, and wherein the fourth IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from group J IRS biomarker genes.

Suitably, the first IRS biomarker is a PLA2G7 expression product, the second IRS biomarker is a PLAC8 expression product, the third IRS biomarker is a CEACAM4 expression product, and the fourth IRS biomarker is a LAMP1 expression product.

In another broad form, it is an object of the invention to provide a method for diagnosing the presence or absence of mild sepsis or severe sepsis in a biological subject, the method comprising: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, extent or prognosis of at least one condition selected from mild sepsis and severe sepsis, wherein: (i) at least two IRS biomarkers have a cross-correlation for the at least one condition within a cross-correlation range, the cross-correlation range being between ± 0.9; and, (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or extent of at least one condition, or to provide a prognosis of at least one condition, the performance threshold indicating an interpretation variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker panel, and wherein at least another one of the at least two IRS biomarkers is selected from a second IRS biomarker panel, wherein the first IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from a K group IRS biomarker genes, and wherein the second IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from a L group IRS biomarker genes.

In another form, it is an object of the invention to provide a method for diagnosing the presence or absence of mild sepsis or septic shock in a biological subject, the method comprising: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, extent or prognosis of at least one condition selected from mild sepsis and septic shock, wherein: (i) at least two IRS biomarkers have a cross-correlation for the at least one condition within a cross-correlation range, the cross-correlation range being between ± 0.9; and, (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or extent of at least one condition, or to provide a prognosis of at least one condition, the performance threshold indicating an interpretation variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker panel, and wherein at least another of the at least two IRS biomarkers is selected from a second IRS biomarker panel, wherein the first IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from a group M IRS biomarker genes, and wherein the second IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from a group N IRS biomarker genes.

In another form, it is an object of the present invention to provide a method for diagnosing the presence or absence of severe sepsis or septic shock in a biological subject, the method comprising: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, extent or prognosis of at least one condition selected from severe sepsis and septic shock, wherein: (i) at least two IRS biomarkers have a cross-correlation for the at least one condition within a cross-correlation range, the cross-correlation range being between ± 0.9; and, (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or extent of at least one condition, or to provide a prognosis of at least one condition, the performance threshold indicating an interpretation variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker panel, and wherein at least another one of the at least two IRS biomarkers is selected from a second IRS biomarker panel, wherein the first IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from an O group IRS biomarker gene, and wherein the second IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from a P group IRS biomarker gene.

In another broad form, it is an object of the invention to provide a kit comprising: (i) (ii) reagents allowing quantification of the first IRS biomarker; and (ii) an agent allowing quantification of a second IRS biomarker, wherein said first IRS biomarker and said second IRS biomarker have a cross-correlation with respect to at least one condition selected from the group consisting of healthy condition, insiRS, ipsIRS, or a stage of ipsIRS selected from mild sepsis, severe sepsis, and septic shock, said at least one condition lying within a cross-correlation range between ± 0.9, and wherein for a combination of the respective biomarker values of the first IRS biomarker and the second IRS biomarker measured or derived from a biological subject having a performance value greater than or equal to a performance threshold representing the ability of the combination of the first IRS biomarker and the second IRS biomarker to diagnose the presence, absence, or extent of the at least one condition or to provide a prognosis of the at least one condition, the performance threshold is an interpretation variance of at least 0.3.

Suitably, the kit further comprises: (iii) (ii) reagents allowing quantification of a third IRS biomarker; and (ii) an agent that allows quantification of a fourth IRS biomarker, wherein the third IRS biomarker and the fourth IRS biomarker have a cross-correlation with respect to at least one condition that lies within a cross-correlation range of ± 0.9, and wherein for a combination of the respective biomarker values of the third IRS biomarker and the fourth IRS biomarker measured to or derived from a biological subject has a performance value that is greater than or equal to a performance threshold representing the ability of the combination of the third IRS biomarker and the fourth IRS biomarker to diagnose the presence, absence, or extent of the at least one condition or to provide a prognosis of the at least one condition, the performance threshold is an interpretation variance of at least 0.3.

Suitably, the kit is for diagnosing the presence or absence of inSIRS or a health condition, wherein the first IRS biomarker is selected from a first IRS biomarker panel, and wherein the second IRS biomarker is selected from a second IRS biomarker panel, wherein the first IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from group a IRS biomarker genes, and wherein the second IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from group B IRS biomarker genes.

Suitably, the kit is for diagnosing the presence or absence of ipSIRS or a health condition, wherein said first IRS biomarker is selected from a first IRS biomarker panel and wherein said second IRS biomarker is selected from a second IRS biomarker panel, wherein said first IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from a group C IRS biomarker gene and wherein said second IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from a group D IRS biomarker gene.

Suitably, the kit is for diagnosing the presence or absence of inSIRS or ipSIRS, wherein said first IRS biomarker is selected from a first IRS biomarker panel and wherein said second IRS biomarker is selected from a second IRS biomarker panel, wherein said first IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from a group E IRS biomarker gene and wherein said second IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from a group F IRS biomarker gene.

Suitably, the kit is for diagnosing the presence or absence of inSIRS or ipSIRS, wherein the first IRS biomarker is selected from a first IRS biomarker group, wherein the second IRS biomarker is selected from a second IRS biomarker group, wherein the third IRS biomarker is selected from a third IRS biomarker group, and wherein the fourth IRS biomarker is selected from a fourth IRS biomarker group, wherein the first IRS biomarker group consists of polynucleotide expression products and/or polypeptide expression products from group G IRS biomarker genes, wherein the second IRS biomarker group consists of polynucleotide expression products and/or polypeptide expression products from group H IRS biomarker genes, wherein the third IRS biomarker group consists of polynucleotide expression products and/or polypeptide expression products from group I IRS biomarker genes, and wherein the fourth IRS biomarker group consists of polynucleotide expression products and polypeptide expression products from group J IRS biomarker genes And/or polypeptide expression product composition.

Suitably, the kit is for diagnosing the presence or absence of mild sepsis or severe sepsis, wherein said first IRS biomarker is selected from a first IRS biomarker panel and wherein said second IRS biomarker is selected from a second IRS biomarker panel, wherein said first IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from a K-group IRS biomarker gene and wherein said second IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from a L-group IRS biomarker gene.

Suitably, the kit is for diagnosing the presence or absence of mild sepsis or septic shock, wherein said first IRS biomarker is selected from a first IRS biomarker panel and wherein said second IRS biomarker is selected from a second IRS biomarker panel, wherein said first IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from a group M IRS biomarker gene and wherein said second IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from a group N IRS biomarker gene.

Suitably, the kit is for diagnosing the presence or absence of severe sepsis or septic shock, wherein the first IRS biomarker is selected from a first IRS biomarker panel and wherein the second IRS biomarker is selected from a second IRS biomarker panel, wherein the first IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from a group O IRS biomarker genes and wherein the second IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from a group P IRS biomarker gene.

In another broad form, it is an object of the invention to provide a method for treating, preventing or inhibiting the development of at least one condition selected from inSIRS, ipSIRS or a specific stage of ipSIRS (e.g. mild sepsis, severe sepsis or septic shock) in a subject, the method comprising (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining an indicator using a combination of the plurality of IRS biomarker values, the indicator being at least partially indicative of the presence, absence or extent of the at least one condition, wherein: (i) the at least two IRS biomarkers have a cross-correlation for at least one condition within a cross-correlation range, the cross-correlation range being between ± 0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence, or extent of the at least one condition, the performance threshold being indicative of an interpretation variance of at least 0.3; and (c) administering to the subject an effective amount of an agent that treats or ameliorates symptoms of inSIRS or reverses or inhibits the development of inSIRS based on the indicator indicating the presence of inSIRS or a specific stage of ipSIRS based on the indicator indicating the presence of ipSIRS or a specific stage of ipSIRS, administering to the subject an effective amount of an agent that treats or ameliorates symptoms of ipSIRS or a specific stage of ipSIRS or reverses or inhibits the development of ipSIRS or a specific stage of ipSIRS.

Suitably, the method further comprises: (1) determining a plurality of measured IRS biomarker values, each measured IRS biomarker value being a measured value of an IRS biomarker of the biological subject; and (2) applying a function to at least one of the measured IRS biomarker values to determine at least one derived IRS biomarker value, the at least one derived IRS biomarker value indicating a value of a corresponding derived IRS biomarker.

Typically, the function includes at least one of: (a) multiplying the two IRS biomarker values; (b) dividing the two IRS biomarker values; (c) adding the two IRS biomarker values; (d) subtracting the two IRS biomarker values; (e) a weighted sum of at least two IRS biomarker values; (f) a logarithmic sum of at least two IRS biomarker values; and, (g) a sigmoid function of at least two IRS biomarker values.

In another broad form, it is an object of the invention to provide a method of monitoring the efficacy of a particular treatment regimen in a subject towards a desired health state, the method comprising: a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker on a biological subject following treatment with a treatment regimen; (b) determining an indicator using a combination of the plurality of IRS biomarker values, the indicator at least partially indicating the presence, absence, or extent of the at least one condition selected from a healthy condition, an insiRS, an ipsIRS, or a particular stage of ipsIRS, wherein: (i) at least two IRS biomarkers have a cross-correlation for at least one condition within a cross-correlation range, the cross-correlation range being between ± 0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or extent of the at least one condition or to provide a prognosis of the at least one condition, the performance threshold being indicative of an interpretation variance of at least 0.3; and (c) determining that the treatment regimen is effective to change the health state of the subject to a desired health state based on the indicator indicating the presence of a health condition, or a lesser degree of the presence of a condition in the subject relative to the degree of the condition prior to treatment with the treatment regimen.

In another broad form, it is an object of the invention to provide a method of correlating biomarker signatures to an effective treatment regimen for a condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS (e.g. mild sepsis, severe sepsis and septic shock), the method comprising: (a) determining a biomarker signature defining a combination of at least two IRS biomarker values corresponding to the values of at least two IRS biomarkers measurable to or derivable from a biological subject having the condition and for which effective treatment has been identified, wherein: (i) the at least two IRS biomarkers have a cross-correlation for the condition within a cross-correlation range, the cross-correlation range being between ± 0.9; and (ii) the combination of at least two biomarker values has a performance value greater than or equal to a performance threshold representing the ability of the combination of at least two biomarker values to diagnose the presence, absence or extent of the condition or to provide a prognosis of the condition, the performance threshold being indicative of an interpretation variance of at least 0.3; and (b) associating the biomarker signature so determined with an effective treatment regimen for the condition.

In another broad form, it is an object of the invention to provide a method of determining whether a treatment regimen is effective to treat a subject having a condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS (e.g. mild sepsis, severe sepsis and septic shock), the method comprising: (a) determining a plurality of post-treatment IRS biomarker values, each post-treatment IRS biomarker value indicating a value measured or derived for at least one IRS biomarker on a biological subject following treatment with the treatment regimen; (b) determining a post-treatment indicator using a combination of the plurality of post-treatment IRS biomarker values, the post-treatment indicator at least partially indicative of the presence, absence, or extent of at least one condition selected from a healthy condition, an insiRS, an ipsIRS, or a particular stage of ipsIRS, wherein: (i) the at least two IRS biomarkers have a cross-correlation for at least one condition within a cross-correlation range, the cross-correlation range being between ± 0.9; and (ii) the post-treatment indicator has a performance value greater than or equal to a performance threshold representing the ability of the post-treatment indicator to diagnose the presence, absence, or extent of the at least one condition, the performance threshold indicating an interpretation variance of at least 0.3, wherein the post-treatment indicator indicates whether the treatment regimen is effective to treat the condition in the subject based on: the post-treatment indicator indicates the presence of a healthy condition, or a lesser degree of the presence of the condition relative to the degree of the condition in the subject prior to treatment with the treatment regimen.

In another broad form, it is an object of the invention to provide a method of correlating biomarker signatures with positive or negative responses or side effects to a treatment regimen, the method comprising: (a) determining a biomarker signature defining a combination of at least two IRS biomarker values corresponding to values of at least two IRS biomarkers that can be measured on or derived from a biological subject after initiation of the treatment regimen, wherein: (i) the at least two IRS biomarkers have a cross-correlation with at least one condition selected from the group consisting of a healthy condition, inSIRS, ipSIRS, or a particular stage of ipSIRS (e.g., mild sepsis, severe sepsis, and septic shock) within a cross-correlation range of ± 0.9; and (ii) the combination of at least two biomarker values has a performance value greater than or equal to a performance threshold representing the ability of the combination of at least two biomarker values to diagnose the presence, absence, or degree of the at least one condition or to provide a prognosis of the at least one condition, the performance threshold being indicative of an interpretation variance of at least 0.3; and (b) correlating the biomarker signature so determined with whether a positive or negative response to the treatment regimen.

In another broad form, it is an object of the invention to provide a method of determining a positive or negative response to and/or side effects of a treatment regimen in a subject having a condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS (e.g. mild sepsis, severe sepsis and septic shock), the method comprising: (a) correlating a reference biomarker signature with a positive or negative response or side effect to the treatment regimen, wherein the biomarker signature defines a combination of at least two IRS biomarker values corresponding to the values of at least two IRS biomarkers measured or derived from a control biological subject or group, wherein: (i) the at least two IRS biomarkers have a cross-correlation with at least one condition selected from the group consisting of a healthy condition, inSIRS, ipSIRS, or a particular stage of ipSIRS (e.g., mild sepsis, severe sepsis, and septic shock) within a cross-correlation range of ± 0.9; and (ii) the combination of at least two biomarker values has a performance value greater than or equal to a performance threshold representing the ability of the combination of at least two biomarker values to diagnose the presence, absence, or degree of the at least one condition or to provide a prognosis of the at least one condition, the performance threshold being indicative of an interpretation variance of at least 0.3; (b) determining a sample biomarker signature defining a combination of at least two IRS biomarker values corresponding to values of at least two IRS biomarkers measured to or derived from a biological subject after initiation of the treatment regimen, wherein: (i) the at least two IRS biomarkers have a cross-correlation with respect to at least one condition selected from a healthy condition, inSIRS, ipSIRS or a specific stage of ipSIRS within a cross-correlation range, the cross-correlation range being between ± 0.9; and (ii) the combination of at least two biomarker values has a performance value greater than or equal to a performance threshold representing the ability of the combination of at least two biomarker values to diagnose the presence, absence, or degree of the at least one condition or to provide a prognosis of the at least one condition, the performance threshold being indicative of an interpretation variance of at least 0.3; wherein the sample biomarker signature indicates whether the subject is responding positively or negatively to the treatment regimen and/or whether to develop a side effect from the treatment regimen based on: the reference biomarker identifies a correlation with a positive or negative response to the treatment regimen.

In another broad form, it is an object of the invention to provide a method of determining a positive or negative response to a treatment regimen and/or a side effect to a treatment regimen in a biological subject, the method comprising: (a) determining a sample biomarker signature defining a combination of at least two IRS biomarker values corresponding to values of at least two IRS biomarkers measured to or derived from a biological subject after initiation of the treatment regimen, wherein: (i) the at least two IRS biomarkers have a cross-correlation with at least one condition selected from the group consisting of a healthy condition, inSIRS, ipSIRS, or a particular stage of ipSIRS (e.g., mild sepsis, severe sepsis, and septic shock) within a cross-correlation range of ± 0.9; and (ii) the combination of at least two biomarker values has a performance value greater than or equal to a performance threshold representing the ability of the combination of at least two biomarker values to diagnose the presence, absence or extent of the at least one condition or to provide a prognosis of the at least one condition, the performance threshold being indicative of an interpretation variance of at least 0.3, wherein the sample biomarker signature is associated with a positive or negative response to the treatment regimen and/or a side effect from the treatment regimen; and (b) determining whether the subject is responding positively or negatively to the treatment regimen and/or developing side effects from the treatment regimen based on the sample biomarker signature.

Brief Description of Drawings

Examples of the invention will now be described with reference to the accompanying drawings, in which: -

Fig. 1A is a flow diagram of an example of a method for deriving an indicator for use in diagnosing the presence, absence, or extent of at least one condition in a biological subject or providing a prognosis of at least one condition;

FIG. 1B is a flow chart of an example of a method for identifying biomarkers for use in biomarker identification;

FIG. 2 is a schematic diagram of an example of a distributed computer architecture;

FIG. 3 is a schematic diagram of an example of the processing system of FIG. 2;

FIG. 4 is a schematic diagram of an example of the computer system of FIG. 2;

FIG. 5 is a flow diagram of a specific example of a method for identifying biomarkers for use in biomarker identification;

FIG. 6A is a flow chart of a first example of a method for selecting a candidate biomarker;

FIG. 6B is a flow chart of a second example of a method for selecting candidate biomarkers;

fig. 7 is a flow diagram of a second example of a method for use in diagnosing the presence, absence, or extent of at least one condition in a biological subject or providing a prognosis of at least one condition;

fig. 8A is a graph of 941 mRNA biomarkers versus AUC for distinguishing between health and post-operative inflammation (PS) (also referred to herein as "infection-negative SIRS" (inSIRS)), biomarker alone having an AUC greater than 0.7;

FIG. 8B is a Box and whisker plot (Box and Whisker spots) showing the best mRNA biomarkers for separating health status and PS;

fig. 8C is a graph of AUC for the diagnostic ability of 1000 derived biomarkers in isolated health and PS, all derived biomarkers having an AUC of 1.0;

FIG. 8D is a box and whisker plot of derived biomarkers for separating optimal performance of health and PS based on AUC;

FIGS. 8E and 8F are two graphs showing that the biomarkers in each group are correlated with each other;

FIGS. 8G and 8H are two graphs showing the AUC of biomarkers in each group (group 1 and group 2);

FIG. 8I is a boxand whisker plot showing the greater overall AUC obtained when biomarkers were derived from groups 1 and 2;

figure 9A is a graph of the AUC of 941 mRNA biomarkers for distinguishing between health and sepsis (also referred to herein as "infection positive SIRS" (ipSIRS)), all biomarkers alone having an AUC greater than 0.7;

FIG. 9B is a box and whisker plot showing optimal mRNA biomarkers for isolating health and sepsis;

fig. 9C is a graph of AUC for the diagnostic ability of 1000 derived biomarkers in isolating health and sepsis, all derived biomarkers having an AUC of 1.0;

Fig. 9D is a box and whisker plot of derived biomarkers for separating optimal performance of health and sepsis based on AUC;

FIGS. 9E and 9F show the correlation of biomarker panels with each other, with each panel correlated with each other;

FIGS. 9G and 9H are two graphs showing the AUC of biomarkers in each group (buckettgroups 1 and bucket 2);

FIG. 9I is a boxed and whisker plot showing the greater overall AUC obtained when biomarkers were derived from bucket 1 and bucket 2 groups;

fig. 10A is a graph of 359 mRNA biomarkers versus AUC for distinguishing PS from sepsis, all biomarkers alone having an AUC greater than 0.7;

FIG. 10B is a box and whisker plot showing the best mRNA biomarkers for separating PS and sepsis;

fig. 10C is a graph of AUC for the diagnostic ability of 1000 derived biomarkers in separating PS and sepsis, all derived biomarkers having an AUC of 0.9;

FIG. 10D is a box and whisker plot of best derived biomarkers for separating PS and sepsis;

FIG. 10E is a graph showing the correlation of biomarkers to conditions in each bucket group;

FIG. 10F is a boxed and whisker plot showing the greater overall AUC obtained when biomarkers were derived from bucket 1 and bucket 2 groups;

fig. 10G is a graph showing AUC of biomarkers in each of four bucket groups;

FIG. 10H is a boxed and whisker plot showing the greater overall AUC obtained when biomarkers were derived from each of the four bucket groups;

fig. 11A is a graph of 66 mRNA biomarkers versus AUC for distinguishing between mild sepsis and severe sepsis, all individual biomarkers selected having an AUC greater than 0.7;

FIG. 11B is a box and whisker plot showing the best mRNA biomarkers for isolating mild sepsis and severe sepsis;

fig. 11C is a graph of AUC for the diagnostic ability of 1000 derived biomarkers in isolating mild sepsis and severe sepsis, all derived biomarkers having an AUC of at least 0.87;

fig. 11D is a boxand whisker plot of the derived biomarkers for separating the best performance of mild sepsis and severe sepsis based on AUC;

FIGS. 11E and 11F are graphs showing the correlation of biomarkers in each group with each other;

fig. 11G and 11H are graphs showing AUC for biomarkers in each group;

FIG. 11I is a boxand whisker plot showing the greater overall AUC obtained when biomarkers were derived from groups 1 and 2;

figure 12A is a graph of the AUC for 48 mRNA biomarkers versus AUC for distinguishing between mild sepsis and septic shock (also referred to herein as "infection-positive SIRS-shock"), all biomarkers alone having an AUC greater than 0.7;

FIG. 12B is a box and whisker plot showing the best mRNA biomarkers for isolating mild sepsis and septic shock;

figure 12C is a graph of AUC for the diagnostic ability of 1000 derived biomarkers in isolating mild sepsis and septic shock, all derived biomarkers having an AUC of at least 0.793;

FIG. 12D is a boxand whiskers plot of the derived biomarkers for separating the best performance of mild sepsis and septic shock based on AUC;

FIGS. 12E and 12F are graphs showing that the biomarkers in each group correlate with each other;

fig. 12G and 12H are graphs showing AUC for biomarkers in each group;

FIG. 12I is a boxand whisker plot showing the greater overall AUC obtained when biomarkers were derived from groups 1 and 2;

FIG. 13A is a graph of 61 mRNA biomarkers versus AUC for distinguishing severe sepsis from septic shock, all biomarkers selected alone having an AUC greater than 0.7;

FIG. 13B is a box and whisker plot showing the best mRNA biomarkers for isolating severe sepsis and septic shock;

fig. 13C is a graph of AUC for the diagnostic ability of 1000 derived biomarkers, all derived biomarkers having an AUC of at least 0.821, in isolating severe sepsis and septic shock;

Fig. 13D is a box and whisker plot of the derived biomarkers for separating the best performance of mild sepsis and septic shock based on AUC;

FIGS. 13E and 13F are graphs showing the correlation of biomarkers in each group with each other;

fig. 13G and 13H are graphs showing AUC for biomarkers in each group;

FIG. 13I is a boxand whisker plot showing the greater overall AUC obtained when biomarkers were derived from groups 1 and 2;

FIG. 14 is a user interface showing an example of a thermal cycler protocol;

FIG. 15 is an illustration of an example of a report;

FIGS. 16A to 16L are whisker plots of the first twelve biomarker combinations used to differentiate health status from PS;

FIGS. 17A to 17L are whisker plots of the first twelve biomarker combinations used to differentiate between health and sepsis;

FIGS. 18A to 18L are box and whisker plots of the first twelve biomarker combinations used to differentiate PS from sepsis;

FIGS. 19A to 19L are boxand whisker plots of the first twelve biomarker combinations used to differentiate sepsis from severe sepsis;

FIGS. 20A to 20L are box and whisker plots of the first twelve biomarker combinations used to differentiate severe sepsis from septic shock;

FIGS. 21A to 21L are box and whisker plots of the first twelve biomarker combinations used to differentiate sepsis from septic shock;

FIG. 22 is a graph of the effect on AUC of adding a biomarker to a biomarker signature;

figure 23 is a graph showing an example of biomarker identification as an ability of two patient populations to distinguish between PS and sepsis;

FIG. 24 is a flow chart of an example of a method for determining an indicator reference;

FIGS. 25A and 25B are flow diagrams of examples of methods for verifying an indicator derived from a biomarker measurement;

FIG. 26 is an example showing a comparison of indicator values to indicator references; and the number of the first and second groups,

fig. 27A and 27B are exemplary representations of pointer values.

Detailed description of the preferred embodiments

Examples of methods for determining an indicator for use in diagnosing the presence, absence or extent of at least one condition in a biological subject or diagnosing the presence, absence or extent of at least one condition in a biological subject, or in monitoring the progression of at least one condition in a subject or monitoring the progression of at least one condition in a subject, or in prognosing at least one condition in a subject or prognosing at least one condition in a subject will now be described with reference to fig. 1A.

For purposes of explanation, a number of different terms will be used. For example, the term "biomarker" refers to a measurable parameter, or combination of parameters, that can be used as an indicator of a biological state and includes, but is not limited to, proteins, nucleic acids, carbohydrates, lipids, metabolites, gases, steroids, ions, nutrients, toxins, cells, pathogenic organisms, non-pathogenic organisms, organic compounds, and inorganic compounds. Biomarkers also include non-blood-derived (non-blood-borne) factors, non-analyte physiological markers of health status, or other factors or biomarkers not measured from a sample (e.g., a biological sample such as a bodily fluid), such as "clinical" parameters or "phenotypic" parameters, including, but not limited to, age, race, gender, species, breed, genetic information, white blood cell count, diastolic and systolic blood pressure, bone density, height, weight, waist and hip circumference, body mass index, and other parameters such as type I or type II diabetes or gestational diabetes (collectively referred to herein as diabetes), resting heart rate (resting heart rate), assessment of steady state pattern (HOMA), HOMA insulin resistance (HOMA-IR), venous glucose resistance (si (ivgt), resting heart rate (resting heart rate), beta cell function, Macrovascular function, microvascular function, atherogenic index, low density lipoprotein/high density lipoprotein ratio, intima-media thickness, body temperature, Sequential Organ Failure Assessment (SOFA), etc. "biomarkers" may also include "immune response biomarkers" which will be described in more detail below.

The term "biomarker value" refers to a value measured or derived for at least one corresponding biomarker of a biological subject and typically at least partially indicative of the concentration of an immune system biomarker in a sample taken from the subject. Thus, a biomarker value may be a measured biomarker value, which is a value of a biomarker measured on a subject, or alternatively may be a derived biomarker value, which is a value that has been derived from one or more measured biomarker values, for example, by applying a function to one or more measured biomarker values.

The biomarker value may be in any suitable form depending on the manner in which the value is determined. For example, biomarker values may be determined using high throughput techniques such as mass spectrometry, sequencing platforms, array and hybridization platforms, immunoassays, flow cytometry, or any combination of such techniques, and in one preferred example, biomarker values relate to the level of activity or abundance of an expression product or other measurable molecule that is quantified using techniques such as PCR, sequencing, and the like. In this case, the biomarker value may be in the form of an amplification amount or cycle time, which is a logarithmic representation of the concentration of the biomarker within the sample, as will be understood by those skilled in the art and as will be described in more detail below.

The term "reference biomarker" is used to refer to a biomarker for which activity has been quantified for a sample population of one or more individuals having one or more conditions, a stage of one or more conditions, a subtype of one or more conditions, or a different prognosis. The term "reference data" refers to data measured for one or more individuals in a sample population, and may include a quantification of the level or activity of a biomarker measured for each individual, information about any condition of the individual, and optionally any other information of interest.

The term "candidate biomarker" refers to a subset of reference biomarkers that have been identified as potentially useful for distinguishing between different groups of individuals, such as individuals having different conditions or having different stages or prognoses. The number of candidate biomarkers will vary, but is typically about 200.

The term "marker biomarker" is used to refer to a subset of candidate biomarkers that have been identified for use in biomarker identification that can be used for clinical assessment, such as scoring (rule in) or excluding a particular condition, different stages or severity of a condition, subtypes of different conditions, or different prognosis. The number of identifying biomarkers will vary, but is typically on the order of 10 or less.

The term "biomarker signature" means a combination of at least two biomarker values corresponding to values of a biomarker measurable to or derivable from one or more biological subjects that is characteristic of an independent condition, stage of a condition, subtype of a condition, or prognosis for an independent condition, stage of a condition, subtype of a condition.

The terms "biological subject," "individual," and "patient" are used interchangeably herein to refer to an animal subject, particularly a vertebrate subject, and even more particularly a mammalian subject. Suitable vertebrates falling within the scope of the present invention include, but are not limited to, Chordata (chord), any member of the vertebrate subgenus (vertebrata), including primates, rodents (e.g., mice, rats, guinea pigs), lagomorphs (e.g., rabbits, hares), bovines (e.g., cows), ovine-like (ovines) (e.g., sheep), such as caprines (caprines) (e.g., goats), porcine-like (porcines) (e.g., pigs), equine-like (equines) (e.g., horses), canines (e.g., dogs), felines (e.g., cats), birds (e.g., chickens, turkeys, ducks, geese, companion birds, such as canaries, budgerigars, etc.), marine mammals (e.g., dolphins, whales), reptiles (snakes, frogs, lizards, etc.), and fish. Preferred subjects are primates (e.g., humans, apes, monkeys, chimpanzees).

As used herein, the term SIRS ("systemic inflammatory response syndrome") refers to a clinical response resulting from nonspecific injury with two or more of the following measurable clinical characteristics; body temperature of more than 38 deg.C or less than 36 deg.C, heart rate of more than 90 beats per minute, respiration rate of more than 20 beats per minute, and white blood cell count (total white blood cells) of more than 12,000/mm³Or less than 4,000/mm³Or band neutrophilic granulocytes (band neutrophilic) percentage greater than 10%. From an immunological perspective, it can be considered to represent a systemic response to injury (e.g., major surgery) or systemic inflammation. As used herein, "inSIRS" (which includes within its scope "post-operative" (PS) inflammation) includes the clinical responses mentioned above, but lacks a systemic infectious process. In contrast, "ipSIRS" (also referred to herein as "sepsis") includes the clinical response mentioned above, but there is a presumed or confirmed infection. Confirmation of infection may be determined using microbial culture or isolation of infectious agents. From exempting fromFrom a clinical perspective, ipSIRS can be regarded as a systemic response to a microorganism regardless of whether it is a local, peripheral or systemic infection.

As used herein, the term "degree" of a condition refers to the severity, stage, or state of the condition. For example, the condition may be characterized as mild, moderate, or severe. One skilled in the art will be able to determine or assess the extent of a particular condition. For example, the degree of the condition can be determined by comparing the likelihood or length of survival of a subject having the condition to the likelihood or length of survival in other subjects having the same condition. In other embodiments, the degree of the condition can be determined by comparing the degree of the clinical sign in a subject having the condition to the clinical sign in other subjects having the same condition.

It is to be understood that the terminology and the associated definitions described above are used for the purpose of explanation only and are not intended to be limiting.

In this example, the method comprises determining a plurality of biomarker values at step 100, each biomarker value being indicative of a value measured or derived for at least one biomarker of the biological subject.

The biomarker values and the biomarkers corresponding to the biomarker values may be in any suitable form, and in particular may relate to any property of the subject for which a quantitative value may be determined. The technology is particularly suited for high throughput techniques such as mass spectrometry, sequencing platforms, arrays and hybridization platforms, or any combination of such techniques, and in a preferred example, biomarker values relate to the level of activity or abundance of an expression product or other measurable molecule.

The biomarker value may be a measured biomarker value, which is a value of a biomarker measured on the subject, or alternatively may be a derived biomarker value, which is a value that has been derived from one or more measured biomarker values, for example, by applying a function to one or more measured biomarker values. As used herein, a biomarker to which a function has been applied is referred to as a "derived marker".

The biomarker values may be determined in any of a number of ways. In one example, the process of determining a biomarker value may comprise measuring the biomarker value, for example, by performing a test on the biological subject. More generally, however, the step of determining the biomarker value comprises causing the electronic processing device to receive or otherwise obtain a previously measured or derived biomarker value. This may include, for example, retrieving the biomarker values from a data store, such as a remote database, obtaining biomarker values that have been manually entered using an input device, or the like.

At step 100, an indicator is determined using a combination of a plurality of biomarker values, the indicator being at least partially indicative of the presence, absence, extent or prognosis of the at least one condition.

The biomarker values may be combined in any of a number of ways, and this may include, for example, adding, multiplying, subtracting, or dividing the biomarker values to determine the indicator value. This step is performed such that multiple biomarker values can be combined into a single indicator value, providing a more useful and direct mechanism for allowing interpretation of the indicator and thus use in diagnosing the presence, absence or extent of at least one condition in a subject, or in prognosing at least one condition in a subject.

Assuming the method is performed using an electronic processing device, an indication of the indicator is optionally displayed or otherwise provided to the user at step 120. In this regard, the indication may be a graphical or alphanumeric representation of the indicator value. However, the indication may alternatively be the result of a comparison of the indicator value with a predefined threshold or range, or alternatively may be an indication of the presence, absence, extent or prognosis of the at least one condition derived using the indicator.

To ensure that an effective diagnosis or prognosis can be determined, the at least two biomarkers have a cross-correlation for at least one condition within a cross-correlation range, the cross-correlation range being between ± 0.9. This requirement means that the two biomarkers are not fully correlated with respect to each other when considered in the context of the condition being diagnosed or prognosticated. In other words, at least two of the biomarkers in the combination respond differentially as the condition changes, which significantly increases their ability to distinguish between the at least two conditions, diagnose the presence, absence, or extent of the at least one condition, and/or provide a prognosis of at least the condition in the biological subject or a prognosis of the at least one condition of the biological subject when combined. As used herein, "and/or" refers to and includes any and all possible combinations of one or more of the associated listed items, as well as the lack of combinations when interpreted in the alternative (or).

In general, the requirement that a biomarker have low cross-correlation means that the biomarker may be involved in different biological properties or domains such as, but not limited to, different molecular functions, different biological processes, and different cellular components. Illustrative examples of molecular functions include adding or removing one or more of the following moieties to or from a protein, polypeptide, peptide, nucleic acid (e.g., DNA, RNA): straight, branched, saturated, or unsaturated hydrocarbon radicals (e.g. C)₁-C₂₄A hydrocarbyl group); phosphoric acid; ubiquitin; an acyl group; fatty acids, lipids, phospholipids; a nucleotide base; hydroxyl groups, and the like. Molecular functions also include signaling pathways, including, but not limited to, receptor signaling pathways and nuclear signaling pathways. Non-limiting examples of molecular functions also include cleavage of a nucleic acid, peptide, polypeptide, or protein at one or more sites; polymerization of nucleic acids, peptides, polypeptides, or proteins; translocating through a cell membrane (e.g., an outer cell membrane; nuclear membrane); translocation into or out of organelles (e.g., golgi, lysosomes, endoplasmic reticulum, nucleus, mitochondria); receptor binding, receptor signaling, membrane channel binding, membrane channel influx or efflux, etc.

Illustrative examples of biological processes include: stages of the cell cycle such as meiosis, mitosis, cell division, prophase, metaphase, anaphase, telase and interphase, stages of cell differentiation; apoptosis of the cell; necrosis; chemotaxis; immune responses include adaptive and innate immune responses, pro-inflammatory immune responses, autoimmune responses, tolerogenic responses, and the like. Other illustrative examples of biological processes include the production or breakdown of Adenosine Triphosphate (ATP), sugars, polysaccharides, fatty acids, lipids, phospholipids, sphingolipids, glycolipids, cholesterol, nucleotides, nucleic acids, membranes (e.g., cytoplasmic membranes, nuclear membranes), amino acids, peptides, polypeptides, proteins, and the like.

Representative examples of cellular components include organelles, membranes, and others as indicated, for example, above.

It will be appreciated that the use of biomarkers having different biological properties or domains provides even further information than if the biomarkers relate to the same or a common biological property or domain.

In this regard, it is understood that if at least two biomarkers are highly correlated with each other, the use of both biomarkers will hardly increase the diagnostic/prognostic improvement compared to the use of a single one of the biomarkers. Thus, the method uses biomarkers that are poorly correlated with each other, thereby ensuring that the inclusion of each biomarker in the method significantly increases the discriminatory power of the indicator.

Nevertheless, to ensure that the indicator can be accurately used to make a prognosis that distinguishes between at least two conditions or diagnoses the presence, absence or extent of at least one condition or at least one condition, the indicator has a performance value that is greater than or equal to a performance threshold. The performance threshold may be of any suitable form, but will typically indicate an interpretation variance of at least 0.3, or an equivalent of another performance metric.

It has been found that with a combination of biomarkers having a cross-correlation between ± 0.9 and using these in a combination that provides an interpretation variance of at least 0.3, this allows for the definition of an indicator suitable to ensure accurate discrimination, a diagnosis or prognosis can be obtained while minimizing the number of required biomarkers.

It should be understood that in this context, a biomarker used within the above-described methods may define a biomarker signature for at least one condition, including a minimum number of biomarkers, while maintaining sufficient performance to allow the biomarker signature to be used to make clinically relevant diagnoses, prognoses, or differentiations. Minimizing the number of biomarkers used minimizes the costs associated with performing diagnostic or prognostic tests and, in the case of nucleic acid expression products, allows the tests to be performed using relatively straightforward techniques such as nucleic acid arrays and Polymerase Chain Reaction (PCR) processes, etc., allowing the tests to be performed rapidly in a clinical setting.

Furthermore, generating a single indicator value allows the results of the test to be easily interpreted by a clinician or other medical practitioner so that the test can be used for reliable diagnosis in a clinical setting.

An example of a process for generating suitable biomarker signatures for use in the method of fig. 1A will now be described with reference to fig. 1B.

In particular, biomarker signatures are typically generated by analyzing a large number of biomarkers and then selecting combinations of biomarkers that meet the criteria described above.

In this example, at step 150, the process includes ranking the plurality of candidate biomarkers according to each biomarker's ability to distinguish the presence, absence, degree, or prognosis of at least one condition in the biological subject.

Candidate biomarkers may be obtained in any suitable manner, but typically this will include obtaining reference data comprising reference biomarker values associated with a number of reference biomarkers that have been measured or derived for one or more reference individuals having different presence, absence, degree, or prognosis of one or more conditions of interest. Thus, it should be understood that candidate biomarkers may include measured and/or derived biomarkers, as will be described in more detail below.

The reference data typically includes measurements of a plurality of reference biomarkers that include information about the activity, such as level, abundance, or functional activity, of any expression product or measurable molecule, as will be described in more detail below. The reference data may also include information about one or more conditions that each individual has, such as clinical data. This may include information about the presence, absence, extent or progression of the condition, phenotypic information, such as details of phenotypic traits, genetic information or information of genetic regulation, amino acid or nucleotide related genomic information, results of other tests including imaging, biochemical assays and hematological assays, other physiological scores such as SOFA (sequential inertial organ failure estimation) scores, and the like, and this is not intended to be limiting, as will be apparent from the description below.

Depending on the preferred implementation, the candidate biomarkers may include some or all of the reference biomarkers. Thus, for example, a reference biomarker value may be analyzed to determine a correlation between the reference biomarker and at least one condition, where the reference biomarker is coarsely filtered to remove those with low correlations, such as those with correlations with conditions below 0.3.

At step 160, at least two candidate biomarkers are selected based on the ranking and cross-correlation. In particular, at least two candidate biomarkers having a cross-correlation within a cross-correlation range of ± 0.9 are selected. Thus, when considered in the context of one or more conditions, this process excludes highly interrelated, and it would therefore not significantly increase any biomarker of the ability to distinguish the presence, absence, degree, or prognosis of at least one condition.

At step 170, performance values for the selected combination of candidate biomarkers are determined. As mentioned above, the combination may be any combination of candidate biomarker values, such as addition, subtraction, multiplication or division of candidate biomarker values, and therefore this will not be described in further detail.

At step 180, it is determined whether the combined performance value exceeds a performance threshold equal to an interpretation variance of at least 0.3. If so, a combination of candidate biomarkers can be used to define the biomarker signature. Otherwise, the preceding steps may be repeated, for example by determining a selectable combination of candidate biomarkers, selecting a different candidate biomarker, or adding additional candidate biomarkers, as will be described in more detail below. In this regard, it should be understood that other measures may be used, and that reference to an explained variance of at least 0.3 is intended to be a specific example for illustrative purposes.

Thus, the methods described above may be used to select a combination of candidate biomarkers suitable for use as marker biomarkers in biomarker identification for diagnosing the presence, absence, extent or prognosis of at least one condition in a biological subject, e.g., using the method of fig. 1A above.

In one example, this is achieved by ensuring that at least two of the biomarkers used are not highly cross-correlated, thereby ensuring that each of these biomarkers contributes to the performance of the resulting identification.

A number of further features will now be described.

In one example, the method includes determining a plurality of measured biomarker values, each measured biomarker value being a measured value of a corresponding biomarker for the biological subject, and determining at least one derived biomarker value indicative of a value of the corresponding derived biomarker as a function of at least one application of the measured biomarker values.

The function used will therefore vary depending on the preferred implementation. In one example, the function includes at least one of: multiplying the two biomarker values; dividing the two biomarker values; adding the two biomarker values; subtracting the two biomarker values; a weighted sum of at least two biomarker values; a logarithmic sum of at least two biomarker values; and, a sigmoid function of the at least two biomarker values.

More specifically, the function is a division of two biomarker values such that the derived biomarker value corresponds to a ratio of the two measured biomarker values. There are many reasons why this ratio may be preferred. For example, the use of ratios is self-normalizing, meaning that deviations in the measurement technique will automatically be adjusted. For example, if the input concentration of the sample is doubled, the relative proportion of biomarkers will remain unchanged. As a result, the type of function therefore has a stable profile (profile) for a range of input concentrations, which is important because input concentrations are known variables of expression data. In addition, many biomarkers are nodes on biochemical pathways, so the ratio of biomarkers gives information about the relative activation of one biological pathway to another, which is a natural representation of biological changes within the system. Finally, ratios are generally easy to interpret.

The method generally includes combining at least two biomarker values to determine an indicator value representative of an indicator. This is typically achieved by combining at least two biomarker values using a combination function such as: an additive model; a linear model; a support vector machine; a neural network model; a random forest model; a regression model; a genetic algorithm; an annealing algorithm; and a weighted sum.

In one example, at least one of the at least two biomarkers is a derived biomarker, and in a preferred example, the combining function is an addition of derived biomarker values that are ratios, in which case the method comprises determining a first derived biomarker value and a second derived biomarker value from the ratio of the first measured biomarker value and the second measured biomarker value and the third measured biomarker value and the fourth measured biomarker value, and then adding the first derived biomarker value and the second derived biomarker value to generate the indicator value.

In one example, the method includes determining an indicator value, comparing the indicator value to at least one indicator value range, and using the result of the comparison in diagnosing the presence, absence, degree, or prognosis of at least one condition.

The processes described above are typically performed using an electronic processing device that forms part of a processing system, such as a computer system or the like. In such a case, the method generally includes causing the electronic processing device to receive a plurality of measured biomarker values, apply a function to at least one of the measured biomarker values to determine at least one derived biomarker value, and combine the at least one derived biomarker value with at least one other biomarker value to determine an indicator value.

The electronic processing device may then generate a representation from the at least one indicator value, for example by presenting a numerical indication of the indicator value. More generally, however, the electronic processing device compares the indicator value with at least one indicator value range and presents the result of the comparison. This can be used to compare the indicator with a defined range representing a particular stage, progression or prognosis of one or more conditions, allowing an indication of the respective stage, progression or prognosis to be exhibited.

The cross-correlation range is typically at least one of: plus or minus 0.8; plus or minus 0.7; plus or minus 0.6; plus or minus 0.5; plus or minus 0.4; plus or minus 0.3; plus or minus 0.2; and ± 0.1. In this regard, it will be appreciated that the smaller the cross-correlation range used, the less correlated the biomarkers will be, and thus these will be more useful in distinguishing between particular stages, progression or prognosis of one or more conditions.

Typically, biomarkers having at least minimal correlation with condition are also used. In one example, each biomarker has a condition correlation with the presence, absence, degree, or prognosis of at least one condition that is outside of a condition correlation range, the condition correlation range being between ± 0.3. However, it will be appreciated that the larger the range, the greater the correlation between the biomarker and the condition, and thus the more useful this will be in making a diagnosis. Thus, the condition correlation is more typically one of: plus or minus 0.9; plus or minus 0.8; plus or minus 0.7; plus or minus 0.6; plus or minus 0.5; and ± 0.4.

Furthermore, it will be appreciated that the greater the performance of the indicator, the greater the use of the indicator in making a diagnosis. Thus, the performance threshold generally indicates an interpretation variance of at least one of: 0.4; 0.5; 0.6; 0.7; 0.8; and 0.9.

As described above, the biomarker values may be in any suitable form. However, the technique is particularly suitable for biomarker values indicative of the level or abundance of molecules selected from one or more of: a nucleic acid molecule; a proteinaceous molecule; an amino acid; a carbohydrate; a lipid; a steroid; an inorganic molecule; ions; a drug; a chemical; a metabolite; a toxin; a nutrient; a gas; a cell; a pathogenic organism; and non-pathogenic organisms.

When determining biomarkers for use in biomarker identification, the method generally comprises selecting a combination function, determining whether a performance value for a combination of at least two candidate biomarkers determined by the combination function is greater than or equal to a performance threshold, and if the performance value is not greater than or equal to the performance threshold, repeating these steps for successive different combination functions. This therefore allows many different combining functions to be tried in succession, allowing the best combining function to be identified.

The method also typically includes selecting two candidate biomarkers, determining whether a performance value of the combination of the two candidate biomarkers is greater than or equal to a performance threshold, and if not, combining the selected candidate biomarker with at least one additional candidate biomarker before repeating the steps with the at least one additional candidate biomarker. This allows the use of a large number of biomarkers in situations where both biomarkers are insufficient, and with an increasing number of candidate biomarkers used in combination, this may be repeated and compared to a performance threshold until the required performance is reached, or until a defined number limit of candidate biomarkers is reached.

To ensure that the required cross-correlation is met when selecting a candidate biomarker, the method typically comprises selecting the highest and next highest ranked candidate biomarker, for the selected candidate biomarker, determining whether the cross-correlation of the candidate biomarker is within a cross-correlation range, and if not, repeating these steps until two candidate biomarkers having a cross-correlation within the cross-correlation range are selected. Alternatively, however, this may be achieved by defining at least two groups of candidate biomarkers, the candidate biomarkers in different groups having a cross-correlation within a range of cross-correlations, ranking the candidate biomarkers in each group, and selecting the candidate biomarkers from the different groups.

Typically, candidate biomarkers are determined by: reference data for at least one individual is used to define a plurality of sets indicative of the presence, absence, degree or prognosis of at least one condition, and at least one analytical technique is then used to identify a plurality of candidate biomarkers potentially useful for distinguishing between the sets. These sets may also be used to establish at least two ranges of reference biomarker values to determine a range of indicator values for the set.

In one example, the reference value measured for the reference biomarker for at least one individual may then be used to identify the candidate biomarker, for example by filtering the reference biomarker based on the correlation of each biomarker with the condition to determine the candidate biomarker.

In one example, the process is performed by one or more processing systems operating as part of a distributed architecture, an example implementation of which will be described with reference to FIG. 2.

In this example, the arrangement includes a number of processing systems 201 and computer systems 203 interconnected via one or more communication networks, such as the Internet 202 and/or a number of Local Area Networks (LANs) 204. It should be understood that the configuration of the networks 202, 204 is for purposes of example only, and in practice the processing and computer systems 201, 203 may communicate via any suitable mechanism, such as via wired or wireless connections, including, but not limited to, mobile networks, private networks, such as 802.11 networks, the internet, LANs, WANs, and the like, as well as via direct or point-to-point connections, such as bluetooth and the like.

The use of the separate terms "processing system" and "computer system" is for illustrative purposes and enables the distinction of different devices, optionally devices having different functions. For example, the processing and computer systems 201, 203 may represent servers and clients, respectively, as will become apparent from the following description. However, this is not intended to be limiting, and in practice any suitable computer network architecture may be used.

An example of a suitable processing system 201 is shown in fig. 3. In this example, the processing system 201 includes electronic processing devices, such as at least one microprocessor 300, memory 301, optional input/output devices 302, such as a keyboard and/or display, and an external interface 303, interconnected via a bus 304 as shown. In this example, the external interface 303 may be used to connect the processing system 201 to peripheral devices, such as communication networks 202, 204, databases 211, other storage devices, and the like. Although a single external interface 303 is shown, this is for example purposes only, and in practice multiple interfaces using multiple methods (e.g., ethernet, serial, USB, wireless, etc.) may be provided.

In use, the microprocessor 300 executes instructions in the form of application software stored in the memory 301 to perform the required processes, such as communicating with other processing systems or computer systems 201, 203. Thus, actions by the processing system 201 are performed by the processor 300 in accordance with instructions stored as application software in the memory 301 and/or input commands received via the I/O device 302, or commands received from other processing systems or computer systems 201, 203. The application software may include one or more software modules and may be executed in a suitable execution environment, such as an operating system environment or the like.

Accordingly, it should be appreciated that the processing system 201 may be formed by any suitable processing system, such as a suitably programmed computer system, PC, web server, or the like. In one particular example, the processing system 201 is a standard processing system, such as a processing system based on a 32-bit or 64-bit Intel architecture, that executes software applications stored on non-volatile (e.g., hard disk) storage, although this is not essential. However, it should also be understood that the processing system may be or include any electronic processing device, such as a microprocessor, microchip processor, logic gate configuration, firmware optionally associated with execution logic such as an FPGA (field programmable gate array), or any other electronic device, system, or arrangement.

As shown in fig. 4, in one example, computer system 203 includes electronic processing devices such as at least one microprocessor 400, memory 401, input/output devices 402 such as a keyboard and/or display, and external interfaces 403, interconnected as shown via a bus 404. In this example, external interface 403 may be used to connect computer system 203 to peripheral devices, such as communication networks 202, 204, databases, other storage devices, and the like. Although a single external interface 403 is shown, this is for example purposes only, and in practice multiple interfaces using multiple methods (e.g., ethernet, serial, USB, wireless, etc.) may be provided.

In use, the microprocessor 400 executes instructions in the form of application software stored in the memory 401 to perform the required processes, for example, to allow communication with other processing or computer systems 201, 203. Thus, actions by the processing system 203 are performed by the processor 400 in accordance with instructions stored as application software in the memory 401 and/or input commands received from a user via the I/O device 402. The application software may include one or more software modules and may be executed in a suitable execution environment, such as an operating system environment or the like.

It should therefore be appreciated that the computer system 203 may be formed by any suitable processing system, such as a suitably programmed PC, internet terminal, laptop, handheld PC, smart phone, PDA, tablet, etc. Thus, in one example, processing system 300 is a standard processing system, such as a processing system based on a 32-bit or 64-bit Intel architecture, that executes software applications stored on non-volatile (e.g., hard disk) storage, although this is not essential. However, it should also be understood that the processing system 203 may be any electronic processing device, such as a microprocessor, microchip processor, logic gate configuration, firmware optionally associated with executing logic such as an FPGA (field programmable gate array), or any other electronic device, system, or arrangement.

It should also be noted that although the processing and computer systems 201, 203 are shown as a single entity, it should be understood that this is not essential, and that one or more of the processing and/or computer systems 201, 203 may be distributed in geographically separate locations, for example, by using a processing system provided as part of a cloud-based environment.

Examples of the above described methods will now be described in further detail. For the purposes of these examples, it is assumed that the process is performed by one or more of the processing systems 201 acting as diagnostic servers. The interaction with the user is via the user computer system 203, which is used to allow the user to submit raw data, e.g., obtained from measurements on the subject, with the processing system 201 generating an indicator, allowing this to be presented on the computer system 203.

It should be understood, however, that the above-described configuration, assumed for the purposes of the following example, is not essential, and that many other configurations may be used. It should also be understood that the distribution of functionality between the processing system and the computer systems 201, 203 may vary depending on the particular implementation.

An example of a specific method for identifying biomarkers for identification using biomarkers will now be described with reference to fig. 5.

In this example, reference data is obtained from at least one individual at step 500. The reference data is typically in the form of measured biomarker values obtained for different phases of at least one individual for at least one condition.

Reference data may be obtained in any suitable manner, but typically this includes obtaining gene expression product data from a plurality of individuals selected to include individuals diagnosed as having one or more conditions of interest, as well as healthy individuals. Detection of any type of gene expression using any of the methods described herein is encompassed by the present invention. The term "expression" or "gene expression" refers to either RNA production alone or RNA production and RNA translation into a protein or polypeptide. Thus, the term "expression product" includes (i) polynucleotides comprising an RNA transcript and a corresponding nucleic acid (comprising a complementary cDNA copy of the RNA transcript), and (ii) polypeptides encoded by the RNA transcript. The condition is typically a medical, veterinary or other health state condition and may include any disease, stage of disease, subtype of disease, severity of disease, disease of varying prognosis, etc. The terms "healthy individual", "healthy subject" and the like are used herein to refer to a subject, particularly a mammal, that does not have a disease, disorder, weakness or ailment that has been diagnosed. The condition of such an individual or subject is referred to herein as a "health condition," such that in one example, the condition can include health. In particular embodiments, a healthy subject lacks SIRS (e.g., inSIRS or ipSIRS).

To accomplish this, gene expression product data is collected, for example, by obtaining a biological sample, such as a peripheral blood sample, and then performing a quantitative process, such as a nucleic acid amplification process, including PCR (polymerase chain reaction) or the like, to assess the activity, and in particular, the level or abundance, of a number of reference biomarkers. The quantitative values indicative of relative activity are then stored as part of the reference data.

Exemplary reference biomarkers can include expression products such as nucleic acids or proteinaceous molecules, as well as other molecules that are relevant in conducting clinical assessments. The number of biomarkers measured for use as reference biomarkers will vary depending on the preferred implementation, but typically includes a large number such as 1000, 5000, 10000 or more, although this is not intended to be limiting.

The individual also typically undergoes a clinical assessment that allows clinical identification of any condition, and any assessment or indication of a condition forms part of the reference data. Although any condition can be assessed, in one example, The process is particularly applicable to identifying conditions such as SIRS (Systemic inflammatory response Syndrome) (M S Range-Frausto, D Pittt, M Costigan, T Hwang, CS Davis, and R P Wenzel, "The Natural History of The Systematic Inflammatory Response Synthesis (SIRS). a Selective study," JAMA: The Journal of The American Medical Association273, No.2(January11,1995): 117-. (M S Range-Frausto, D Pittt, M Costin, T Hwang, C S Davis, and R P Wenzel, "The Natural History of The Systematic Informational Response Syndrome (SIRS). a.Proctive study", JAMA: The Journal of The American Medical Association273, No.2(January11,1995): 117. 123.) SIRS is an excessive Systemic reaction that can have infectious or non-infectious etiology, while sepsis is SIRS that occurs during infection. Both are defined by changes in a number of non-specific host response parameters including heart and respiratory rate, body temperature, and white blood cell count (Mitchell M Levy et al, "2001 SCCM/ESICM/ACCP/ATS/SIS International separations Definitions Conference," Critical Care Medicine 31, No.4(April 2003): 1250-. (Mitchell M Levy et al, "2001 SCCM/ESICM/ACCP/ATS/SIS International Sepsis Definitions Conference", Critical Caremedicine 31, No.4(April 2003): 1250-jar 1256.; K Reinhart, M Bauer, N C Riedemann, and S Hartog, "New Aproaches to Sepsis: Molecular Diagnostics and Biomarkers", Clinical Microbiology Reviews 25, No.4(October 3,2012): minus 634) in order to distinguish these conditions, they are referred to herein as SIRS (both conditions), infection-negative SIRS (SIRS without infection, hereinafter "inSIRS"), and infection-positive S (SIRS 609, SIRS with a known or suspected infection, hereinafter "ipsires"). The causes of SIRS are diverse and varied and may include, but are not limited to, trauma, burns, pancreatitis, endotoxemia, surgery, adverse drug reactions, and infections (both local and systemic). However, it will be appreciated from the following that this may be applied to a range of different conditions, and reference to SIRS or sepsis is not intended to be limiting.

In addition, the reference data may comprise further biomarkers such as one or more phenotypic or clinical parameters of the individual and/or its relatives. The phenotypic parameters may include information such as gender, race, age, hair color, eye color, height, weight, waist circumference, hip circumference, and the like. In addition, in the case where the technique is applied to individuals other than humans, this may also include information such as the species, the name of the breed, and the like. Clinical traits may include genetic information, white blood cell count, diastolic and systolic blood pressure, bone density, body mass index, diabetes, resting heart rate, HOMA-IR, IVGT, resting heart rate, beta cell function, macrovascular function, microvascular function, atherogenic index, low density lipoprotein/high density lipoprotein ratio, intimal-media thickness, body temperature, SOFA, and the like.

Thus, in one example, for each reference individual, the reference data can include information related to at least one of the condition and desirably related to the presence, absence, extent or progression of the plurality of reference biomarkers and the condition.

Reference data can typically be collected from individuals present at a medical center with clinical signs correlated with any relevant condition of interest, and can include follow-up counseling to confirm clinical assessment, and to identify biomarkers and/or clinical signs and/or changes in severity of clinical signs over time. In this latter case, the reference data may comprise time series data indicative of the progression of the condition and/or the activity of the reference biomarker, such that the reference data of the individual may be used to determine whether the condition of the individual is improving, worsening or static. It will also be appreciated that for individuals within the sample population, the reference biomarkers are preferably substantially similar, such that a comparison of measured activity may be made between individuals.

The reference data may also be collected from a single individual over time (e.g., as the condition within the individual progresses), although more typically it will be obtained from multiple individuals, each having a different stage of the one or more conditions of interest.

It should be appreciated that, after collection, the reference data may be stored in the database 211, allowing this to be subsequently retrieved by the processing system 201 for subsequent analysis. The processing system 201 also typically stores an indication of the identity of each reference biomarker.

In one example, the measurements are received as raw data, which is then subjected to preliminary processing. Such raw data corresponds to information that has been unmodified from the source, such as output from an instrument such as a PCR instrument, an array (e.g., microarray) scanner, a sequencer, a clinical record, or any other biochemical data, biological data, observed data, and the like. This step can be used to convert the raw data into a format more suitable for analysis. In one example, this is done to normalize the raw data and thereby help ensure that biomarker values show consistency even when measured using different techniques, different equipment, etc. The purpose of the normalization is therefore to remove variations within the sample that are not directly attributable to the particular analysis under consideration. For example, variations caused by differences in sample processing at different sites are removed. Classical examples of normalization include z-score conversion for general data, or specific normalization in the hot domain, such as RMA normalization for microarrays.

However, it should be understood that in some applications, such as a single sample experiment run on a single data acquisition machine, this step may not be strictly necessary, in which case the function may be a Null function that produces the same output as the input.

In one example, the preferred method is a pairing function method compared to log normalized data. Log normalization is a standard data transformation of microarray data because the data follows a log-normal distribution when leaving the machine. Logarithmic conversion is applied to convert the data into process-friendly normal data.

In step 505, the reference data is used to define different groups.

Before this occurs, the processing system 201 optionally removes the verification sub-group of the individual from the reference data to allow the processing system 201 to determine the candidate biomarker using the reference data without the verification sub-group, such that the verification sub-group may then be used to verify the candidate biomarker or the identity comprising the plurality of candidate biomarkers. Thus, data from the verification sub-group is used to verify the efficacy of the candidate or identifying biomarker in identifying the presence, absence, degree, stage, severity, prognosis or progression of any one or more of the conditions to ensure that the potential or identifying biomarker is effective.

In one example, this is accomplished by having the processing system 210 mark individuals within the verification sub-group or alternatively store these in an alternative database of alternative locations or reference data within the database 211. The verification sub-set of individuals is typically randomly selected and may optionally be selected to include individuals with different phenotypic traits. When the verification sub-set of individuals is removed, the remaining individuals will simply be referred to as reference data for convenience throughout the remaining specification.

The reference data (i.e., not including the verification sub-group) is divided into groups. Groups may be defined in any suitable manner, and may be defined based on: any one or more of an indication of the presence, absence, degree, stage, severity, prognosis or progression of a condition, other tests or assays, or measured biomarkers associated with an individual.

For example, a group preference may be one or more groups that identify individuals with SIRS, one or more groups of individuals with ipSIRS, one or more groups of individuals with inSIRS, and one or more groups of healthy individuals. Additional groups may also be defined for individuals with other conditions. Groups may include overlapping groups, so for example, it may be desirable to define healthy individuals and groups of individuals with SIRS, with further definition to distinguish inSIRS patients from ipSIRS patients, and different degrees of inSIRS or ipSIRS, which groups have SIRS in common, but which groups differ in whether a clinician has determined the presence or absence of an infection. In addition, further subdivision may be made based on phenotypic traits, and thus groups may be defined based on gender, race, etc., such that multiple groups of individuals with a condition are defined, wherein each group relates to a different phenotypic trait.

However, it should be understood that the identification of the different groups may be performed in other ways, e.g., based on the specific activity of a biomarker within a biological sample of a reference individual, and thus, reference to a condition is not intended to be limiting and other information may be used as desired.

The manner in which the classification into groups is performed may vary depending on the preferred implementation. In one example, this may be done automatically by the processing system 201, for example, using an unsupervised approach such as Principal Component Analysis (PCA), or a supervised approach such as k-means or self-organizing map (SOM). Alternatively, this may be done manually by the operator by allowing the operator to review reference data presented on a Graphical User Interface (GUI) and define the respective groups using appropriate input commands.

At step 510, the biomarkers are filtered based on their ability to distinguish groups. The process typically examines the activity of reference biomarkers of individuals within and across groups to identify reference biomarkers whose activity differs between groups and thus can distinguish groups. A range of different analysis techniques may be utilized including, for example, regression or correlation analysis techniques. Examples of techniques used may include established methods for parametric modeling, such as partial least squares, random forests or support vector machines, which are typically combined with feature reduction techniques for selecting a particular subset of biomarkers to be used in identification.

Such techniques are known and described in numerous publications. For example, the use of Partial least squares is described in Boulesteix, Anne-Laure and Strimmer, Korbian, "Partial least squares: a versatile tool for the analysis of high-dimensional genetic data", from Briefings in Bioinformatics 2007 Vol.8.1, pages 32-44. Support vector machines are described in "LIBSVM: a library for support vector machines" from ACMTransactions on Intelligent Systems and Technology (TIST), volume 2011, Chang, C.C. and Lin, C.J. at page 27. Standard random forests in the language R are described in "Classification and Regression by random Forest" in R news 2002, volume 2, No. 3, pages 18-22, Liaw, A. and Wiener, M. .

The analysis techniques are implemented by the processing system 201 using application software that allows the processing system 201 to perform multiple ones of the analysis techniques in sequence. This is advantageous since different analysis techniques often have different biases and can therefore be used to identify different potential biomarkers that can be grouped differently, thereby reducing the risk that clinically relevant biomarkers are overlooked.

In one example, the process includes filtering out any biomarkers that show relevance to the group and thus to the condition, i.e., are below a certain relevance threshold, such as 0.3.

At step 515, derived biomarkers are generated from the filtered reference biomarkers using one or more functions. The nature of the derived biomarkers and the function used will vary depending on the preferred implementation. For example, the function may include division, subtraction, multiplication, addition of two markers, an sigmoid function applied to the product of two markers, a negative logarithm of the division of two biomarkers, a least squares regression applied to two vectors of markers to produce the output of the function (equation), a consensus correlation coefficient of the two vectors of the classified biomarkers, and the like.

Typically, the function is selected based on a number of rules. These rules may include: practicality, a function that provides the best results; explanatory, functions that can be interpreted in terms of biological functions; an output, generating a function of the output providing the information; the method is simple; performance evaluation; a minimum number of biomarkers for optimal performance; the number of biomarkers at a statistical overfitting threshold, etc.

In one example, the preferred function is a division, where the resulting biomarkers are different ratios. It will be appreciated that the division may be performed in a number of different ways, such that for three biomarkers nine different derived biomarkers may be determined.

At step 520, a performance metric is determined for each of the candidate biomarkers, including the filtered reference biomarker and any derived biomarkers. The performance measure may be in any suitable form and typically comprises a correlation or performance interpretation measure indicative of the correlation of the corresponding biomarker and its ability to distinguish groups. In one example, the performance function used to determine the performance metric is a standard univariate statistical test for all candidate biomarkers. However, other examples include t-tests, non-parametric equivalence (non-parametric equivalence) or area under the receiver operating characteristic curve (area under receiver operator curve), chi-square distribution or regression analysis or their equivalents, extensions or derivatives may be used.

The result of applying the performance function to each of the candidate selection steps is an ordered list of biomarkers, wherein the top N ordered biomarkers continue to the next stage. Biomarkers that fall below this threshold are no longer considered. The threshold applied may be the absolute number or proportion of all biomarkers, or determined by a property, such as a p-value ≦ 0.05. The threshold should be selected to contain a sufficiently large number of biomarkers to favor the inclusion of sufficiently independent biomarkers (i.e., low cross-correlation).

In step 525, the processing system 201 selects the next two candidate biomarkers based on the performance metric and the cross-correlation. In this regard, two markers that are highly correlated with each other depending on the context of the condition will not necessarily improve the ability to distinguish a particular presence, absence, degree, or prognosis of the condition more than a single one of the markers. Thus, in general, biomarkers are selected that have a high performance measure with respect to the condition but have cross-correlation that falls below a cross-correlation threshold. The cross-correlation threshold used will vary depending on the preferred implementation and is typically selected to be as low as possible, as described above. Examples of the manner in which the biomarkers are selected will be described in more detail below with respect to fig. 6A and 6B.

At step 530, the processing system 201 determines the performance of the next candidate biomarker combination. In this regard, the processing system 201 will combine the biomarker values of the selected candidate biomarkers using a combination function, such as addition, and use this to determine the indicator values based on the combination of biomarker values. The performance may be determined in any suitable manner, such as using statistical measures, correlation measures, consistency measures, or aggregate performance measures such as averages. In one particular example, the performance metric is 'explained variance' (VE). VE of "1" means that, using biomarkers, you can perfectly classify/predict the disease. VE of "0.8" means that your logo accounts for 80% of the results in practice.

Thus, at step 535, the processing system 201 compares the performance of the indicator to a performance threshold and determines at step 540 whether this is exceeded. In the event that the threshold is exceeded, this indicates that the combination of selected markers provides the degree required to distinguish, allowing the presence, absence, degree, or prognosis of the condition to be determined.

In the event that the threshold is not exceeded at step 540, a determination is made at step 545 as to whether all combinations have been considered. In this regard, it is possible to try a number of different combinations of the two selected biomarkers, and thus, if each possible combination has not been considered, the processing system 201 returns to step 530 to determine the performance of the next candidate biomarker combination. In this regard, the combinations used will typically be ordered according to preference such that the preferred combination is tried first and the less preferred combination is only tried in cases where the preferred combination proved unsuccessful.

After all candidate biomarker combinations have been considered for the two candidate biomarkers selected, and if the performance threshold has not yet been exceeded, the process moves to step 550 to compare the current number of candidate biomarkers considered to the limit. In this regard, the limit is used to control the overall number of biomarkers in a biomarker signature, thereby minimizing the size of the signature and thus the cost of performing the associated measurements and diagnostics.

If the limit has not been exceeded, additional biomarkers are added based on the correlation and performance at step 560, where the process moves to step 530 to determine the performance of the next candidate biomarker combination. Otherwise, if the limit has been reached, the next two candidate biomarkers are optionally selected at step 525. Thus, the process allows additional candidate biomarkers to be progressively included, with combinations of multiple candidate biomarkers being compared to a performance threshold to determine whether the required performance is met. If, before the number of candidate biomarkers reaches the limit, this is not achieved, the process restarts using two different candidate biomarkers.

After the performance threshold has been exceeded at step 540, the selected candidate biomarker may be defined as an identification biomarker for inclusion in the biomarker identification for the one or more conditions at step 565.

It should be noted that before the biomarker identification is finalized at step 565, additional checks may be made to ensure that candidate biomarkers included in the identification should not be excluded for any reason. For example, candidate biomarkers may be excluded from cost considerations because some combinations of candidate biomarkers may cost more than others. For example, a large number of biomarkers may cost more than a small number, and additional costs cannot be justified by small improvements in performance. Alternatively, if multiple different tests are required to measure the required biomarker values, the cost may increase.

Biomarkers may also be excluded from use for legal reasons, for example, if their use is approved or restricted for intellectual property reasons. Some biomarkers may be difficult to measure from a technical point of view, e.g. very low expression in vivo, which increases variability and thus reduces robustness.

The performance of each biomarker combination set may also include some variability, usually expressed as a confidence interval around the reported performance. Although the point estimates for one set may be higher than the point estimates for the other set, the combinations may be considered equal if the differences in providing variability are not significant.

After the particular combination of identifying biomarkers is defined, the processing system 201 may determine the range of indicator values associated with each group at step 570. In particular, the range of reference biomarker values for the identifying biomarkers within each group is used to calculate the range of indicator values for each group. These can then be compared to indicator values calculated for biological subjects having an unknown presence, absence, degree or progression of at least one condition and used to identify the group to which the subject will belong and thus identify the presence, absence, degree or progression of the condition.

Thus, the process described above iteratively evaluates biomarkers, initially selecting two biomarkers, various combinations of these being considered to determine whether these have the required properties for use in diagnosing the presence, absence, extent or progression of a condition. In the case where the required performance is not provided, additional biomarkers may be added and further combinations attempted. Thus, the process may consider three biomarkers, four biomarkers, five biomarkers, six biomarkers, seven biomarkers, eight biomarkers, nine biomarkers, or more. Typically, this is done to a limit, which may be defined, for example, based on the number of biomarkers that may in fact be measured within a given cost or process parameter. In the case where the required performance is not obtained, the process moves to select alternative candidate biomarkers, and repeats therewith.

It will therefore be appreciated that the above process initially selects those biomarkers that are not highly correlated with appropriate performance, providing maximum performance based on these. The ability of these biomarkers to differentiate is then tested and in cases where this is not sufficient, additional biomarkers may be added to the limit. If this still does not provide the required distinguishing performance, an alternative biomarker may be selected.

Depending on the preferred implementation, the process of selecting two candidate biomarkers at step 525 may be implemented in a number of ways, and examples of this will now be described with reference to fig. 6A and 6B.

In the example of FIG. 6A, at step 600, the biomarkers are grouped according to their mutual similarity. Thus, highly relevant biomarkers are placed together in a common group. The biomarkers within the group are ranked at step 610 based on their performance measure according to their relevance to the condition, with the highest ranked biomarkers from the two groups being selected at step 620 to define the next two candidate biomarkers. It will be appreciated that these may be selected from a different set than the first two candidate biomarkers if additional candidate biomarkers are required.

An alternative process is shown in fig. 6B. In this example, at step 650, the biomarkers are ranked based on their performance in distinguishing the conditions. At step 660, the next highest biomarker is selected, with the remaining biomarkers reordered based on a combination of their similarity to the selected biomarker, e.g., using mutual information and their performance. The next highest biomarker is then selected at step 680, where the process is repeated as necessary.

It will therefore be appreciated that the above described process provides a mechanism for selecting a combination of biomarkers and more generally derived biomarkers (which can be used to form a biomarker signature), which in turn can be used in diagnosing the presence, absence or extent of at least one condition or in providing a prognosis of at least one condition. In this regard, biomarker identification defines the biomarker that should be measured (i.e., identifies the biomarker), how derived biomarker values should be determined for the measured biomarker values, and then how the biomarker values should be subsequently combined to generate the indicator values. The biomarker signature may also specify a defined indicator value range that indicates a particular presence, absence, degree, or prognosis of one or more conditions.

An example of a method of using the biomarker identification described above will now be described with reference to fig. 7.

In this example, at step 700, a plurality of measured biomarker values are measured for a biological subject whose condition is unknown, these typically being provided to the processing system 201, for example by downloading from measurement equipment or the like.

After any required processing, such as normalization, etc., the processing system 201 applies one or more functions to the measured biomarker values to determine any required derived biomarker values at step 710. The derived biomarker value and another biomarker value (i.e., another derived biomarker value or a measured biomarker value) are combined to generate an indicator value, which may then be displayed or otherwise used in determining the presence, absence, extent, or prognosis of one or more conditions. Thus, this may comprise simply presenting the indicator value, allowing the medical practitioner to make an assessment or alternatively may comprise further processing, such as comparing the indicator to a defined indicator value range indicative of the particular presence, absence, extent or prognosis of one or more conditions, the result of the comparison being presented.

Thus, in the above described method, the biomarker signature defines the biomarker values that need to be measured and/or derived, allowing the processing system 201 to automatically generate indicator values based on the received measured biomarker values. After this has been done, the processing system 201 may compare the indicator value to the indicator value range and display the result of the comparison or optionally interpret the result of the comparison, allowing an indicator to be displayed that indicates the presence, absence, degree, or prognosis of the condition. This can then be used as needed by a medical practitioner in performing a medical diagnosis of the biological subject.

Using the methods described above, it has been determined that the use of a ratio of "immune system biomarkers" is particularly beneficial when assessing the likelihood that a biological subject has the presence, absence, extent, or prognosis of at least one medical condition.

As used herein, the term "immune system biomarker" refers to a biomarker of the immune system of a host that is altered as part of, or has its expression level altered as part of, an inflammatory response to an injury or pathogenic damage, including metabolic, toxic, neurotoxic, iatrogenic, thermal, or chemical damage, illustrative examples of which include trauma, surgery, drugs including chemotherapeutic drugs, radiation, diseases, including pathogenic infections, metabolic diseases, and ischemia, and foreign or implanted substances.

As used herein, the term "immune system" refers to cells, molecular components and mechanisms that provide defense against damage and lesions and substances, including antigen-specific and non-specific classes of the adaptive immune system and innate immune system, respectively, including antigenic molecules, including but not limited to tumors, pathogens, and autoreactive cells.

The term "innate immune system" refers to the host's nonspecific response to damage, including antigen-nonspecific defense cells, molecular components, and mechanisms that begin to act immediately or within hours after exposure to virtually any damage or antigen. Elements of innate immunity include, for example, phagocytes (monocytes, macrophages, dendritic cells, polymorphonuclear leukocytes such as neutrophils, reticulocytes such as kupffer cells and microglia), cells that release mediators of inflammation (basophils, mast cells and eosinophils), natural killer cells (NK cells), and physical barriers and molecules such as keratin, mucus, secretions, complement proteins, immunoglobulin m (igm), acute phase proteins, fibrinogen and molecules of the coagulation cascade, and cytokines. Effector compounds of the innate immune system include chemicals such as lysins, IgM, mucus, and chemoattractants (e.g., cytokines or histamine), complement, and coagulation proteins.

The term "adaptive immune system" refers to the antigen-specific cells, molecular components and mechanisms that occur within a few days and react with and remove a particular antigen. The adaptive immune system develops throughout the life of the host. The adaptive immune system is based on leukocytes and is divided into two major components: the humoral immune system, which functions primarily via immunoglobulins produced by B cells, and the cell-mediated immune system, which functions primarily via T cells.

Thus, in one example, an indicator correlated to a ratio of immune system biomarkers is determined, which can be used in assessing the likelihood that a biological subject has the presence, absence, extent, or prognosis of at least one medical condition.

In this example, the method includes determining a pair of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject, and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject.

The biomarker values are used to determine derived biomarker values using the pair of biomarker values, the derived biomarker values indicating a ratio of the concentrations of the pair of immune system biomarkers.

Thus, if the biomarker value is a concentration of a biomarker, the derived biomarker value will be based on a ratio of the biomarker values. However, if the biomarker values are related to the concentration of the biomarker, for example if they are logarithmically related by virtue of the biomarker values being based on the number of cycles of PCR or the like, the biomarker values may be combined in some other way, such as by subtracting the number of cycles, to determine a derived biomarker value indicative of the ratio of concentrations.

The derived biomarkers are then used to determine the indicator, either by using the derived biomarker values as indicator values, or by performing additional processing, such as comparing the derived biomarker values to a reference, etc., as will be described in more detail below.

In any case, the biomarker values are combined to determine the ratio of the concentrations of the immune system biomarkers, and this determination indicator is then used, allowing the indicator to be determined for use in determining the likelihood of the subject having a range of different conditions, depending on the immune system biomarker selected, as will be appreciated, this may be done using the process described above.

A number of further features will now be described.

In one example, the process includes determining a first derived biomarker value using a first pair of biomarker values, the first derived biomarker value indicative of a ratio of concentrations of a first immune system biomarker and a second immune system biomarker, determining a second derived biomarker value using a second pair of biomarker values, the second derived biomarker value indicative of a ratio of concentrations of a third immune system biomarker and a fourth immune system biomarker, and determining the indicator by combining the first derived biomarker value and the second derived biomarker value. Thus, in this example, two pairs of derived biomarker values may be used, which may help increase the ability of the indicator to reliably determine the likelihood that the subject has the condition.

The derived biomarker values may be combined using a combining function, such as an additive model; a linear model; a support vector machine; a neural network model; a random forest model; a regression model; a genetic algorithm; an annealing algorithm; a weighted sum; a nearest neighbor model; and a probabilistic model.

In one example, the indicator is compared to an indicator reference, wherein the likelihood is determined based on the result of the comparison. The indicator reference is typically derived from indicators determined for a number of individuals in a reference population. The reference population typically includes individuals with different characteristics, such as different genders; and/or ethnic groups, wherein different groups are defined based on different characteristics, comparing the subject's indicator with an indicator reference derived from individuals with similar characteristics. The reference population may also include a plurality of healthy individuals, a plurality of individuals having at least one diagnosed medical condition, a plurality of individuals exhibiting clinical signs of at least one medical condition, and/or a first group of individuals and a second group of individuals, each group of individuals having a respective diagnosed medical condition.

It will be appreciated that the individual selected will depend on the intended use of the indicator. In particular, when the indicator is for use in determining the likelihood of a biological subject having a particular medical condition, the sample population includes individuals who exhibit clinical signs of the particular medical condition, individuals who are diagnosed as having the particular medical condition, and healthy individuals. This ensures that the assessment of the effectiveness of the indicator applies regardless of or whether the individual has a particular condition.

It is also understood that the sample population may also include a plurality of individuals of different genders, races, ages, etc., allowing the range of control values to be common across the population. However, this is not essential, and alternatively, a control value threshold may be established that is specific to a particular subset of the population. In this case, it will be necessary to ensure that the control value threshold range used is appropriate for the subject in question.

The indicator may also be used to determine the likelihood that the subject has a first condition or a second condition, in other words to distinguish between conditions. In this case, this will typically be achieved by comparing the indicator with a first indicator reference and a second indicator reference, the first indicator reference and the second indicator reference being indicative of the first condition and the second condition, and determining the likelihood from the result of the comparison. In particular, this may include determining a first indicator probability and a second indicator probability using the results of the comparison and combining the first indicator probability and the second indicator probability, e.g., using a bayesian approach, to determine a condition probability corresponding to a likelihood that the subject has one of the conditions. In this case, the first condition and the second condition may include two medical conditions, or a single medical condition and a health condition.

In this case, the first indicator reference and the second indicator reference are distributions of indicators determined for a first set of reference populations and a second set of reference populations, the first set and the second set consisting of individuals diagnosed as having the first condition or the second condition, respectively. In this regard, this may be accomplished by determining a first group of individuals and a second group of individuals, each group of individuals having the presence or absence of a diagnosed medical condition, and determining a first indicator reference and a second indicator reference for the first group and the second group, respectively. This allows the indicator to be used to distinguish between a first condition and a second condition, which may include different medical conditions, as well as health conditions. It should also be understood that although two groups are described, this is not essential and three or more groups may also be defined.

The process is typically performed using at least one electronic processing device, such as a suitably programmed computer system or the like.

In this case, the electronic processing means typically obtain at least two pairs of measured biomarker values by receiving these from the measuring means or other quantifying means, or by retrieving these from a database or the like. The processing device then determines a first derived biomarker value indicative of a ratio of concentrations of the first and second immune system biomarkers and a second derived biomarker value indicative of a ratio of the third and fourth immune system biomarkers. The processing device then determines the indicator by combining the first derived biomarker value with the second derived biomarker value.

The processing device may then generate a representation of the indicator, for example by generating an alphanumeric indication of the indicator, a graphical indication of a comparison of the indicator to one or more indicator references, or an alphanumeric indication of the likelihood that the subject has at least one medical condition.

The method will also typically comprise obtaining a sample collected from the biological subject, the sample comprising polynucleotide expression products, and quantifying at least some of the polynucleotide expression products within the sample to determine the pair of biomarker values. This may be achieved using any suitable technique and will depend on the nature of the immune system biomarker.

For example, if the indicator is based on a ratio of concentrations of polynucleotide expression products, the process will generally include quantifying the polynucleotide expression products by amplifying at least some of the polynucleotide expression products in the sample, determining an amount of amplification that represents the degree of amplification required to obtain a defined level of each of a pair of polynucleotide expression products, and determining the indicator by determining the difference between the amounts of amplification. In this regard, the amount of amplification is typically the cycle time, the number of cycles, the cycle threshold, and the amplification time.

In this aspect, the method comprises determining a first derived biomarker value by determining a difference between amplified amounts of a first pair of polynucleotide expression products, determining a second derived biomarker value by determining a difference between amplified amounts of a second pair of polynucleotide expression products, and determining an indicator by adding the first derived biomarker value and the second derived biomarker value.

As previously discussed, the at least two immune system biomarkers have a cross-correlation for the at least one condition that is within a cross-correlation range, the cross-correlation range being between ± 0.9, and the indicator has a performance value that is greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence, extent, or prognosis of the at least one condition, the performance threshold indicating an interpretation variance of at least 0.3.

Typically, the cross-correlation range is one of: plus or minus 0.8; plus or minus 0.7; plus or minus 0.6; plus or minus 0.5; plus or minus 0.4; plus or minus 0.3; plus or minus 0.2; and ± 0.1.

Typically, each immune system biomarker has a condition correlation with the presence, absence, degree, or prognosis of at least one condition that is outside of a condition correlation range, the condition correlation range being between ± 0.3, and more typically ± 0.9; plus or minus 0.8; plus or minus 0.7; plus or minus 0.6; plus or minus 0.5; and, ± 0.4. Typically, the performance threshold indicates an interpretation variance of at least one of: 0.4; 0.5; 0.6; 0.7; 0.8; and 0.9.

The methods described above have been used to identify biomarkers of 1650 inflammatory response syndrome (also interchangeably referred to herein as "IRS biomarkers" or "IRS immune system biomarkers") that are useful to help distinguish between: (1) distinguishing between a subject infected with SIRS (i.e., a subject with inSIRS or ipSIRS) and a healthy subject or a healthy subject not infected with SIRS; (2) distinguishing between subjects with inSIRS and subjects with ipSIRS; and/or (3) differentiating subjects with different stages of ipSIRS (e.g., sepsis, severe sepsis, and septic shock). Based on this identification, the inventors have developed methods, compositions, devices and kits that utilize these biomarkers to provide an indicator for use in diagnosing the presence, absence or extent of at least one condition, or for prognosing at least one condition, wherein the at least one condition is selected from a healthy condition (e.g., a normal condition or a condition in which inSIRS and inSIRS are absent), inSIRS, ipSIRS or a stage of ipSIRS (e.g., a stage of ipSIRS with a particular severity, illustrative examples of which include mild sepsis, severe sepsis and septic shock). In advantageous embodiments, the methods and kits comprise monitoring expression of an IRS biomarker gene in cells of the immune system, including blood cells (e.g., immune cells such as leukocytes), which expression of the IRS biomarker gene may be reflected in, for example, a pattern of change in RNA levels or protein production associated with the presence of or in response to active disease.

An IRS biomarker is the expression product of a gene (also interchangeably referred to herein as an "IRS biomarker gene" or an IRS immune system biomarker gene "), including polynucleotide expression products and polypeptide expression products. As used herein, a polynucleotide expression product of an IRS biomarker gene is referred to herein as an "IRS biomarker polynucleotide". Polypeptide expression products of IRS biomarker genes are referred to herein as "IRS biomarker polypeptides". As used herein, the term "gene" refers to a stretch of nucleic acid encoding a polypeptide or functional RNA strand. Although, exon regions of a gene are transcribed to form RNA (e.g., mRNA), the term "gene" also includes regulatory regions such as promoters and enhancers that govern the expression of the exon regions.

Suitably, the IRS biomarker gene is selected from the group consisting of: PRKCZ, SKI, RER1, TAS1R1, VAMP3, AGTRAP, VPS13D, KLHDC7A, NBL1// C1orf151, MDS2, RCAN3, LDLRAP1, MAN1C1, SH3BGRL3, DHDDS, HCRTR1, CCDC28B, LCK, ZNF362, THRAP3, PPIE// CCDC25, CAP1, RNPS, C1orf84, FAAH, DMBX1, CYP4B1, BTF3L4, LRRTR 42, C1orf175// TTC4, TMEM61, FPGT// TNT 3K, ACADM 1, SPA 1, EPHX 1, RPAP 1, RPL 1/SNORCP 1, SLC 1/1, SLC 1/GCATPSR 1, SLC 1/1, SLC1, SLCP 1/1, SLCP 1, SLC1, SLCP 1, SLC1, SLCP 1, SLC1, SLCP 1, SLC1, SLCP 1, SLC1, SL, NRD, KTI, CC2D1, YIPF, JAK, SLC35D, DIRAS, ZZZ, GNG, ZNHIT, ODF2, SEP, BARHL, GCLM, CLCC// GPSM// C1orf, SORT, SLC16A, PHTF, RSBN, DENND 2// BCAS, CD, SPAG// WDR, REG// NBPF, RP-94I 2.2// NBPF// NBPF// NBPF// NBPF// NBPF// NBPF// NBPF// LOC100288142// NBPF/KIAA// 1242905// LOC 100137, APH1, POGZ, TDRKH, THEM, S100A, CRNNNR, SPNNRR 2A, S100A, GATAD 2// PLIN, XINND 4, PYC, TOGG/TABC, TOGARG, TABD 1/TADF, TABD 1/TAD, TADF, TAD1, TAD, TABD 1/TAD, TAD1, TAD1, TAD1, TAD1, TAD, TA, CYB5R1, MYBPH, CHI3L1, PIK3C2B// LOC100130573, NUAK2, NUCKS1, FAIM3, PLXNA2, SLC30A1, LPGAT1, ANGEL2, RAB3GAP2// AURKAPS1// AURKA// SNORA36B, TP53BP2, TMEM63A, PARP1, ITPKB, TARBP1, CHML, AKT3, SMYD3, AHCTF1, OR1C1, NC36OA 72, HADHB, ABHD 1/PREB, SPAST, SLC30A 1// AFX 1, CRIPT 1, FOXN 1, VRK NM 72, AHX 1/1, PHASP 1, CANKC 1, CANKS 72, CANKS 1, CANKS 72, CANKS 1, CANKS 72, CANKS 1, CANKS 72, CANKS 1, CANKS 72, CANKS 1, CANKS 72, AGFG, CHRNG, EIF4E, TRPM, LRRFIP, GAL3ST, TMEM, LAPTM4, SF3B, TP53I, UNQ2999, GPR113// SELI, MPV, PPM1, NLRC, CDC42EP, HNRPLL, COX7A2, KCNG, CALM// C2orf, BCL11, XPO, NAT8, DUSP, MOGS, SNRNP200, SEMA4, MITD, IL1, QC35F, CCDC, CLASP, SAP130, YSK, GTDC, ORC4, NR 4A// FLJ46875, DPP, GALN, SCN7, FRZB, STK17, CLK/PPIL, MPP, INO80, KLF, FAM119, NGEF, ARL4, RAB, HDP, PXRN, SECD, IRAK, C3orf, TSNR 2E, TSNR 2 SLC, SLC2 SLC, SLC 1/SLC, SLC1, SLC2 SLC, SLC1, SLC2, SLC1, SLC2, SLC1, SLC2, SLC1, SLC2, SLC1, SLC1, SLC, CCDC71, UBA7, CAMKV, WDR82, LMOD3, FOXP1, MORC1, ATG3, GSK3B// LOC100129275, HCLS1, KPNA1, PTPLB, C3orf22, RPN1, KIAA1257// ACAD9// LOC100132731, FOXL2, MECOM, PLD1, GNB4, MRPL47, KLHL6, THPO, ETV 6, BCL 6/LOC 100635, ATP13A 6, TMEM6, KIAA1530, TACC 6, CNO, BST 6, TMEM 6// DCAF 6L 6, KIT 36ENDAPG 6, TK 6, TPEGPRC 6// GCSHGCSHGCSHGCSHGCH 6, TPEGFP 6, TPEGFLX 6, TPEGFLD 6, TPEGFLX 6, TPEGFLD 6, TFAS 6, TFSC 6, TFAS 6, TFAS 6, 363636363636363672, TFC 6, 36363672, 6, 363636363636363636363636363636363672, 6, 36363636363636363672, 363672, 363636363636363672, 6, 363672, 363636363636363636363672, 6, 363636363636363636363636363636363636363672, 36363636363672, 363636363672, 363636363636363636363636363636363636363636363636363636363636363636363636363636363636363636363672, 3636363672, 363636363636, KIAA 361, GRPEL2, MFAP3, GABRA6, GABRA1, DOCK2, RANBP17// USP12, ERGIC1, ATP6V0E1// SNORA74 1, ZNF346, NSD1, CLPTM 11, UGT3A1, GDNF, TTC 1, hCG _1, MOCS 1, SLC38A 1, CCDC125, KRANA 1, HAPLN1, CCNH, TMEM 36161 1, MBLAC 1, MCTP1, TIRXRRB 1// TMTMDE 1/TMED 1-TITAN 1, KIF 31, SNC 5orf1, SKP1, CX36CL 72, KL1, CD1, YBATF 1, LACTB 1, LACTC 1/TFSC 1, SQ 1, TPRNTSCP 1, TPRNTSPT 1, TPRNTSNF 1, TPNST 1, TPRNS 1, TFAS 1, TFSC 1, TFAS 1, TFAS 1, TFS 1, TFAS 1, TFS 1, TFAS 1, TFAS 1, TFS 1, 3636363636363636363636363636363636363672, TFS 363636363636363636363672, TFS 36363636363636363636363636363636363636363636363636363636363636363636363636363636363636363636363636363672, TFN 36363636363636363672, TFN 363672, 1, TFN 363672, TFN 1, 363636, IL17, HTR1, GABRR, UBE2J, BACH, MCM, VNN, IL20, FLJ27255, 6KA, HGC6.3, UNC 84// C7orf, SDK, ZDHHHC, C7orf, GLCCI// tcag, GPNMB, CCDC126, WIPF// ZNRF// LOC441208, GPR141, STARD3, POU6F, CDC2L, ZMIZ, UPP, ZNF273, KCTD// RABGEF, RABGEF// tcag// tcag// KCTD// LOC// TD// LOC100293333, CCDC132, PVRIG// PILRB/STAG, PILRB/PVRIG// STAG, C7orf, GNB, LRRC, DDCK 427, CFTR, LSM, MGLUC 7L, MGLUAM/LOC, 124692, GINSE 2J, SLC// CANTM, SLC 7/SLC, SLC 7// TAD, SLC 1/TAD, SLC7, SLC 1/TAD, SLC7, SLC 1/TAD, SLC1, SLC7, SLC 1/TAD, SLC1, SLC7, SL, MSRA, PIWIL, NEFM// LOC100129717, EPHX, LEPROTL, MAK// C8orf, AP3M, FNTA, SGK196, UBE2V, FLJ46365, SNTG, TRIM, C8orf, PREX, PLEKHF, BAALC// FLJ10489, TTC, MTBP, ZHX, RNF139, TG, DENND// C8orf, TNFRSF10, TRIM, GSR, WHSC1L, PCMTD// PXDNL, NCOA, TRAM// LOC286190, RUNX1T, EXT, DDEF1IT, CDC37L, MRE 2R, UBAP/KIF, GALT, RGP/GBA, TGR, C9 orf/BKAGF, GE 53089, ATP6V1G, 149, FB 149, FBAP/10, FBF 9, SCA// KIF, SACK 2 XOFFT 11// FB, SHK, ENTPD1// C10orf131, ABCC2, SFXN2, SHOC2, ACSL5, BCCIP// DHX32, FAM188A, CUBN, SVIL// hCG _1783494, FAM13C// PHIPYHL, ATAD1, ANKRD22, FLJ34077, COX15, ERLIN1, ACTR1A, ABLIM1, FGLIN 2, C10orf 2, PRDX 2, C10orf119 orf 36119, NSMCE4, TALDO 2// INTS 2, NT2, FGC 2, PDE 32, DNJC 2, PTPRJ// OR4B 2, C11orf 2, TMEM 36109, CD2, TMPOEM 36138, POLR 22, TFC 2, TMACK 2, TMFACTC 2, TFC 2, TFOCB 2, TFC 2, TFOCB 2, TFC 2, TFOCB 2, TFC 2, TFOCB 2, TFC 2, TFOCB 2, TFC 2, TFOCB 2, TFC 2, TFOCB 2, TFC 2, TFOCB 2, TFC 2, TFOCB 2, COQ10A, TSPAN31, CDK4/MARCH9/C3HC4, LEMD3, IRAK3, TMTC3, ACTR6, TCTN1, PXMP2// PGAM5, CHC 1B, SLC2A3// SLC2A14, C3AR1, PLBD1, TM7SF 1, ASB 1// PHB, LMBR 11, FMNL 1// PRPF40 1, AAAS, NFE 1, GPR1, CD1, SARNP// DNAJNAJC 1, NACA, CDK 72, CDK 1/TSPAN 72, TMBIM 1// LUFC 322, IL1, 367 1, HAL 1, APPL 1, GLTP, GIT 1, VPS1, PPTC 1, PCP 1/GCP 1/GCK 1, GCD 1, GCD 1, 363636363672, 1, 363672, 1, 36, LAMP1, TMCO3, UPF3A, ZYM 5// ZYM 2, ZDHHHC 20// LOC728099, PARP4, MTMR6// LOC 6482, HSPH1, N4BP2L2// CG030, ELF1, LCP1, KPNA3, C13orf1, DLEU2// DLEU2L, CYCYGU 1B2, INTS6, DACH1, TBC1D4, EDNRB, GT2, GPR183, LIG4, ANKRD10, RASA3, RNGU 2/LOC 643332, RPGRIP1, IRF9, TSSK 9, C14orf 9, SCFD 9, SCFLD 9, FANCKR 9, ABGDHD 363672, ACT/9, ACT 9/TAC 9, ACT 9, SSC 9/TAC 9, SSN 9, TAC 9/TAC 9, TAC 363672, TAC 9, TAC 3636363636363636363672, TAC 9, TAC 363636363636363636363672, TAC 36363636363636363636363672, TAC 9, TAC 3636363672, TAC 36363672, TAC 363672, TAC 36363672, TAC 9, TAC 36363636363636363636363672, TAC 9, TAC 3636363672, TAC 9, TAC 36363636363672, TAC 9, TAC 3636363636363636363636363636363636363636363672, TAC 9, TAC 363636363636363636363636363672, TAC 9, TAC 3636363672, TAC 9, TAC, PPP1R13B, AKT1, BRF1, TUBGCP5, SNRPN// SNURF// IPW// SNORD116-16// SNORD116-18// SNORD116-21// SNORD116-22// SNORD116-17// SNORD116-19// PAR5// PAR-SN// SNORD116-2// SNORD116-25// SNORD116-26// SNORD107// SNORD115-12// SNORD115-5// SNORD115-6// SNORD115-9// SNORD116-11// SNORD116-12// SNORD116-13// SNORD116-28// SNORD116-4// SNORD 64/PAR 63/SNORD 109// SNORD 109/SNORD 116-13// SNORD 116// SNORD115-6// SNORD 116// SNORD115-13// SNORD115-6// SNORD115-9// SNORD115-13// SNORD115-9// SNORD 116// SNORD115-11// SNORD115-1// SNORD115-14// SNORD115-15// SNORD115-21// SNORD115-10// SNORD115-7// SNORD115-16// SNORD115-40// SNORD115-42// SNORD115-11// SNORD115-29// SNORD115-34// SNORD115-36// SNORD115-4// SNORD115-43// HBII-52-24// SNORD116-5// SNORD116-7// SNORD115-26// SNORD115-30// SNORD116-15// SNORD116-8// SNORD115-2// SNORD115-39// SNORD116-14// SNORD116-20// SNORD115-8// SNORD 115-115// SNORD115-39// SNORD115-8// SNORD115-6// SNORD115-8// SNORD115-6// -38// SNORD115-41// SNORD115-22// SNORD115-44// SNORD116-1// SNORD115-17// SNORD115-18// SNORD115-19// SNORD115-20// SNORD116@, APBA2, MTMR15// MTMR10, RYR3, BAHD1, CHP, JMJD7-PLA2G4B// JXRD 7// PLA2G4B, HAUS2, C15orf 2// SERF2, USP 22, TRIM2, PLDN, RDSQL, JXK 2, USP 2, GLDN, MAPK 2, LACTB 368 2, APH 12, USP NINI/LOC 13072, SALG 36XC 2/SALT 72, CALCX 2/SAL 2, CALCO 72, CALCMO 2// CALCO 2, CALCMO 2, SAL 2// CALCO 2, SAL 2, CALCF 2, CALCO 72// CALCO 2, CALCO 72// CALCO 2, CALCO 72, OIP, ZFP106, CDAN, SPG// ISLR, SPPL2, GNB// LOC100129973, MYO5, ARPP, RAB27, CCPG// PIGB// DYX1C, BNIP, CA, FAM96, KIAA0101// CSNK1G, TLE, PARP, NPTN, MAN2C, IMP, MTHFS, ST// C15orf, TMC, AP3B, C15orf, DETR, NTN, CRAM, TM2D, LOCWDOT// FBXL, TMEM204, CRAMP 1// HN1, MAPK8IP, TBL, TSC, KCTD// PRO 0461/PDPK, CLUAP, LOCDNASE, DNAJAK, CP110, C16orf, LYRM, METTL, POEEF 2, POF 3, PLK, PRLL/9121/2888/PDPK, SUA// 7242// SUN 7242, SUN 727243, SUA// LOC 7243, LOC// LAK, LOC/OCK, LOC// OCK2, LOC// OCK 3, NAK, TAD, SUN 723/OCK, and SUN 723/OCK 3/OCK, and SUN 3/OCK, and SUN, ITGAL, SRCAP// SNORA, ZNF646// ZNF668, C16orf, TMEM188, LPCAT, CETP, CKLF, CMTM// CKLF, TMEM208, CTCF, THAP, NUTF, EDC, SLC 7A// SLC7A6, PRMT, SNTB, VPS4, DDX 19// DDX19, CHST, HP// HPR, PLCG, KLHL, KIAA0182, BANP// RUNDC2, TRAPPC2, SPG, CDK, TCF, AFG3L, LUC7, AXIN, JMJD, LMF, UNKL, CLCN, MRPS, RNPS, NLRC, TRAP/DNASE, ADCY, CORO, C16orf, RRN// VTR, 6590// LOC 7392/LOC 998, YXXVTR// LORD, ZNF 3/KR// ZN, ZC// SOG 2, LOC// LOC, LOC2, LOC, OCF, OCD, LOC, OCD, OCK, OC, CSNK2A, GOT, FAM96, FHOD// SLC9A, ATP6V 0D// LOC100132855, GFOD, SLC12A, DPEP, CHTF// HAS, COG// PDF, TERF, AARS, ST3GAL, VAC// LOC100130894, AP1G, WDR, CTRB// CTRB, TAF 1// ADAD, FBXO, ZCCHC, FAM38, CENPBD, TIMM, RPA, DPH// OVCA, SGSM, ARRB, LOC100130950, DNAH, PIGL, TRPV, DRIP, DRG, ALKBH/FLJ 13773, SMCR, WSB, TAOK, CPD, SUZ12, RNF135, ZNF830, TAF, GGNBP, PSMP 580, CDC, NBLAS, NBR, MRK 46/GOP 019, MRTSORF 13417/SSB, TAK 2C, TARG// MGC, TAG 2, TACK 2, TAB, TAK 2, TAG// SLA, LACK 2, LACK// FLK, LACK 2, TAM 2, LACK 2, MFSD, SEPT, TNRC6, TMC, ENGASE, RPTOR, GPS, FN3KRP, TBCD, GEMIN, GLOD, SLC43A, PRPF, SMG// C17orf, METT 10// LOC284009, SHPK, TAX1BP, P2RX, MYBBP 1// SPNS, PELP, PFN, ZNF232, DHX, DERL, NLRP// LOC728392, ASGR, NEURL// GPS// D4S234, ZBTB, TP, VAMP, PIK3R, ELAC, NCOR// C20orf191// LOC 100704, ZNF287, TOM 1L/LOC 2415, GRAP// SNORD 3-1// SNORD 3-2// XK 133581, ALF, RAB, PHF, NUP, TOMO 2/LOC, SLC 822/OCR, SLC 17/OCR, SLC// LOC 10017, SLC// LOC 7217, SLC 10017, SLC// OCR, SLC 10017, SLC, SEPT, MED// LOC100129112, LIMD// MAP3K, STRADA, FTSJ, CD79, ICAM, ERN, TEX, LRRC37A// LRRC37A// LRRC 37// ARL 17P// LRRC37A// LOC100294335// LOC644397, GNA, WIPI// ARSG, FAM20, NAT, GGA, H3F 3// H3F3, EXOC, SFRS, TMC// LOC100131096, USP, CD, RAB, VAPA, SEH1, HQ0644/PRO0644, RNMT, RNF138, GALNT, TYPP, PIK3C, SLC14A, ME, SERNB// PINB, AFZNF 407, ZNF236, NF// SU 1004, ENOSF/MSS, PIR 5520, TSN 3C, SLC, SERNFB// SHC, SARG, SARCF, SARG, SACK 2, SACK, ARRDC2, IFI30, C19orf60, CRTC1// MAML2, RFXANK// MEF2B// LOC729991, ZNF101, ZNF738, ZNF257// ZNF492// ZNF99// ZNF98// LOC646864, C19orf2, KIAA0355// FLJ21369, USF2, TMEM147, LIN 2// PSENEN, ZNF19 orf2, TBCB/POLR2 2, ZNF382, ZNF420, ZNF383, CCDC2, XAF 574, CD177, ZNF 230/ZNF 222, ZNF611, GRWD 2, FLT 32, ZNF175, NCN0003685, PPP2R 12, XAF 578/ZNF 72, ZNF 72// ZNF 72, ZNF 72/ZNF 72, ZNF 72// ZNF 72, ZNF 72, ZNF 72// ZNF 72, ZNF 3/SSF// ZNF 72, ZNF, C19orf56, DHPS, TNPO2// SNORD41, LPHN1, NDB 7, AKAP8, AKAP8L, CHERP// C19orf44// CALR3, INSL3// JAK3, IL12RB1, UPK1A, TYRBP, ZNF529, ZNF461, ZNF607, YIF1B, PRR13, CEACAM4, PLAUR, TRAPPC6A, ERCC1// CD3EAP, RTN2, SYMPK, LYRP1, NOSIP, PNKP, NKG7, FPR1, ZNF28, OSRNOAT 7, LILRA5, ZNF// ZNF 550/36416, ZNF 36KPF 5, SSF 5, SSCP 5, SSC 5, SSCP 5, SSF 5, SSDE 36, RBM39// LOC643167, BLCAP, SERINC3// TTPAL, ZNF335, ELMO2, B4GALT5, DPM1, ZFP64, ZNF217, CTSZ, SYCP2, PSMA7, DIDO1, YTHDF1, CHODL, BACH1, C21orf41// BACH1, IL10RB, IFNAR1, IFNGR2, ZN, MORC3// DOPEY2, DYRK1A, KCNJ15, ETS2, RRP1B, PFKL, TRPM2, ADARB1, SAMSN1// LOC 38368813, N6AMT1, SYNJ1, TMEM 3650, KCNE1, PRNE 1, CD2, CSTC 2, CSTR 2 TSCP 2872, SANPN 1003672// SSP 1, SUNPN 1// SOCP 1, SUNPN 1// 1, SUNPN 1, 368, 1, 368, 1, 368, 1, 3611, 1, 3611, 1, 368, 1, 368, 1, 368, 1, 368, 1, 368, 1, 368, 36, TPST2, SF3A1// CCDC157, PES1, PIK3IP1, PATZ1, C22orf30, IL2RB, CSNK1E// LOC400927, UNC84B, CBX7// LOC100128400, RPS19BP1, MKL1// KIAA1659, RANGAP1, TCF20, LDOC1L, UNQ6126, TUBGCP6, SBF1// SBF1P1, MSL3, MOSPD2, BMX// HNRPDL, PDHA1, YY2, PDK 2, GK// GK3 2// FTL// SNOC 2, CANFORF 2, CANFET 2, SADDS 36363636363672, SADDS 2, SADDS 3636363672, SADDS 363636363636363636363672, SADDP 363672, SADDP 2, SADDS 2, 363636363672, SADDP 363672, SADDP 2, SADDS 2, SADDP 363672, SADDP 2, SADDP 2, 363636363672, 36363672, SADDP 363672, ODZ1, ELF4, RAP2C, FAM127B// FAM127C// FAM127A, TMEM185A, ARD1A, IRAK1, DNASE1L1// RPL10, SH3KBP1, Mitochondrial, CCNL2, INPP5B, TLR5, ADRB3// GOT1L1, NOC2L// SAMD11// LOC401 1 (hereinafter interchangeably referred to herein as "complete list of IRS immune system biomarker genes" or "complete list IRS biomarker genes 010").

The methods, compositions, devices and kits of the invention provide an indicator for use in diagnosing the presence, absence or extent of at least one condition selected from a healthy condition (e.g., a normal condition or a condition in which inSIRS and inSIRS are absent), inSIRS, ipSIRS or a stage of ipSIRS (e.g., a stage of ipSIRS with a particular severity, such as mild sepsis, severe sepsis and septic shock) or for providing a prognosis of at least one condition using IRS biomarkers as broadly described herein above and elsewhere, which methods, compositions, devices and kits may comprise: (a) determining a plurality of IRS biomarker values, each IRS biomarker value indicating a value measured or derived for at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarker of a biological subject; (b) determining the indicator using a combination of a plurality of IRS biomarker values (also referred to herein as "biomarker signature"), the indicator being at least partially indicative of the presence, absence, extent or prognosis of at least one condition, wherein: (i) at least two (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10 or more) IRS biomarkers have a cross-correlation for at least one condition within a cross-correlation range, the cross-correlation range being between ± 0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or extent of the at least one condition or to provide a prognosis for the at least one condition, the performance threshold being indicative of an interpretation variance of at least 0.3.

In an advantageous embodiment, the diagnostic or prognostic methods, compositions, devices and kits of the present invention comprise: (1) determining a plurality of measured IRS biomarker values, each measured IRS biomarker value being a measured value of an IRS biomarker of the biological subject; and (2) determining at least one derived IRS biomarker value indicative of a value of a corresponding derived IRS biomarker for at least one application function of the measured IRS biomarker values. The function suitably comprises at least one of: (a) multiplying the two IRS biomarker values; (b) dividing the two IRS biomarker values; (c) adding the two IRS biomarker values; (d) subtracting the two IRS biomarker values; (e) a weighted sum of at least two IRS biomarker values; (f) a logarithmic sum of at least two IRS biomarker values; and (g) a sigmoid function of at least two IRS biomarker values.

In some embodiments, diagnostic or prognostic methods, compositions, devices, and kits include: at least one derived IRS biomarker value corresponding to a ratio of the two measured IRS biomarker values is determined. In these examples, the diagnostic or prognostic methods, devices, and kits suitably include combining at least two IRS biomarker values to determine an indicator value representative of the indicator, and in illustrative examples of this type, the at least two IRS biomarker values are combined using a combination function (e.g., any one or more of: an additive model, a linear model, a support vector machine, a neural network model, a random forest model, a regression model, a genetic algorithm, an annealing algorithm, a weighted sum, a nearest neighbor model, and a probability model). Suitably, the diagnostic or prognostic methods, devices and kits comprise: (a) determining a first derived IRS biomarker value indicative of a ratio of a first measured IRS biomarker value and a second measured IRS biomarker value; (b) determining a second derived IRS biomarker value indicative of a ratio of a third measured IRS biomarker value and a fourth measured IRS biomarker value; and (c) adding the first derived IRS biomarker value and the second derived IRS biomarker value to generate an indicator value.

In some embodiments, the methods, compositions, kits and devices of the invention are useful for diagnosing the presence or absence of inSIRS or a health condition in a biological subject, suitably comprising: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, extent or prognosis of at least one condition selected from insiRS and a health condition, wherein: (i) at least two IRS biomarkers have a cross-correlation for the at least one condition within a cross-correlation range, the cross-correlation range being between ± 0.9; and, (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or extent of at least one condition, or to provide a prognosis of at least one condition, the performance threshold indicating an interpretation variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker panel, and wherein at least another one of the at least two IRS biomarkers is selected from a second IRS biomarker panel, wherein the first IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from group a IRS biomarker genes as defined herein, and wherein the second IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from group B IRS biomarker genes as defined herein.

In other embodiments, methods, devices and kits useful for diagnosing the presence or absence of ipSIRS or a health condition in a biological subject suitably include: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, extent or prognosis of at least one condition selected from ipsIRS and a health condition, wherein: (i) at least two IRS biomarkers have a cross-correlation for the at least one condition within a cross-correlation range, the cross-correlation range being between ± 0.9; and, (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or extent of at least one condition, or to provide a prognosis of at least one condition, the performance threshold indicating an interpretation variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker panel, and wherein at least another one of the at least two IRS biomarkers is selected from a second IRS biomarker panel, wherein the first IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from group C IRS biomarker genes as defined herein, and wherein the second IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from group D IRS biomarker genes as defined herein.

In still other embodiments, methods, devices and kits are useful for diagnosing the presence or absence of inSIRS or ipSIRS in a biological subject, suitably comprising: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, extent or prognosis of at least one condition selected from insiRS and ipsIRS, wherein: (i) at least two IRS biomarkers have a cross-correlation for the at least one condition within a cross-correlation range, the cross-correlation range being between ± 0.9; and, (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or extent of at least one condition, or to provide a prognosis of at least one condition, the performance threshold indicating an interpretation variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker panel, and wherein at least another one of the at least two IRS biomarkers is selected from a second IRS biomarker panel, wherein the first IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from group E IRS biomarker genes as defined herein, and wherein the second IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from group F IRS biomarker genes as defined herein.

In other embodiments, methods, devices and kits are useful for diagnosing the presence or absence of inSIRS or ipSIRS in a biological subject, suitably comprising: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, extent or prognosis of at least one condition selected from insiRS and ipsIRS, wherein: (i) at least four IRS biomarkers have a cross-correlation for at least one condition within a cross-correlation range, the cross-correlation range being between ± 0.9; and, (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or extent of at least one condition, or to provide a prognosis of at least one condition, the performance threshold indicating an interpretation variance of at least 0.3, wherein at least one of the at least four IRS biomarkers is selected from a first IRS biomarker panel, wherein at least another of the at least four IRS biomarkers is selected from a second IRS biomarker panel, wherein at least another of the at least four IRS biomarkers is selected from a third IRS biomarker panel, and wherein at least another of the at least four IRS biomarkers is selected from a fourth IRS biomarker panel, wherein the first IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products of G-group IRS biomarker genes as defined herein, wherein the second IRS biomarker group consists of polynucleotide expression products and/or polypeptide expression products from group H IRS biomarker genes as defined herein, wherein the third IRS biomarker group consists of polynucleotide expression products and/or polypeptide expression products from group I IRS biomarker genes as defined herein, and wherein the fourth IRS biomarker group consists of polynucleotide expression products and/or polypeptide expression products from group J IRS biomarker genes as defined herein.

In still other embodiments, methods, devices and kits are useful for diagnosing the presence or absence of mild sepsis or severe sepsis in a biological subject, suitably comprising: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, extent or prognosis of at least one condition selected from mild sepsis and severe sepsis, wherein: (i) at least two IRS biomarkers have a cross-correlation for the at least one condition within a cross-correlation range, the cross-correlation range being between ± 0.9; and, (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or extent of at least one condition, or to provide a prognosis of at least one condition, the performance threshold indicating an interpretation variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker panel, and wherein at least another one of the at least two IRS biomarkers is selected from a second IRS biomarker panel, wherein the first IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from a K group of IRS biomarker genes as defined herein, and wherein the second IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from a L group of IRS biomarker genes as defined herein.

In still other embodiments, methods, devices and kits useful for diagnosing the presence or absence of mild sepsis or septic shock in a biological subject suitably include: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, extent or prognosis of at least one condition selected from mild sepsis and septic shock, wherein: (i) at least two IRS biomarkers have a cross-correlation for the at least one condition within a cross-correlation range, the cross-correlation range being between ± 0.9; and, (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or extent of at least one condition, or to provide a prognosis of at least one condition, the performance threshold indicating an interpretation variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker panel, and wherein at least another one of the at least two IRS biomarkers is selected from a second IRS biomarker panel, wherein the first IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from group M IRS biomarker genes as defined herein, and wherein the second IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from group N IRS biomarker genes as defined herein.

In still other embodiments, methods, devices and kits useful for diagnosing the presence or absence of severe sepsis or septic shock in a biological subject suitably include: (a) determining a plurality of IRS biomarker values, each IRS biomarker value being indicative of a value measured or derived for at least one IRS biomarker of a biological subject; (b) determining the indicator using a combination of the plurality of IRS biomarker values, the at least one indicator being at least partially indicative of the presence, absence, extent or prognosis of at least one condition selected from severe sepsis and septic shock, wherein: (i) at least two IRS biomarkers have a cross-correlation for the at least one condition within a cross-correlation range, the cross-correlation range being between ± 0.9; and, (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or extent of at least one condition, or to provide a prognosis of at least one condition, the performance threshold indicating an interpretation variance of at least 0.3, wherein at least one of the at least two IRS biomarkers is selected from a first IRS biomarker panel, and wherein at least another one of the at least two IRS biomarkers is selected from a second IRS biomarker panel, wherein the first IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from O group IRS biomarker genes as defined herein, and wherein the second IRS biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from P group IRS biomarker genes as defined herein.

As used herein, the terms "diagnosis", "diagnosing", and the like are used interchangeably herein, and include determining the likelihood that a subject will develop a condition, or the presence or nature of a condition in a subject. These terms also include determining the severity of the disease or onset of the disease, and in the case of rational therapy, where diagnosis guides therapy, including initial selection of therapy, modification of therapy (e.g., adjustment of dosage or dosing regimen), and the like. "likelihood" means a measure of: whether a biological subject having a particular measured or derived biomarker value actually has a condition (or does not have it) based on a given mathematical model. For example, the increased likelihood may be relative or absolute and may be represented qualitatively or quantitatively. For example, based on previous population studies, the increased likelihood can be simply determined by determining the subject's measured or derived biomarker values for at least two IRS biomarkers, and placing the subject in an "increased likelihood" classification. The term "likelihood" is also used herein interchangeably with the term "probability".

In some embodiments, biomarkers, including IRS biomarkers, are obtained from a biological sample. The term "biological sample" as used herein refers to a sample that can be extracted, untreated, treated, diluted, or concentrated from an animal. The biological sample is suitably a biological fluid such as whole blood, serum, plasma, saliva, urine, sweat, ascites, peritoneal fluid, synovial fluid, amniotic fluid, cerebrospinal fluid, tissue biopsy, or the like. In certain embodiments, the biological sample comprises blood, particularly peripheral blood, or a fraction or extract thereof. Typically, the biological sample comprises blood cells such as mature leukocytes, immature leukocytes, or developing leukocytes, including lymphocytes, polymorphonuclear leukocytes, neutrophils, monocytes, reticulocytes, basophils, coelomic cells, blood cells, eosinophils, megakaryocytes, macrophages, dendritic cell natural killer cells, or a fraction (e.g., a nucleic acid or protein fraction) of such cells. In particular embodiments, the biological sample comprises leukocytes, including Peripheral Blood Mononuclear Cells (PBMCs). Obtained "means initially owned. Biological or reference samples so obtained include, for example, nucleic acid or polypeptide extracts isolated or derived from a particular source. For example, the extract may be isolated directly from a biological fluid or tissue of the subject.

The term "nucleic acid" or "polynucleotide" as used herein includes RNA, mRNA, miRNA, cRNA, cDNA, mtDNA or DNA. The term generally refers to a polymeric form of nucleotides (ribonucleotides or deoxyribonucleotides) or a modified form of either type of nucleotide that is at least 10 bases in length. The term includes single-and double-stranded forms of DNA or RNA.

"protein," "polypeptide," and "peptide" are used interchangeably herein to refer to polymers of amino acid residues and variants and synthetic analogs of polymers of amino acid residues.

In some embodiments, the biomarker signature is determined by analysis of IRS biomarker measured or derived IRS biomarker values for one or more control subjects with or without a condition. These biomarkers are referred to herein as "reference IRS biomarkers". In particular embodiments, the individual control subject is selected from a "healthy control subject", "non-healthy control subject", "SIRS control subject", "inSIRS control subject", "ipSIRS control subject", "control subject with a particular stage of ipSIRS", illustrative examples of which include a "mild sepsis control subject", "severe sepsis control subject", and "septic shock control subject", and the like, which are also referred to herein as control groups (e.g., "healthy control group", "non-healthy control group", "SIRS control group", "inSIRS control group", "ipSIRS stage group", illustrative examples of which include a "mild sepsis control group", "severe sepsis control group", and "septic shock control group", and the like).

Suitably, the measured or derived IRS biomarker value of the individual corresponds to the level or amount of the respective IRS biomarker or to a function applied to that level or amount. As used herein, the terms "level" and "amount" are used interchangeably herein to designate an amount of a quantity (e.g., weight or mole), a semi-quantitative amount, a relative amount (e.g., weight% or mole% within a classification), a concentration, and the like. Thus, these terms include the absolute or relative amount or concentration of an IRS biomarker in a sample.

In some embodiments, the presence, absence, extent, or prognosis of at least one condition in a biological subject is established by determining a plurality of IRS biomarker values, wherein each IRS biomarker value is indicative of a value measured or derived for at least one IRS biomarker in a biological sample obtained from the biological subject. These biomarkers are referred to herein as "sample IRS biomarkers". According to the present invention, the sample IRS biomarker corresponds to a reference IRS biomarker (also referred to herein as a "corresponding IRS biomarker"). By "corresponding IRS biomarker" is meant an IRS biomarker that is structurally and/or functionally similar to a reference IRS biomarker. Representative corresponding IRS biomarkers include the expression products of allelic variants (same locus), homologues (different locus) and orthologues (different organism) of a reference IRS biomarker gene. The nucleic acid variants of the reference IRS biomarker gene and the encoded IRS biomarker polynucleotide expression product may comprise nucleotide substitutions, deletions, inversions and/or insertions. Variations may occur in either or both of the coding and non-coding regions. Variations may result in both conservative and non-conservative amino acid substitutions (as compared to the encoded product). With respect to nucleotide sequences, conservative variants include those sequences that, due to the degeneracy of the genetic code, encode the amino acid sequence of a reference IRS polypeptide.

Typically, a variant of a particular IRS biomarker gene or polynucleotide will have at least about 40%, 45%, 50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59% 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69% 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or more sequence identity to that particular nucleotide sequence as determined by sequence alignment programs known in the art using default parameters. In some embodiments, an IRS biomarker gene or polynucleotide displays at least about 40%, 45%, 50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59%, 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69% 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or more sequence identity to a nucleotide sequence selected from any one of SEQ ID NOs 1-1650, as summarized in table 1.

Corresponding IRS biomarkers also include amino acid sequences that show substantial sequence similarity or identity to the amino acid sequence of a reference IRS biomarker polypeptide. Typically, an amino acid sequence corresponding to a reference amino acid sequence will exhibit at least about 50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59%, 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 97%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or even up to 100% sequence similarity or identity to a reference amino acid sequence selected from any one of SEQ ID NO 1651-3284, as summarized in Table 2.

In some embodiments, the calculation of sequence similarity or sequence identity between sequences is performed as follows:

to determine the percent identity of two amino acid sequences or two nucleic acid sequences, the sequences are aligned for optimal comparison purposes (e.g., gaps can be introduced in one or both of the first and second amino acid or nucleic acid sequences for optimal alignment, and non-homologous sequences can be omitted for comparison purposes). In some embodiments, the length of a reference sequence aligned for comparison purposes is at least 30%, typically at least 40%, more typically at least 50%, 60%, and even more typically at least 70%, 80%, 90%, 100% of the length of the reference sequence. The amino acid residues or nucleotides at the corresponding amino acid positions or nucleotide positions are then compared. When a position in the first sequence is occupied by the same amino acid residue or nucleotide at the corresponding position in the second sequence, then the molecules are identical at that position. For amino acid sequence comparisons, when a position in a first sequence is occupied by the same or a similar amino acid residue (i.e., a conservative substitution) at the corresponding position in a second sequence, then the molecules are identical at that position.

The percent identity between two sequences is a function of the number of identical amino acid residues shared by the sequences at the individual positions, taking into account the number of gaps that need to be introduced and the length of each gap for optimal alignment of the two sequences. In contrast, the percent similarity between two sequences is a function of the number of identical and similar amino acid residues shared by the sequences at the individual positions, taking into account the number of gaps that need to be introduced and the length of each gap for optimal alignment of the two sequences.

Comparison of sequences and determination of percent identity or percent similarity between sequences can be accomplished using mathematical algorithms. In certain embodiments, the percent identity or percent similarity between amino acid sequences is determined as follows: the Needleman and Hunsch (1970, J.mol.biol.48: 444-. In particular embodiments, the percent identity between nucleotide sequences is determined as follows: CMP matrix and GAP weights of 40, 50, 60, 70, or 80 and length weights of 1, 2, 3, 4, 5, or 6 were used using the GAP program in the GCG software package (available at http:// www.gcg.com). Non-limiting sets of parameters (and unless otherwise specified that the set of parameters should be used) include the Blossum62 scoring matrix with a gap penalty of 12, a gap extension penalty of 4, and a frameshift gap penalty of 5.

In some embodiments, the percent identity or percent similarity between amino acid sequences or nucleotide sequences can be determined as follows: the PAM120 weight residue table, gap length penalty of 12 and gap penalty of 4 were used using the algorithm of e.meyers and w.miller (1989, cab, 4:11-17) which had been incorporated into the ALIGN program (version 2.0).

The nucleic acid sequences and protein sequences described herein can be used as "query sequences" to search against public databases, for example, to identify other family members or related sequences. Such searches can be performed using the NBLAST and XBLAST programs of Altschul et al (version 2.0) (1990, J.mol.biol.,215: 403-10). BLAST nucleotide searches can be performed using NBLAST program, scoring 100, and word length 12, to obtain nucleotide sequences homologous to the 53010 nucleic acid molecules of the invention. BLAST protein searches can be performed using the XBLAST program, with a score of 50 and a word length of 3, to obtain amino acid sequences homologous to the YYYYY protein molecules of the invention. For comparison purposes to obtain gap-bearing alignments, Gapped BLAST can be utilized as described in Altschul et al (1997, Nucleic Acids Res,25: 3389-. When utilizing BLAST and Gapped BLAST programs, the default parameters of the respective programs (e.g., XBLAST and NBLAST) can be used.

Corresponding IRS biomarker polynucleotides also include nucleic acid sequences that hybridize to a reference IRS biomarker polynucleotide, or their complement, under stringent conditions as described below. As used herein, the term "hybridizes under low stringency, medium stringency, high stringency, or very high stringency conditions" describes conditions for hybridization and washing. "hybridization" is used herein to mean the pairing of complementary nucleotide sequences to produce a DNA-DNA hybrid or a DNA-RNA hybrid. Complementary base sequences are those related by the base pairing rules. In DNA, A pairs with T and C pairs with G. In RNA, U pairs with a and C pairs with G. In this regard, the terms "match" and "mismatch" as used herein refer to the hybridization potential of paired nucleotides in a complementary nucleic acid strand. Matched nucleotides hybridize efficiently, such as the classical A-T and G-C base pairs mentioned above. Mismatches are other combinations of nucleotides that do not hybridize efficiently.

Instructions for performing hybridization reactions can be found in Ausubel et al, (1998, supra), sections 6.3.1-6.3.6. Aqueous and non-aqueous methods are described in this reference, and either can be used. References herein to low stringency conditions include and encompass from at least about 1% v/v to at least about 15% v/v formamide and from at least about 1M to at least about 2M salt for hybridization at 42℃, as well as at least about 1M to at least about 2M salt for washing at 42℃. Low stringency conditions can also include 1% Bovine Serum Albumin (BSA), 1mM EDTA, 0.5M NaHPO ₄(pH7.2), 7% SDS for hybridization at 65 ℃ and (i)2 ℃ × SSC, 0.1% SDS, or (ii) 0.5% BSA, 1mM EDTA, 40mM NaHPO₄(pH7.2), 5% SDS for washing at room temperature one embodiment of low stringency conditions comprises hybridization at about 45 ° C in 6 ° × sodium chloride/sodium citrate (SSC), followed by two washes at least 50 ° C in 0.2 × SSC, 0.1% SDS (wash temperature can be increased to 55 ° C for low stringency conditions). moderately stringent conditions comprise and encompass from at least about 16% v/v to at least about 30% v/v formamide and from at least about 0.5M to at least about 0.9M salt for hybridization at 42 °. C, and at least about 0.1M to at least about 0.2M salt for washing at 55 °. CComprises 1% Bovine Serum Albumin (BSA), 1mM EDTA, 0.5M NaHPO₄(pH7.2), 7% SDS for hybridization at 65 ℃ and (i)2 ℃ × SSC, 0.1% SDS, or (ii) 0.5% BSA, 1mM EDTA, 40mM NaHPO₄(pH7.2), 5% SDS for washing at 60-65 deg.C one embodiment of medium stringency conditions comprises hybridization at about 45 deg.C in 6 deg. × SSC followed by one or more washes at 60 deg.C in 0.2 deg. × SSC, 0.1% SDS high stringency conditions comprise and encompass from at least about 31% v/v to at least about 50% v/v formamide and from about 0.01M to about 0.15M salt for hybridization at 42 deg.C, and from about 0.01M to about 0.02M salt for washing at 55 deg.C high stringency conditions can also comprise 1% BSA, 1mM EDTA, 0.5M NaHPO ₄(pH7.2), 7% SDS for hybridization at 65 ℃ and (i)0.2 ℃ × SSC, 0.1% SDS, or (ii) 0.5% BSA, 1mM EDTA, 40mM NaHPO₄(pH7.2), 1% SDS for washing at temperatures in excess of 65 deg.C one embodiment of high stringency conditions comprises hybridization at about 45 deg.C in 6 deg. × SSC followed by one or more washes at 65 deg.C in 0.2 deg. × SSC, 0.1% SDS.

In certain embodiments, the corresponding IRS biomarker polynucleotide is a polynucleotide that hybridizes to a disclosed nucleotide sequence under very high stringency conditions. One embodiment of very high stringency conditions comprises hybridization at 0.5M sodium phosphate, 7% SDS at 65℃, followed by one or more washes at 0.2 ° × SSC, 1% SDS at 65℃.

Other stringent conditions are well known in the art and the skilled addressee will appreciate that a variety of factors may be manipulated to optimize the specificity of the hybridization. Optimization of the stringency of the final wash can be used to ensure a high degree of hybridization. For a detailed example, see Ausubel et al, supra, at pages 2.10.1 to 2.10.16 and Sambrook et al (1989, supra) at sections 1.101 to 1.104.

The IRS biomarkers disclosed herein each have significant sensitivity and specificity for diagnosing the presence, absence or extent of at least a condition selected from the group consisting of: a health condition (e.g., a normal condition or a condition in which inSIRS and inSIRS are absent), inSIRS, ipSIRS, or a stage of ipSIRS (e.g., a stage of ipSIRS with a particular severity, such as mild sepsis, severe sepsis, and septic shock). Thus, it is feasible to use individual IRS biomarkers in methods, devices and kits that do not rely on the use of low cross-correlation between biomarkers to diagnose at least the presence, absence or extent of a condition. In illustrative examples of this type, the invention includes methods, kits and devices useful for diagnosing the presence or absence of inSIRS or a health condition in a biological subject, suitably including: (1) correlating a reference biomarker signature with the presence or absence of a condition selected from inSIRS and a health condition, wherein the reference biomarker signature evaluates at least one IRS biomarker; (2) obtaining a biomarker signature for a sample from the subject, wherein the sample biomarker signature evaluates the corresponding IRS biomarker for the individual IRS biomarker in the reference biomarker signature; and (3) diagnosing the presence or absence of a condition in the subject based on the sample biomarker signature and the reference biomarker signature, wherein the individual IRS biomarker is an expression product of an IRS biomarker gene selected from the group consisting of a group a IRS biomarker gene and a group B IRS biomarker gene as defined herein.

In other non-limiting examples, methods, devices and kits useful for diagnosing the presence or absence of ipSIRS or a health condition in a biological subject suitably include: (1) correlating a reference biomarker signature with the presence or absence of a condition selected from ipSIRS and a health condition, wherein the reference biomarker signature evaluates at least one IRS biomarker; (2) obtaining a biomarker signature for a sample from the subject, wherein the sample biomarker signature evaluates the corresponding IRS biomarker for the individual IRS biomarker in the reference biomarker signature; and (3) diagnosing the presence or absence of a condition in the subject based on the sample biomarker signature and the reference biomarker signature, wherein the individual IRS biomarker is an expression product of an IRS biomarker gene selected from the group consisting of a group C IRS biomarker gene and a group D IRS biomarker gene as defined herein.

In still other non-limiting examples, methods, devices, and kits are useful for diagnosing the presence or absence of inSIRS or ipSIRS in a biological subject, suitably including: (1) correlating a reference biomarker signature with the presence or absence of a condition selected from inSIRS and ipSIRS, wherein the reference biomarker signature evaluates at least one IRS biomarker; (2) obtaining a biomarker signature for a sample from the subject, wherein the sample biomarker signature evaluates the corresponding IRS biomarker for the individual IRS biomarker in the reference biomarker signature; and (3) diagnosing the presence or absence of a condition in the subject based on the sample biomarker signature and the reference biomarker signature, wherein the individual IRS biomarker is an expression product of an IRS biomarker gene selected from the group consisting of group E IRS biomarker genes and group F IRS biomarker genes as defined herein.

In still other illustrative examples, methods, devices, and kits are useful for diagnosing the presence or absence of mild sepsis or severe sepsis in a biological subject, suitably comprising: (1) correlating a reference biomarker signature with the presence or absence of a condition selected from mild sepsis and severe sepsis, wherein the reference biomarker signature evaluates at least one IRS biomarker; (2) obtaining a biomarker signature for a sample from the subject, wherein the sample biomarker signature evaluates the corresponding IRS biomarker for the individual IRS biomarker in the reference biomarker signature; and (3) diagnosing the presence or absence of a condition in the subject based on the sample biomarker signature and the reference biomarker signature, wherein the individual IRS biomarker is an expression product of an IRS biomarker gene selected from the group consisting of a group K IRS biomarker gene and a group L IRS biomarker gene as defined herein.

In still other illustrative examples, methods, devices, and kits are useful for diagnosing the presence or absence of mild sepsis or septic shock in a biological subject, suitably comprising: (1) correlating a reference biomarker signature with the presence or absence of a condition selected from mild sepsis and septic shock, wherein the reference biomarker signature evaluates at least one IRS biomarker; (2) obtaining a biomarker signature for a sample from the subject, wherein the sample biomarker signature evaluates the corresponding IRS biomarker for the individual IRS biomarker in the reference biomarker signature; and (3) diagnosing the presence or absence of a condition in the subject based on the sample biomarker signature and the reference biomarker signature, wherein the individual IRS biomarker is an expression product of an IRS biomarker gene selected from the group consisting of an M IRS biomarker gene and an N IRS biomarker gene as defined herein.

In other non-limiting examples, methods, devices and kits are useful for diagnosing the presence or absence of severe sepsis or septic shock in a biological subject, suitably comprising: (1) correlating a reference biomarker signature with the presence or absence of a condition selected from severe sepsis and septic shock, wherein the reference biomarker signature evaluates at least one IRS biomarker; (2) obtaining a biomarker signature for a sample from the subject, wherein the sample biomarker signature evaluates the corresponding IRS biomarker for the individual IRS biomarker in the reference biomarker signature; and (3) diagnosing the presence or absence of a condition in the subject based on the sample biomarker signature and the reference biomarker signature, wherein the individual IRS biomarker is an expression product of an IRS biomarker gene selected from the group consisting of an O-group IRS biomarker gene and a P-group IRS biomarker gene as defined herein.

Biomarkers can be quantified or detected using any suitable technique. In particular embodiments, biomarkers including IRS biomarkers are quantified using reagents that determine the level or abundance of individual biomarkers. Non-limiting reagents of this type include reagents for use in nucleic acid-based assays and protein-based assays.

In an illustrative nucleic acid-based assay, nucleic acids are isolated from cells contained in a biological sample according to standard methods (Sambrook, et al, 1989, supra; and Ausubel, et al, 1994, supra). Nucleic acids are typically fractionated (e.g., poly A)⁺RNA) or whole cell RNA. When RNA is used asFor the object of detection (subject), it may be desirable to convert RNA to complementary DNA. In some embodiments, the nucleic acid is amplified by a template-dependent nucleic acid amplification technique. Many template-dependent processes are available for amplifying IRS biomarker sequences present in a given template sample. An exemplary nucleic acid amplification technique is the polymerase chain reaction (referred to as PCR), which is described in detail in U.S. Pat. nos. 4,683,195, 4,683,202, and 4,800,159, Ausubel et al (supra) and Innis et al ("PCRProtocols", Academic Press, inc., San Diego calif., 1990). Briefly, in PCR, two primer sequences are prepared that are complementary to regions on opposite complementary strands of the biomarker sequence. Excess deoxynucleoside triphosphates are added to the reaction mixture along with a DNA polymerase, e.g., Taq polymerase. If a homologous IRS biomarker sequence is present in the sample, the primer will bind to the biomarker and the polymerase will cause the primer to be extended along the biomarker sequence by adding nucleotides. By raising and lowering the temperature of the reaction mixture, the extended primers will separate from the biomarkers, forming reaction products, and excess primers will bind to the biomarkers and to the reaction products and repeat the process. Reverse transcriptase PCR amplification procedures can be performed to quantify the amount of mRNA amplified. Methods for reverse transcription of RNA into cDNA are well known and described in Sambrook et al, 1989, supra. An alternative method for reverse transcription utilizes a thermostable RNA-dependent DNA polymerase. These methods are described in WO 90/07641. Polymerase chain reaction methods are well known in the art.

In certain advantageous embodiments, the template-dependent amplification comprises real-time quantification of the transcript. For example, RNA or DNA can be quantified using real-time PCR techniques (Higuchi,1992, et al, Biotechnology 10: 413-. By determining the concentration of the amplified product of the target DNA in a PCR reaction that has completed the same number of cycles and is within its linear range, it is possible to determine the relative concentration of a particular target sequence in the original DNA mixture. If the DNA mixture is cDNA synthesized from RNA isolated from different tissues or cells, the relative abundance of the particular mRNA from which the target sequence is derived can be determined for each tissue or cell. This direct ratio between the concentration of the PCR product and the relative mRNA abundance is only so in the linear range of the PCR reaction. The final concentration of target DNA in the plateau portion of the curve is determined by the availability of reagents in the reaction mixture and is independent of the original concentration of target DNA. In a specific embodiment, multiplex tandem PCR (MT-PCR) is employed, which uses a two-step process for gene expression profiling from small amounts of RNA or DNA, as described, for example, in U.S. patent application publication No. 20070190540. In the first step, RNA is converted to cDNA and amplified using multiple gene-specific primers. In the second step, each individual gene was quantified by real-time PCR.

In certain embodiments, the target nucleic acid is quantified using blotting techniques well known to those skilled in the art. Southern blotting involves the use of DNA as a target, whereas northern blotting involves the use of RNA as a target. Although cDNA blots are similar in many respects to blots or RNA species, each provides different types of information. Briefly, probes are used to target DNA or RNA species that have been immobilized on a suitable matrix (often a filter of nitrocellulose). The different species should be spatially separated to facilitate analysis. This is usually done by gel electrophoresis of nucleic acid species followed by "blotting" to the filter. Subsequently, the blotted target is incubated with a probe (usually labeled) under conditions that promote denaturation and rehybridization. Because the probe is designed to base pair with the target, the probe will bind to a portion of the target sequence under renaturation conditions. Unbound probes are then removed and detection is done as described above. After detection/quantification, one can compare the results observed in a given subject to a control response group or a statistically significant reference group or population of control subjects as defined herein. In this way, it is possible to correlate the amount of IRS biomarker nucleic acid detected with the progression or severity of the disease. As used herein, the term "probe" refers to a molecule that binds to a particular sequence or subsequence or other portion of another molecule. Unless otherwise indicated, the term "probe" generally refers to a nucleic acid probe that binds to another nucleic acid (also referred to herein as a "target polynucleotide") by complementary base pairing. Depending on the stringency of the hybridization conditions, a probe can bind to a target polynucleotide that lacks complete sequence complementarity to the probe. Probes may be directly or indirectly labeled and include primers within their scope. By "primer" is meant an oligonucleotide that, when paired with a strand of DNA, is capable of initiating synthesis of a primer extension product in the presence of a suitable polymerization agent. The primer is preferably single stranded for maximum efficiency in amplification, but may alternatively be double stranded. The primer must be long enough to prime the synthesis of extension products in the presence of the polymerization agent. The length of the primer depends on many factors, including the application, the temperature to be employed, the template reaction conditions, other reagents, and the source of the primer. For example, depending on the complexity of the target sequence, the primer can be at least about 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 50, 75, 100, 150, 200, 300, 400, 500 to a length that is one base shorter than the template sequence at the 3' end of the primer to allow extension of the nucleic acid strand, although the 5' end of the primer can extend beyond the length of the 3' end of the template sequence. In certain embodiments, the primer may be a large polynucleotide, such as from about 35 nucleotides to thousands of bases or more. A primer may be selected to be "substantially complementary" to a sequence on the template to which it is designed to hybridize and serve as a site for initiation of synthesis. By "substantially complementary" is meant that the primers are sufficiently complementary to hybridize to the target polynucleotide. Desirably, a primer does not contain a mismatch to the template to which the primer is designed to hybridize, but this is not essential. For example, a non-complementary nucleotide residue can be attached to the 5' end of the primer, with the remainder of the primer sequence being complementary to the template. Alternatively, a non-complementary nucleotide residue or a stretch of non-complementary nucleotide residues may be interspersed into a primer, provided that the primer sequence has sufficient complementarity with the template sequence to hybridize therewith, and thereby form a template for synthesis of an extension product of the primer.

Biochip-based techniques such as those described by Hacia et al (1996, Nature Genetics14: 441-. Briefly, these techniques include quantitative methods for the rapid and accurate analysis of large numbers of genes. By tagging genes with oligonucleotides or using an array of immobilized nucleic acid probes, one can isolate target molecules as a high density array using biochip technology and screen these molecules based on hybridization. See also Pease et al (1994, Proc. Natl. Acad. Sci. U.S.A.91: 5022-5026); fodor et al (1991, Science 251: 767-773). Briefly, nucleic acid probes directed to IRS biomarker polynucleotides are prepared and attached to a biochip for the screening and diagnostic methods as outlined herein. The nucleic acid probes attached to the biochip are designed to be substantially complementary to the specifically expressed IRS biomarker nucleic acid, i.e. the target sequence (the target sequence of the sample or other probe sequences, e.g. in a sandwich assay (sandwich assays)), such that hybridization of the target sequence and the probes of the invention occurs. This complementarity is not necessarily perfect; any number of base pair mismatches can be present which would interfere with hybridization between the target sequence and the nucleic acid probe of the invention. However, if the number of mismatches is so large that no hybridization can occur even under the least stringent hybridization conditions, then the sequence is not the complementary target sequence. In certain embodiments, more than one probe per sequence is used, using overlapping probes or probes directed to different portions of the target. I.e., two, three, four or more probes, three being desired, for building redundancy for a particular target. The probes may be overlapping (i.e., have some sequence in common), or separate.

In an illustrative biochip assay, oligonucleotide probes on a biochip are exposed to or contacted with a nucleic acid sample suspected of containing one or more IRS biomarker polynucleotides under conditions that favor specific hybridization. Sample extracts of DNA or RNA, single-or double-stranded, can be prepared from fluid suspensions of biological materials, or by grinding the biological materials, or following cell lysis procedures, including, but not limited to, lysis by SDS (or other detergent), osmotic shock, guanidinium isothiocyanate, and lysozyme treatment. Suitable DNAs that can be used in the methods of the invention include cDNAs. Such DNA may be prepared by any of a number of commonly used protocols, such as, for example, those described in Ausubel, et al, 1994, supra, and Sambrook, et al, 1989, supra.

Suitable RNAs that may be used in the methods of the invention include messenger RNA, complementary RNA transcribed from DNA (crna), or genomic or subgenomic RNA. Such RNA can be prepared using standard protocols as described, for example, in the relevant sections of Ausubel et al 1994, supra and Sambrook, et al 1989, supra).

The cDNA may be fragmented, for example, by sonication or by treatment with restriction enzymes. Suitably, the cDNA is fragmented such that the resulting DNA fragment is of a length greater than that of the immobilized oligonucleotide probe, but small enough to allow rapid access thereto under suitable hybridization conditions. Alternatively, fragments of the cDNA may be selected and amplified using suitable nucleotide amplification techniques (including appropriate random primers or specific primers) as described, for example, above.

Typically, the target IRS biomarker polynucleotide is detectably labeled such that its hybridization to a separate probe can be determined. The target polynucleotide is typically detectably labeled with a reporter molecule, illustrative examples of which include chromogens, catalysts, enzymes, fluorescent dyes, chemiluminescent molecules, bioluminescent molecules, lanthanide ions (e.g., Eu)³⁴) Radioisotopes and direct visual labelling. In the case of direct visual labeling, colloidal metallic or non-metallic particles, dye particles, enzymes or substrates, organic polymers, latex particles, liposomes, or other vesicles containing signal-producing substances, and the like may be utilized. Illustrative labels of this type include large colloids, e.g., metal colloids such as those from gold oxide, selenium oxide, silver oxide, tin oxide, and titanium oxide. In some embodiments in which an enzyme is used as a direct visual marker, biotinylated bases are incorporated into the target polynucleotide.

The hybrid formation step can be performed under conditions suitable for hybridizing the oligonucleotide probe to the test nucleic acid (including DNA or RNA). In this connection, reference may be made, for example, to NUCLEIC ACID HYBRIDIZATION, A PRACTICAL APPROACH (Homes and Higgins, eds.) (IRL press, Washington D.C., 1985). Typically, whether hybridization occurs is affected by: the length of the oligonucleotide probe and the polynucleotide sequence to be tested, the pH, the temperature, the concentration of monovalent and divalent cations, the proportion of G and C nucleotides in the region where the hybrid is formed, the viscosity of the medium and the possible presence of denaturants. Such variables also affect the time required for hybridization. Thus, the preferred conditions will depend on the particular application. However, such empirical conditions can be routinely determined without undue experimentation.

After the hybrid formation step, the probe is washed with hybridization buffer to remove any unbound nucleic acids. This washing step retains only the bound target polynucleotide. The probes are then examined to identify which probes have hybridized to the target polynucleotide.

The hybridization reaction is then examined to determine which of the probes has hybridized to the corresponding target sequence. Depending on the nature of the reporter molecule associated with the target polynucleotide, the signal can be detected with the instrument as follows: by irradiating a fluorescent label with light and detecting fluorescence with a fluorometer; by providing an enzyme system to produce a dye that is detectable using a spectrophotometer; or detecting dye particles or colored colloidal metal or non-metal particles using a reflectometer; in the case of using radiolabels or chemiluminescent molecules, radiation counters or autoradiography are employed. Thus, the detection means may be adapted to detect or scan light associated with the label, which light may comprise fluorescence, luminescence, a focused light beam or a laser. In such cases, a Charge Coupled Device (CCD) or photocell may be used to scan the probe from each location in the microarray: light emission of the target polynucleotide hybrids and data were recorded directly in a digital computer. In some cases, electronic detection of the signal may not be necessary. For example, with enzymatically generated stains associated with a nucleic acid array format, visual inspection of the array will allow for resolution of patterns on the array. In the case of nucleic acid arrays, the detection device is suitably matched with pattern recognition software to convert the pattern of signals from the array into a common linguistic genetic profile. In certain embodiments, the oligonucleotide probes specific for different IRS biomarker polynucleotides are in the form of a nucleic acid array, and detection of the signal generated by the reporter molecules on the array is performed using a 'chip reader'. Detection systems that can be used by 'chip readers' are described, for example, by Pirrung et al (U.S. Pat. No. 5,143,854). The chip reader will typically also incorporate some signal processing to determine whether the signal at a particular array location or feature is a true positive or perhaps a spurious signal. Exemplary chip readers are described, for example, by Fodor et al (U.S. Pat. No. 5,925,525). Alternatively, when the array is made using a mixture of individually addressable species of labeled microbeads, the reaction can be detected using flow cytometry.

In certain embodiments, the IRS biomarker is a target RNA (e.g., mRNA) or a DNA copy of the target RNA, the level of which is measured using at least one nucleic acid probe that hybridizes to the target RNA or to the DNA copy under at least low, moderate, or high stringency conditions, wherein the nucleic acid probe comprises at least 15 (e.g., 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, or more) contiguous nucleotides of the IRS biomarker polynucleotide. In some embodiments, the measured level or abundance of the target RNA or DNA copy thereof is normalized to the level or abundance of the reference RNA or DNA copy of the reference RNA. Suitably, the nucleic acid probe is immobilised on a solid support or a semi-solid support. In an illustrative example of this type, the nucleic acid probes form part of a spatial array of nucleic acid probes. In some embodiments, the level of nucleic acid probe bound to the target RNA or to the DNA copy is measured by hybridization (e.g., using a nucleic acid array). In other embodiments, the level of nucleic acid probe bound to the target RNA or to the DNA copy is measured by nucleic acid amplification (e.g., using Polymerase Chain Reaction (PCR)). In still other embodiments, the level of nucleic acid probe bound to the target RNA or to the DNA copy is measured by a nuclease protection assay.

In other embodiments, IRS biomarker protein levels are determined using protein-based assays known in the art. For example, when the IRS biomarker protein is an enzyme, the protein may be quantified based on its catalytic activity or based on the number of molecules of the protein contained in the sample. Antibody-based techniques may be employed, including, for example, immunoassays, such as enzyme-linked immunosorbent assays (ELISAs) and Radioimmunoassays (RIA).

In particular embodiments, protein capture arrays are employed that allow for the simultaneous detection and/or quantification of a large number of proteins. For example, low density protein arrays on filter membranes, such as the general protein array system (Ge,2000Nucleic Acids Res.28(2): e3), allow imaging of arrayed antigens using standard ELISA techniques and scanning charge-coupled device (CCD) detectors. Immunosensor arrays have also been developed that are capable of simultaneously detecting clinical analytes. It is now possible to use protein array profiling (profile) analysis of protein expression in body fluids, such as serum, of healthy or diseased subjects, as well as of subjects before and after drug treatment.

Exemplary protein capture arrays include arrays comprising spatially addressed antigen binding molecules (often referred to as antibody arrays) that can facilitate extensive parallel analysis of many proteins defining a proteome or a sub-proteome. Antibody arrays have been demonstrated to have the required properties of specificity and acceptable background, and some are commercially available (e.g., BDBiosciences, Clontech, BioRad, and Sigma). Various methods for preparing antibody arrays have been reported (see, e.g., Lopez et al, 2003J. chromatography. B787: 19-27; Cahill,2000Trends in Biotechnology 7: 47-51; U.S. patent application publication 2002/0055186; U.S. patent application publication 2003/0003599; PCT publication WO 03/062444; PCT publication WO 03/077851; PCT publication WO 02/59601; PCT publication WO 02/39120; PCT publication WO 01/79849; PCT publication WO 99/39210). Illustrative examples of such arrays of antigen binding molecules that can recognize at least a subset of proteins expressed by a cell or group of cells include growth factor receptors, hormone receptors, neurotransmitter receptors, catecholamine receptors, amino acid derivative receptors, cytokine receptors, extracellular matrix receptors, antibodies, lectins, cytokines, serine protease inhibitors, proteases, kinases, phosphatases, ras-like gtpases, hydrolases, steroid hormone receptors, transcription factors, heat shock transcription factors, DNA binding proteins, zinc finger proteins, leucine zipper proteins, homeodomain proteins, intracellular signal transduction modulators and effectors, apoptosis-related factors, DNA synthesis factors, DNA repair factors, DNA recombination factors, and cell surface antigens.

Separate spatially distinct protein capture agents are typically attached to a support surface that is typically planar or contoured. Common physical supports include glass slides, silicon, microwells, nitrocellulose or PVDF membranes and magnetic beads, among others.

Particles in suspension can also be used as the basis for an array, provided they are encoded for identification; the system includes color-coded microspheres (e.g., available from Luminex, Bio-Rad, and Nanomics Biosystems) and semiconductor nanocrystals (e.g., Qdots available from Quantum Dots)^TM) And barcode beads (UltraPlex available from Smartbeads)^TM) And multi-metal nanorods (Nanobarcodes available from Surromed)^TMParticles). Beads can also be assembled into planar arrays on semiconductor chips (e.g., available from LEAPS technology and BioArray Solutions). When particles are used, individual protein capture agents are typically attached to individual particles to provide spatial definition or spacing of the array. The particles can then be measured separately but in parallel in a compartmentalized manner, for example in the wells of a microtiter plate or in separate test tubes.

In operation, a protein sample (see, e.g., U.S. patent application publication 2002/0055186), which is optionally fragmented to form peptide fragments, is delivered to a protein capture array under conditions suitable for protein or peptide binding, and the array is washed to remove unbound or non-specifically bound components of the sample from the array. The presence or amount of protein or peptide bound to each feature of the array is then detected using a suitable detection system. The amount of protein bound to a feature of the array can be determined relative to the amount of a second protein bound to a second feature of the array. In certain embodiments, the amount of the second protein in the sample is already known or known to be constant.

In particular embodiments, the IRS biomarker is a target polypeptide, the level of which is measured using at least one antigen binding molecule that immunologically interacts with the target polypeptide. In these embodiments, the measured level of the target polypeptide is normalized to the level of the reference polypeptide. Suitably, the antigen binding molecule is immobilized on a solid support or a semi-solid support. In an illustrative example of this type, the antigen binding molecules form part of a spatial array of antigen binding molecules. In some embodiments, the level of antigen binding molecule bound to the target polypeptide is measured by an immunoassay (e.g., using an ELISA). Reference herein to "immune interaction" includes reference to any interaction, reaction or other form of association between molecules, and in particular wherein one of the molecules is a component of the immune system or a component that mimics the immune system.

Detection and quantification of the biomarkers of the invention, including IRS biomarkers, all necessary reagents required can be assembled together in a kit. In some embodiments, a kit comprises: (i) reagents that allow quantification (e.g., determination of level or abundance) of the first biomarker; and (ii) an agent that allows quantification (e.g., determination of a level or abundance) of a second biomarker, wherein the first biomarker and the second biomarker have a cross-correlation with respect to at least one condition (e.g., at least one of a healthy condition and a stage of one or more diseases such as, but not limited to, inSIRS, ipSIRS, or ipSIRS (e.g., a stage of ipSIRS having a particular severity such as mild sepsis, severe sepsis, and septic shock) that lies within a cross-correlation range of ± 0.9, and wherein the combination of the respective biomarker values of the first biomarker and the second biomarker measured or derived from a biological subject has a value greater than or equal to that represents a combination of the first biomarker and the second biomarker diagnostic of the presence, absence, or abundance of the at least one condition, Or a degree or a performance value of a performance threshold of the ability to provide a prognosis of the at least one condition, the performance threshold being an interpretation variance of at least 0.3. In some embodiments, the kit further comprises (iii) reagents that allow quantification (e.g., determination of level or abundance) of a third biomarker; and (ii) an agent that allows quantification (e.g., determination of a level or abundance) of a fourth biomarker, wherein the third biomarker and the fourth biomarker have a cross-correlation with respect to at least one condition that lies within a cross-correlation range of ± 0.9, and wherein for a combination of the respective biomarker values of the third biomarker and the fourth biomarker measured or derived from a biological subject, the performance threshold is an interpretation variance of at least 0.3 that is greater than or equal to a performance value representing the ability of the combination of the third biomarker and the fourth biomarker to diagnose the presence, absence, or extent of the at least one condition or to provide a prognosis for the at least one condition.

In an advantageous embodiment, the kit of the invention is useful for diagnosing the presence, absence or extent of at least one condition, or providing a prognosis of at least one condition, wherein the at least one condition is selected from the group consisting of a healthy condition, inSIRS, ipSIRS or a stage of ipSIRS. In these embodiments, the IRS biomarker is suitably selected from the group as broadly described herein above and elsewhere.

In the context of the present invention, "kit" is understood to mean a product comprising the different reagents required to carry out the process of the invention, packaged to allow their transport and storage. Materials suitable for packaging the components of the kit include crystals, plastics (polyethylene, polypropylene, polycarbonate, etc.), bottles, vials, paper, packaging (envelopes), and the like. In addition, the kits of the invention may comprise instructions for simultaneous, sequential or separate use of the different components comprised in the kit. The instructions may be in the form of printed material or in the form of an electronic support capable of storing the instructions such that they can be read by a subject, such as electronic storage media (disks, tapes, etc.), optical media (CD-ROMs, DVDs), etc. Alternatively or additionally, the medium may contain an internet address providing the instructions.

By "a reagent that allows quantification of a biomarker" is meant a compound or material, or a group of compounds or materials, that allows quantification of a biomarker. In particular embodiments, the compound, material or group of compounds or materials allows for the determination of the expression level of a gene (e.g., an IRS biomarker gene), including without limitation the extraction of RNA material, the determination of the level of the corresponding RNA, and the like, primers for the synthesis of the corresponding cDNA, primers for the amplification of DNA, and/or probes capable of specifically hybridizing to the RNA encoded by the gene (or the corresponding cDNA), TaqMan probes, and the like.

The kit may also optionally include appropriate reagents for detection of markers, positive and negative controls, wash solutions, blotting membranes, microtiter plates, dilution buffers, and the like. For example, a nucleic acid-based detection kit can include (i) a biomarker polynucleotide (e.g., an IRS biomarker polynucleotide) (which can serve as a positive control), (ii) a primer or probe that specifically hybridizes to the biomarker polynucleotide (e.g., an IRS biomarker polynucleotide). Enzymes suitable for amplifying nucleic acids may also be included, including various polymerases (reverse transcriptase, Taq enzyme, Sequenase^TMDNA ligase, etc., depending on the nucleic acid amplification technique employed), deoxynucleotides, and buffers to provide the necessary reaction mixture for amplification. Such kits will typically also include a different container in a suitable device for each individual reagent and enzyme, and each primer or probe. Alternatively, the protein-based detection kit can include (i) a biomarker polypeptide (e.g., an IRS biomarker polypeptide) (which can serve as a positive control), (ii) an antibody that specifically binds to the biomarker polypeptide (e.g., an IRS biomarker peptide). The kit may also be characterized as follows: a plurality of devices (e.g., one or more) and reagents (e.g., one or more) for performing one of the assays described herein; and/or a stamp for quantifying expression of biomarker genes (e.g., IRS biomarker genes) using the kit Instructions for the brush.

The reagents described herein, which can optionally be associated with a detectable label, can be displayed in the form of: a microfluidic card, chip or chamber, microarray or kit adapted for use with the assays described in the examples or below, e.g., RT-PCR or Q-PCR techniques described herein. The term "microarray" refers to an arrangement of hybridizable array elements, e.g., probes (including primers), ligands, biomarker nucleic acid sequences, or protein sequences, on a substrate.

The reagents also have utility in compositions for detecting and quantifying the biomarkers of the invention. For example, reverse transcriptase can be used to reverse transcribe RNA transcripts, including mRNA, in a nucleic acid sample to produce reverse transcribed transcripts, including reverse transcribed mRNA (also referred to as "cDNA"). The nucleic acid sample is suitably derived from a component of the immune system, representative examples of which include components of the innate and adaptive immune systems as discussed extensively above, for example. In particular embodiments, the reverse transcribed RNA is of blood cell (e.g., peripheral blood cell) origin. Suitably, the reverse transcribed RNA is of leukocyte origin.

The reagent is suitably used to quantify the reverse transcribed transcripts. For example, oligonucleotide primers that hybridize to the reverse transcribed transcripts can be used to amplify at least a portion of the reverse transcribed transcripts via a suitable nucleic acid amplification technique, e.g., the RT-PCR or Q PCR techniques described herein. Alternatively, the oligonucleotide probes can be used to hybridize to the reverse transcribed transcripts for quantification using nucleic acid hybridization assay techniques (e.g., microarray analysis) as described, for example, above. Thus, in some embodiments, the respective oligonucleotide primer or probe hybridizes to a complementary nucleic acid sequence of a reverse transcribed transcript in a composition of the invention. The compositions generally include labeled reagents for detecting and/or quantifying reverse transcribed transcripts. Representative reagents of this type include labeled oligonucleotide primers or probes that hybridize to RNA transcripts or reverse transcribed RNA, labeled reverse transcribed RNA, and labeled oligonucleotide linkers or tags (e.g., labeled RNA or DNA linkers or tags) for labeling (e.g., end labeling such as 3' end labeling) RNA or reverse transcribed RNA. Primers, probes, RNA or reverse transcribed RNA (i.e., cDNA), whether labeled or unlabeled, can be immobilized or free in solution. Representative reagents of this type include labeled oligonucleotide primers or probes that hybridize to the reverse transcribed transcripts as well as to labeled reverse transcribed transcripts. The label may be any reporter molecule as known in the art, illustrative examples of which are described above and elsewhere herein.

The invention also includes an RNA embodiment that is not reverse transcribed, in which cDNA is not prepared and the RNA transcript is the subject of analysis directly. Thus, in other embodiments, reagents are suitably used to directly quantify RNA transcripts. For example, oligonucleotide probes can be used to hybridize to transcripts using nucleic acid hybridization assay techniques (e.g., microarray analysis) as described, for example, above for quantifying immune system biomarkers of the invention. Thus, in some embodiments, the respective oligonucleotide probe hybridizes to a complementary nucleic acid sequence of an immune system biomarker transcript in a composition of the invention. In illustrative examples of this type, the composition may include labeled reagents that hybridize to the transcript for detecting and/or quantifying the transcript. Representative reagents of this type include labeled oligonucleotide probes that hybridize to the transcripts as well as to labeled transcripts. The primer or probe may be immobilized or free in solution.

The term "immobilized" means that the molecular species of interest is immobilized to a solid support, suitably by covalent attachment. This covalent attachment can be achieved by different methods depending on the molecular nature of the molecular species. Furthermore, molecular species can also be immobilized on solid supports by electrostatic forces, hydrophobic or hydrophilic interactions, or van der waals forces. The above-described physicochemical interactions generally occur in interactions between molecules. In particular embodiments, all that needs to be done is that the molecule (e.g., nucleic acid or protein) remains immobilized or attached to the support under conditions in which the support is intended to be used, for example in applications requiring nucleic acid amplification and/or sequencing or in antibody binding assays. For example, the oligonucleotide or primer is immobilized such that the 3' end is available for enzymatic extension and/or at least a portion of the sequence is capable of hybridizing to a complementary sequence. In some embodiments, immobilization may occur via hybridization to a surface-attached primer, in which case the immobilized primer or oligonucleotide may be in the 3'-5' direction. In other embodiments, immobilization may occur by methods other than base-pairing hybridization, such as covalent attachment.

The term "solid support" as used herein refers to a solid inert surface or body to which molecular species, such as nucleic acids and polypeptides, can be immobilized. Non-limiting examples of solid supports include glass surfaces, plastic surfaces, latex, dextran, polystyrene surfaces, polypropylene surfaces, polyacrylamide gels, gold surfaces, and silicon wafers. In some embodiments, the solid support is in the form of a membrane, chip, or particle. For example, the solid support may be a glass surface (e.g., a flat surface of a flow cell channel). In some embodiments, a solid support may comprise an inert substrate or matrix that has been "functionalized", such as by application of a layer or coating of an intermediate material comprising reactive groups that allow covalent attachment to molecules, such as polynucleotides. By way of non-limiting example, such supports may comprise polyacrylamide hydrogels supported on an inert substrate such as glass. Molecules (e.g., polynucleotides) can be covalently attached directly to an intermediate material (e.g., a hydrogel), but the intermediate material itself can be non-covalently attached to a substrate or matrix (e.g., a glass substrate). The support may comprise a plurality of particles or beads each having a different attached molecular species.

The invention also extends to the management of inSIRS, ipSIRS or a particular stage of ipSIRS, or the prevention of further progression of inSIRS, ipSIRS or a particular stage of ipSIRS (e.g. mild sepsis, severe sepsis and septic shock), or to assess the efficacy of a therapy in a subject following positive diagnosis of the presence in the subject of inSIRS, ipSIRS or a particular stage of ipSIRS (e.g. mild sepsis, severe sepsis and septic shock). Management of inSIRS or ipSIRS conditions is typically highly intensive and may include identification and improvement of the underlying cause as well as invasive use of therapeutic compounds such as vasoactive compounds, antibiotics, steroids, antibodies to endotoxin, anti-tumor necrosis factor agents, recombinant protein C. In addition, palliative therapies aimed at restoring and protecting organ function, such as described, for example, in Cohen and Glasser (1991, Lancet 338: 736) 739, can be used, such as intravenous infusion and oxygen delivery and strict glycemic control. Therapies for ipSIRS are reviewed in Healy (2002, Ann. Pharmacother.36(4):648-54) and Brindley (2005, CJEM.7(4):227) and Jenkins (2006, J Hosp Med.1(5): 285-295).

Typically, the therapeutic agent will be administered in a pharmaceutical (or veterinary) composition and in an effective amount to achieve its intended purpose, along with a pharmaceutically acceptable carrier. The dose of active compound administered to the subject should be sufficient to achieve a beneficial response in the subject over time, such as reducing, or alleviating, inSIRS, ipSIRS, or symptoms of a particular stage of ipSIRS. The amount of pharmaceutically active compound to be administered may depend on the subject being treated, including its age, sex, weight and general health. In this regard, the precise amount of active compound for administration will depend on the judgment of the practitioner. In determining the effective amount of the active compound to be administered in treating or preventing inSIRS, ipSIRS, or a particular stage of ipSIRS, a medical practitioner or veterinarian can assess the severity of any symptoms associated with the presence of inSIRS, ipSIRS, or a particular stage of ipSIRS, including inflammation, blood pressure abnormalities, tachycardia, tachypnea, chills, vomiting, diarrhea, rash, headache, obnubilation, muscle pain, spasticity. In any event, one skilled in the art can readily determine suitable dosages and suitable treatment regimens for the therapeutic agents without undue experimentation.

The therapeutic agent may be administered in conjunction with adjuvant (palliative) therapy to increase oxygen supply to the major organs, to increase blood flow to the major organs, and/or to reduce inflammatory responses. Illustrative examples of such adjunctive therapies include non-steroidal anti-inflammatory drugs (NSAIDs), intravenous saline, and oxygen therapy.

In particular embodiments of the invention, the use of the methods, devices and kits described herein above and elsewhere in methods for treating, preventing or inhibiting the development of at least one condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS (e.g., mild sepsis, severe sepsis or septic shock) in a subject is contemplated. These methods (also referred to herein as "methods of treatment") generally include: (a) determining a plurality of IRS biomarker values, each IRS biomarker value indicating a value measured or derived for at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarker of a biological subject; (b) determining an indicator using a combination of a plurality of IRS biomarker values, the indicator being at least partially indicative of the presence, absence or extent of at least one condition, wherein: (i) at least two (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10 or more) IRS biomarkers have a cross-correlation for at least one condition that lies within a cross-correlation range, the cross-correlation range being between ± 0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence, or extent of the at least one condition, the performance threshold being indicative of an interpretation variance of at least 0.3; and (c) administering to the subject an effective amount of an agent that treats or ameliorates symptoms of inSIRS or reverses or inhibits the development of inSIRS based on the indicator indicating the presence of inSIRS or a specific stage of ipSIRS based on the indicator indicating the presence of ipSIRS or a specific stage of ipSIRS, administering to the subject an effective amount of an agent that treats or ameliorates symptoms of ipSIRS or a specific stage of ipSIRS or reverses or inhibits the development of ipSIRS or a specific stage of ipSIRS.

In an advantageous embodiment, the method of treatment comprises: (1) determining a plurality of measured IRS biomarker values, each measured IRS biomarker value being a measured value of an IRS biomarker of the biological subject; and (2) determining at least one derived IRS biomarker value indicative of a value of a corresponding derived IRS biomarker for at least one application function of the measured IRS biomarker values. The function suitably comprises at least one of: (a) multiplying the two IRS biomarker values; (b) dividing the two IRS biomarker values; (c) adding the two IRS biomarker values; (d) subtracting the two IRS biomarker values; (e) a weighted sum of at least two IRS biomarker values; (f) a logarithmic sum of at least two IRS biomarker values; and (g) a sigmoid function of at least two IRS biomarker values.

In some embodiments, the methods, devices, and kits of the invention are used to monitor, treat, and manage conditions that can lead to inSIRS or ipSIRS, illustrative examples of which include placental retention, meningitis, endometriosis, shock, toxic shock (i.e., the sequelae of tampon use), gastroenteritis, appendicitis, ulcerative colitis, crohn's disease, inflammatory bowel disease, gastrointestinal syndrome, liver failure and cirrhosis, neonatal colostrum metastasis failure, ischemia (in any organ), bacteremia, infections in body cavities such as the peritoneum, pericardium, tendon sheaths, and pleural cavity, burns, severe trauma, excessive exercise or stress, hemodialysis, conditions including intolerable distress (e.g., pancreatitis, kidney stones), surgery, and non-healing injuries. In these embodiments, the methods or kits of the invention are generally used at a frequency effective to monitor early development of inSIRS, ipSIRS, or a particular stage of ipSIRS, thereby enabling early therapeutic intervention and treatment of the condition. In an illustrative example, the diagnostic method or kit is used as follows: at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, or 24 hour intervals or at least 1, 2, 3, 4, 5, or 6 day intervals, or at least weekly, biweekly, or monthly.

The invention may be practiced in the field of predictive medicine for the purpose of diagnosing or monitoring the presence or development of a condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS in a subject, and/or monitoring response to therapeutic efficacy.

The biomarker signature and corresponding indicator of the present invention also enable the determination of endpoints in pharmacological co-translation studies. For example, a clinical trial may take months or even years to establish pharmacological parameters for an agent used in treating or preventing inSIRS, ipSIRS, or a particular stage of ipSIRS (e.g., mild sepsis, severe sepsis, and septic shock). However, these parameters may be correlated with a biomarker signature and corresponding indicator of a health state (e.g., health condition). Thus, clinical trials can be accelerated by selecting treatment regimens (e.g., agents and drug parameters) that result in biomarker signatures associated with a desired health state (e.g., health condition). This can be determined, for example, by: a) determining a plurality of IRS biomarker values, each IRS biomarker value indicating a value measured or derived from at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarker to a biological subject following treatment with a treatment regimen; (b) determining an indicator using a combination of a plurality of IRS biomarker values, the indicator being at least partially indicative of the presence, absence or extent of at least one condition selected from a healthy condition, inSIRS, ipSIRS or a particular stage of ipSIRS, wherein: (i) at least two (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10 or more) IRS biomarkers have a cross-correlation for at least one condition within a cross-correlation range, the cross-correlation range being between ± 0.9; and (ii) the indicator has a performance value greater than or equal to a performance threshold representing the ability of the indicator to diagnose the presence, absence or extent of the at least one condition or to provide a prognosis of the at least one condition, the performance threshold being indicative of an interpretation variance of at least 0.3; and (c) determining that the treatment regimen is effective to change the health state of the subject to the desired health state (e.g., health state) based on the indicator indicating the presence of a health condition or a lesser degree of the presence of a condition in the subject relative to the degree of the condition prior to treatment with the treatment regimen. As used herein, the term "degree" refers to the degree or stage of a condition. Thus, for example, mild sepsis is a stage or extent of sepsis that is lower than severe sepsis. Similarly, severe sepsis is a stage or extent of sepsis below septic shock. Accordingly, this aspect of the invention advantageously provides a method of monitoring the efficacy of a particular therapeutic regimen in a subject who has been diagnosed with a condition selected from inSIRS, ipSIRS or a particular stage of ipSIRS (e.g. in the context of a clinical trial). These methods utilize measured or derived IRS biomarker values that are correlated with treatment efficacy, for example, to determine whether the measured or derived IRS biomarker values of a subject undergoing treatment partially or completely return to normal or otherwise exhibit changes correlated with responsiveness to therapy during the course of therapy or after therapy.

As used herein, the term "treatment regimen" refers to prophylactic and/or therapeutic (i.e., after the onset of a specified condition) treatment, unless the context clearly indicates otherwise. The term "treatment regimen" includes natural substances and agents (i.e., "drugs") as well as any other treatment regimen, including, but not limited to, dietary treatments, physical or exercise regimens, surgery, and combinations thereof.

Accordingly, the present invention provides methods of correlating biomarker signatures to an effective treatment regimen for a condition selected from inSIRS, ipSIRS, or a particular stage of ipSIRS (e.g., mild sepsis, severe sepsis, and septic shock), wherein the methods generally comprise: (a) determining a biomarker signature defining a combination of at least two (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarker values corresponding to at least two IRS biomarker values measurable to or derived from a biological subject having the condition and for which an effective treatment has been identified, wherein: (i) the at least two IRS biomarkers have a cross-correlation for the condition within a cross-correlation range, the cross-correlation range being between ± 0.9; and (ii) the combination of at least two biomarker values has a performance value greater than or equal to a performance threshold representing the ability of the combination of at least two biomarker values to diagnose the presence, absence or extent of the condition or to provide a prognosis of the condition, the performance threshold being indicative of an interpretation variance of at least 0.3; and (b) correlating the biomarker signatures to determine an effective treatment regimen for the condition. The term "associating" generally refers to determining a relationship between one type of data and another or state. In particular embodiments, the indicator or biomarker signature is correlated to a global probability or a particular outcome using a Receiver Operating Characteristic (ROC) curve.

The invention also provides methods of determining whether a treatment regimen is effective for treating a subject having a condition selected from inSIRS, ipSIRS, or a particular stage of ipSIRS (e.g., mild sepsis, severe sepsis, and septic shock). These methods generally include: (a) determining a plurality of post-treatment IRS biomarker values, each post-treatment IRS biomarker value indicating a value measured or derived for at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarker in a biological subject following treatment with the treatment regimen; (b) determining a post-treatment indicator using a combination of the plurality of post-treatment IRS biomarker values, the post-treatment indicator at least partially indicative of the presence, absence, or extent of at least one condition selected from a healthy condition, an insiRS, an ipsIRS, or a particular stage of ipsIRS, wherein: (i) the at least two (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10 or more) IRS biomarkers have a cross-correlation for at least one condition within a cross-correlation range, the cross-correlation range being between ± 0.9; and (ii) the post-treatment indicator has a performance value greater than or equal to a performance threshold representing the ability of the post-treatment indicator to diagnose the presence, absence, or extent of the at least one condition, the performance threshold indicating an interpretation variance of at least 0.3; wherein the post-treatment indicator indicates whether the treatment regimen is effective to treat the condition in the subject based on: the post-treatment indicator indicates the presence of a healthy condition or a lesser degree of the condition relative to the degree of the condition in the subject prior to treatment with the treatment regimen.

The present invention can also be practiced to assess whether a subject is responding (i.e., a positive response) or not responding (i.e., a negative response) to a treatment regimen or has a side effect on a treatment regimen. This aspect of the invention provides a method of correlating biomarker signatures with positive or negative responses or side effects to a treatment regimen, the method generally comprising: (a) determining a biomarker signature defining a combination of at least two (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarker values corresponding to at least two IRS biomarker values that can be measured to or derived from a biological subject after initiation of the treatment regimen, wherein: (i) the at least two IRS biomarkers have a cross-correlation with respect to at least one condition selected from a healthy condition, inSIRS, ipSIRS or a specific stage of ipSIRS within a cross-correlation range, the cross-correlation range being between ± 0.9; and (ii) the combination of at least two biomarker values has a performance value greater than or equal to a performance threshold representing the ability of the combination of at least two biomarker values to diagnose the presence, absence, or degree of the at least one condition or to provide a prognosis of the at least one condition, the performance threshold being indicative of an interpretation variance of at least 0.3; and (b) correlating the biomarker signatures to determine whether a positive or negative response to the treatment regimen. As used herein, the term "positive response" means that the result of a treatment regimen includes some clinically significant benefit, such as prevention or reduction of the severity of the condition, prevention or reduction of symptoms of the condition, or slowing of the progression of the condition. In contrast, the term "negative response" means that the treatment regimen does not provide a clinically significant benefit, such as prevention or reduction of the severity of the condition, prevention or reduction of symptoms of the condition, or increasing the rate of progression of the condition.

The invention also includes methods of determining a positive or negative response to a treatment regimen and/or a side effect on a treatment regimen by a subject having a condition selected from inSIRS, ipSIRS, or a particular stage of ipSIRS. These methods generally include: (a) correlating a reference biomarker signature with a positive or negative response or side effect to the treatment regimen, wherein the biomarker signature defines a combination of at least two (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarker values corresponding to at least two IRS biomarker values measured against or derived from a control biological subject or control group, wherein: (i) the at least two IRS biomarkers have a cross-correlation with respect to at least one condition selected from a healthy condition, inSIRS, ipSIRS or a specific stage of ipSIRS within a cross-correlation range, the cross-correlation range being between ± 0.9; and (ii) the combination of at least two biomarker values has a performance value greater than or equal to a performance threshold representing the ability of the combination of at least two biomarker values to diagnose the presence, absence, or degree of the at least one condition or to provide a prognosis of the at least one condition, the performance threshold being indicative of an interpretation variance of at least 0.3; (b) determining a sample biomarker signature defining a combination of at least two (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarker values corresponding to at least two IRS biomarker values measured to or derived from a biological subject after initiation of the treatment regimen, wherein: (i) the at least two IRS biomarkers have a cross-correlation with respect to at least one condition selected from a healthy condition, inSIRS, ipSIRS or a specific stage of ipSIRS within a cross-correlation range, the cross-correlation range being between ± 0.9; and (ii) the combination of at least two biomarker values has a performance value greater than or equal to a performance threshold representing the ability of the combination of at least two biomarker values to diagnose the presence, absence, or degree of the at least one condition or to provide a prognosis of the at least one condition, the performance threshold being indicative of an interpretation variance of at least 0.3; wherein the sample biomarker signature indicates whether the subject is responding positively or negatively to the treatment regimen and/or developing side effects from the treatment regimen based on: the reference biomarker identifies a correlation with a positive or negative response or side effect to the treatment regimen.

In related embodiments, the invention also contemplates methods of determining a positive or negative response to a treatment regimen and/or a side effect to a treatment regimen by a biological subject. These methods generally include: (a) determining a sample biomarker signature that defines a combination of at least two (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) IRS biomarker values corresponding to at least two IRS biomarker values measured to or derived from a biological subject after initiation of the treatment regimen, wherein: (i) the at least two IRS biomarkers have a cross-correlation with respect to at least one condition selected from a healthy condition, inSIRS, ipSIRS or a specific stage of ipSIRS within a cross-correlation range, the cross-correlation range being between ± 0.9; and (ii) the combination of at least two biomarker values has a performance value greater than or equal to a performance threshold representing the ability of the combination of at least two biomarker values to diagnose the presence, absence or extent of the at least one condition or to provide a prognosis of the at least one condition, the performance threshold being indicative of an interpretation variance of at least 0.3, wherein the sample biomarker signature is associated with a positive or negative response to the treatment regimen and/or a side effect from the treatment regimen; and (b) determining whether the subject is responding positively or negatively to the treatment regimen and/or developing side effects from the treatment regimen based on the sample biomarker signature.

This above method can be practiced to identify responders or non-responders relatively early in the course of treatment, i.e., prior to clinical manifestation of efficacy. In this manner, the treatment regimen may optionally be terminated, a different treatment regimen may be administered and/or a supplemental therapy may be administered. Thus, in some embodiments, the sample IRS biomarker signature is obtained within about 2 hours, 4 hours, 6 hours, 12 hours, 1 day, 2 days, 3 days, 4 days, 5 days, 1 week, 2 weeks, 3 weeks, 4 weeks, 6 weeks, 8 weeks, 10 weeks, 12 weeks, 4 months, 6 months, or longer of initiating therapy.

A number of non-limiting exemplary markers for use in diagnosing respective conditions will now be described. For illustrative purposes, the process described above is used to select biomarkers that provide the theoretically best diagnostic biomarker selected from the group consisting of measured biomarkers and/or derived biomarkers. After this, the other measured biomarkers and/or derived biomarkers are grouped based on their correlation with the best diagnostic biomarker, and the ability of the biomarkers to serve as diagnostic markers within these groups is then evaluated.

The results, set forth in detail below, emphasize that the resulting identification provides the discriminative power required for use in diagnosing the presence, absence, degree or prognosis of at least one condition in a biological subject as long as the criteria described above are met.

Identity derivation

An illustrative process for identifying mRNA biomarkers for use in diagnostic algorithms will now be described.

SUMMARY

Peripheral blood samples were obtained from healthy controls and patients who were retrospectively diagnosed by a panel of physicians as having inSIRS or ipSIRS (blood culture positive). ipSIRS patients are further retrospectively classified as "mild", "severe" or "shock" based on clinical parameters. Then, total RNA from patient samples is analyzed in gene expressionAnd/or quantitative pcr (qpcr)). Gene expression data is analyzed using a variety of statistical methods to identify individual markers and derived markers. The derived markers are grouped based on how they correlate with each of the markers that perform the best (AUC-based) ratio. This ratio method provides the best diagnostic ability for AUC for the following separations: health conditions and post-surgical (PS) (also referred to herein as "inSIRS") conditions; health and sepsis (also referred to herein as "ipSIRS"); inSIRS and ipSIRS; mild ipSIRS and severe ipSIRS; mild ipSIRS and septic shock; as well as severe ipSIRS and septic shock.

Clinical trial

Clinical trials were conducted to determine whether certain mRNA transcripts could differentiate between healthy controls and multiple patient groups, as well as within patient groups. Intensive care sepsis patients, postoperative patients and inSIRS patients, as well as healthy controls, are expected to be enrolled and participate in a single visit where blood is collected for gene expression and mRNA analysis using Affymetrix exome arrays and/or quantitative real-time PCR (qRT-PCR). Definitive diagnosis of infection-positive SIRS (mild, severe or shock) or inSIRS is unlikely to be known at the time the patient is enrolled, and thus validation and assignment cohorts for diagnosis of patients are performed retrospectively.

Patients with clinical signs and/or symptoms of ipSIRS or inSIRS agree and are enrolled into the study as soon as possible after they have been identified, in most cases within 24 hours of admission. Final assessment of whether participants had inSIRS, ipSIRS (mild, severe or shock) was performed retrospectively when clinical information and blood culture results became available.

Study participants were all over 18 years of age and they or their agent decision makers identified and dated in clinical trial information sheets and informed consent. All control participants were considered healthy based on simple physical examinations and their medical history at the time of enrollment.

If a patient presents signs and symptoms of inSIRS or ipSIRS upon entry into the ICU (using criteria based on the American College of physicians) and the Critical Care medical Society standards definitions, the patient or their agent decision maker is provided an opportunity to participate in the study. That is, inSIRS and ipSIRS participants require clinical conditions over the past 24 hours that include two or more of the followingThe variable combination of (a): body temperature>38 ℃ or<36 ℃; heart rate>90 heartbeats/min; respiration rate >20 breaths/min or<4.3kPa(<32mm Hg) of PaCO₂(ii) a And white blood cell count<4,000 cells/mm³(<4x10⁹Individual cell/L) or>12,000 cells/mm³(>12x 10⁹cell/L) or>Evidence for 10% immature neutrophils (in ribbon form). If they have any chronic systemic immunoinflammatory disorder, including SLE, crohn's disease, Insulin Dependent Diabetes Mellitus (IDDM); are transplant recipients or are currently receiving chemotherapy for cancer, and are excluded. Most patients have other potential comorbidities. All study participants were 18 years old or older and had a body mass index of less than 40.

Demographics, vital sign measurements (blood pressure, heart rate, respiration rate, oxygen saturation, body temperature), hematology (complete blood count), clinical chemistry (urine, electrolytes, liver function enzymes, blood glucose), and microbiological status were recorded.

Blood was collected into PAXgene tubes for the purpose of extracting high quality RNA^TM(PreAnalytix Inc., Valencia, Calif., USA). For bacterial culture, blood was collected into BacTec Plus Aerobic (10mL) and BacTec Plus Anaerobic (10mL) tubes (Becton Dickinson) for detection of Aerobic and Anaerobic bacterial growth, respectively.

PAXgene blood RNA kits available from Qiagen Inc. (Valencia, CA, USA) were used to isolate total RNA from PAXgene tubes. The separation begins with a centrifugation step to pellet the nucleic acids in the PAXgene blood RNA tube. The pellet was washed and resuspended and incubated with proteinase K in optimized buffer to digest the protein. Additional centrifugation was performed to remove remaining cell debris and the supernatant was transferred to a fresh microcentrifuge tube. Ethanol was added to adjust the binding conditions and the lysate was applied to a PAXgene RNA spin column. During brief centrifugation, RNA is selectively bound to the silica gel membrane as contaminants pass through. The remaining contaminants were removed in three high efficiency wash steps and the RNA was then eluted in buffer BR 5. Determination of RNA quantity and quality was performed using an Agilent bioanalyzer and absorbance 260/280 ratio was performed using a spectrophotometer.

Sample processing

Measurement of specific mRNA levels in a tissue sample can be accomplished using a variety of techniques. A common and readily available technique covering most of the known human mRNA is using Affymetrix technologyAnd (6) analyzing. Details regarding the techniques and methods can be found at www.affymetrix.com.The assay has the advantage of being able to analyse thousands of RNA transcripts simultaneously. Another common and readily available technique is qPCR (quantitative polymerase chain reaction), which has the advantage of being able to analyze and simultaneously quantify hundreds of RNA transcripts in real time. Regarding one of these technologiesDetails of the chemistry and methods, which can be found at the Life Technologies website, include published protocols entitled "Protocol: Introduction to TaqMan SYBR Green chemistry for Real-Time PCR" and "TaqMan Gene Expression Assays Protocol".And qPCR are both used in the discovery and proof of concept stage for biomarker identification. qPCR was used exclusively for biomarker viability testing.

Analysis, data interpretation and selection of biomarkers and derived biomarkers

Healthy control vs. insiRS

A list of 941 mRNA individual markers with an AUC of at least 0.7 was generated to distinguish between healthy and inSIRS two conditions. Figure 8A plots these markers against AUC, and figure 8B is a box and whisker plot of the best mRNA biomarkers for the isolation of the two conditions (AGFG 1-ArfGAP with FG repeat 1 (ArfGAP with FG repeat 1)). When this single mRNA biomarker was used, the conditions of health and inSIRS were completely separated.

At least 1000 derived markers (ratios) with an AUC of 1.0 were generated from 941 individual markers. A graphical representation of the AUC of these derived markers for isolating healthy and inSIRS conditions is shown in figure 8C, with the best performing ratio shown as box-and-whisker plot in figure 8D. The AUC for AGFG1 and PVRIG (comprising poliovirus receptor-associated immunoglobulin domain containing) has a ratio of 1.0.

All 941 individual markers were then divided into two groups-those associated with AGFG1 and those associated with PVRIG. Fig. 8E and 8F show the correlation between the two groups based on their similarity to AGFG1 or PVRIG. In these figures, the groups are referred to as group 1 (AGFG1) or group 2 (PVRIG). As can be seen, each "group" contains those tokens that are most highly correlated with each other.

Markers in each "group" also correlated strongly (as shown by AUC greater than 0.7 for all markers) with the condition studied (in this case healthy versus insirs (ps)), as shown in fig. 8G and 8H.

Better AUC for isolating healthy and inSIRS was obtained by selecting mRNA from these two groups to create derived markers compared to when selecting markers from within the groups (p <2.558e-13), as demonstrated in fig. 8I, which shows a larger overall AUC compared to when using markers from group 1 or group 2 alone. The mean AUC for markers derived from group 1 and group 2 was higher than 0.97, while the mean AUC for markers derived from group 1 or group 2 alone was less than 0.9.

Healthy control vs. ipSIRS

A list of 941 mRNA individual markers with an AUC of at least 0.7 was generated for isolating both conditions of health and ipSIRS. Figure 9A plots these markers against AUC, and figure 9B is a box and whisker plot of the best mRNA biomarker (LTBP 3-potential transforming growth factor beta binding protein 3) for isolating the two conditions. When this single mRNA biomarker was used, the conditions of health and ipSIRS were completely separated.

At least 1000 derived markers (ratios) with an AUC of 1.0 were generated from 941 individual markers. A graphical representation of the AUC of these derived markers for isolating healthy and inSIRS conditions is shown in figure 9C, with the best performing ratio shown as box-and-whisker plots in figure 9D. The AUC for LTBP3 and LPHN1 (arachnoid receptor 1 (laterolilin 1)) was 1.0 as a ratio.

All 941 individual markers were then divided into two groups-those associated with LTBP3 and those associated with LPHN 1. The two graphs in fig. 9E and 9F show the correlation between the two groups based on their similarity to LTBP3 or LPHN 1. As can be seen, each "group" contains those tokens that are most highly correlated with each other.

The markers in each "group" were also strongly correlated with the condition studied (in this case healthy versus ipSIRS (sepsis)) as shown by AUC greater than 0.7 for all markers, as shown in fig. 9G and 9H.

By selecting mrnas from these two groups to create derived markers, a better AUC was obtained for isolating healthy and ipSIRS (p <2.2e-16) than when selecting markers from within the groups, as demonstrated in fig. 9I, which shows an improved AUC compared to using markers from either group 1 or group 2 alone. The mean AUC for markers derived from group 1 and group 2 was higher than 0.97, while the mean AUC for markers derived from group 1 or group 2 alone was less than 0.8.

insiRS vs. ipSIRS

A list of 359 mRNA biomarkers with AUC of at least 0.7 was generated for isolating the two conditions of inSIRS and ipSIRS. Fig. 10A plots these markers against AUC, and fig. 10B is a box and whisker plot of the best mRNA biomarker (PIWIL 4-piwi-like RNA mediated gene silencing 4) for isolating these two conditions. When this single mRNA biomarker was used, the conditions of insirs (ps) and ipSIRS (sepsis) were completely separated.

At least 1000 derived markers (ratios) with AUC greater than 0.9 were generated from 359 individual markers. A plot of the AUC of these derived markers for the condition separating inSIRS and ipSIRS is shown in figure 10C, the ratio of best performing PLA2G7 (phospholipase a2, group VII, (platelet activating factor acetylhydrolase, plasma)) and PLAC8 (placenta-specific 8) is shown as box and whisker plot in figure 10D.

All 359 individual markers were then divided into two groups-those associated with PLA2G7 and those associated with PLAC 8. The graph in figure 10E shows that the markers in each "group" were strongly correlated (as shown by AUC greater than 0.7 for all markers) with the condition being studied (in this case insirs (ps) versus ipSIRS (sepsis)).

By selecting mRNA from these two groups to create derived markers, better AUC for isolating insirs (ps) and ipSIRS (sepsis) was obtained when selecting markers from within the groups (p <5.78e-5) than when using markers from group 1 or group 2 alone, as demonstrated in figure 10F. The mean AUC for markers derived from group 1 and group 2 was higher than 0.80, while the mean AUC for markers derived from group 1 or group 2 alone was less than 0.8.

In alternative embodiments, the markers are divided into four groups — those associated with CEACAM4 (bucket 3), those associated with LAMP1 (bucket 4), those associated with PLA2G7 (bucket 1), and those associated with PLAC8 (bucket 2). The graph in figure 10G shows that the markers in each "group" were strongly correlated with the condition being studied (i.e. insirs (ps) versus ipSIRS (sepsis)) (as shown by AUC greater than 0.7 for all markers). As with the two-bucket (two-bucket) embodiment discussed above, mRNA was selected from four groups to create derived markers that resulted in an overall better AUC of isolated insirs (ps) and ipSIRS (sepsis) than when using markers from any of groups 1 to 4 (p <0.2564, as shown in fig. 10H. the mean AUC for markers derived from groups 1 to 4 was higher than 0.8, while the mean AUC for markers derived from any of groups 1 to 4 alone was less than 0.8, compared to when selecting markers from within groups.

Mild ipSIRS vs severe ipSIRS

A list of 66 mRNA individual markers with an AUC of at least 0.7 was generated for isolating the two conditions of mild ipSIRS and severe ipSIRS. Figure 11A plots these markers against AUC, and figure 11B is a box and whisker plot of the best mRNA biomarker (N4BP2L 2-NEDD 4binding protein 2-like 2(NEDD4binding protein 2-like 2)) for the isolation of the two conditions. When using this single mRNA biomarker, the status of mild ipSIRS and severe ipSIRS was well separated.

At least 1000 derived markers (ratios) with an AUC of 0.87 were generated from the 66 individual markers. A graphical representation of the AUC of these derived markers for the condition separating mild ipSIRS and severe ipSIRS is shown in fig. 11C, with the best performing ratio shown as box and whisker plot in fig. 11D. The AUC for N4BP2L2 and ZC3H11A (CCCH-type zinc finger protein structure (zinc finger CCCH-type binding 11A) containing 11A) was 0.983 as a ratio.

All 66 individual markers were then divided into two groups-those related to N4BP2L2 and those related to ZC3H 11A. Two of fig. 11E and 11F show the correlation between two groups based on their similarity to N4BP2L2 or ZC3H 11A. In these figures, the groups are referred to as group 1 (N4BP2L2) or group 2 (ZC3H 11A). As can be seen, each "group" contains those tokens that are most highly correlated with each other. The markers in each "group" were also strongly associated with the condition being studied (in this case mild ipSIRS versus severe ipSIRS) (as shown by AUC greater than 0.7 for all markers).

By selecting mrnas from these two groups to create derived markers, a better AUC for distinguishing mild ipSIRS from severe ipSIRS was obtained (p <2.2e-16) than when using markers from either group 1 or group 2 alone than when selecting markers from within the groups, as shown in figure 11I. The mean AUC for markers derived from group 1 and group 2 was higher than 0.89, while the mean AUC for markers derived from group 1 or group 2 alone was less than 0.6.

Mild ipSIRS to ipSIRS shock

A list of 48 mRNA individual markers with an AUC of at least 0.7 was generated for isolating the two conditions of mild ipSIRS and ipSIRS-shock. FIG. 12A plots these markers against AUC, and FIG. 12B is a box and whisker plot of the best mRNA biomarkers (CD 6-CD 6 molecules) for the separation of the two conditions. When using this single mRNA biomarker, the conditions of mild ipSIRS and ipSIRS shock were well separated.

At least 1000 derived markers (ratios) with an AUC of at least 0.793 were generated from the 48 individual markers. A graphical representation of the AUC of these derived markers for conditions separating mild ipSIRS and ipSIRS shock is shown in figure 12C, with the best performing ratio shown as box and whisker plot in figure 12D. AUC for VAMP2 and UBAP1 (ubiquitin related protein 1) was 0.978 as a ratio.

All 48 individual markers were then divided into two groups-those associated with VAMP2 and those associated with UBAP 1. Both of fig. 12E and 12F show the correlation between the two groups based on their similarity to VAMP2 or UBAP 1. In these figures, the group is referred to as group 1 (VAMP2) or group 2 (UBAP 1). As can be seen, each "group" contains those tokens that are most highly correlated with each other. The markers in each "group" also correlated strongly (as shown by AUC greater than 0.7 for all markers) with the condition being studied (in this case mild ipSIRS versus ipSIRS-shock).

By selecting mrnas from these two groups to create derived markers, a better AUC for separating mild ipSIRS and ipSIRS-shock was obtained (p <2.2e-16) than when using markers from either group 1 or group 2 alone than when selecting markers from within the groups, as shown in figure 12I. The mean AUC for markers derived from group 1 and group 2 was higher than 0.87, while the mean AUC for markers derived from group 1 or group 2 alone was less than 0.65.

Severe ipSIRS vs ipSIRS shock

A list of 61 mRNA individual markers with an AUC of at least 0.7 was generated for distinguishing between two conditions of severe ipSIRS and ipSIRS-shock. Figure 13A plots these markers against AUC, and figure 13B is a box and whisker plot of the best mRNA biomarker (SIRPG-signaling regulatory protein γ) for isolating the two conditions. When this single mRNA biomarker was used, the conditions of severe ipSIRS and ipSIRS-shock were well separated.

At least 1000 derived markers (ratios) with an AUC of at least 0.821 were generated from 61 individual markers. A graphical representation of the AUC of these derived markers for separating conditions of severe ipSIRS and ipSIRS-shock is shown in figure 13C, with the best performing ratio shown as box and whisker plot in figure 13D. The AUC for GATA3(GATA binding protein 3) and MECOM (MDS1 and EVI1 complex locus) was 0.936 as a ratio.

All 61 individual markers were then divided into two groups-those related to GATA3 and those related to MECOM. Both of fig. 13E and 13F show the correlation between the two groups based on their similarity to GATA3 or MECOM. In these figures, the groups are referred to as group 1 (GATA3) or group 2 (MECOM). As can be seen, each "group" contains those tokens that are most highly correlated with each other. The markers in each "group" also correlated strongly (as shown by AUC greater than 0.7 for all markers) with the condition being studied (in this case severe ipSIRS versus ipSIRS-shock).

By selecting mrnas from these two groups to create derived markers, a better AUC for distinguishing severe ipSIRS and ipSIRS-shock was obtained (p <2.2e-16) than when using markers from either group 1 or group 2 alone than when selecting markers from within the groups, as shown in figure 13I. The mean AUC for markers derived from group 1 and group 2 was higher than 0.82, while the mean AUC for markers derived from group 1 or group 2 alone was less than 0.7.

Method of marking

The use of the markers and resulting markers described above in a patient population and the benefits in distinguishing inSIRS from ipSIRS will now be described.

An assay capable of distinguishing patients with inSIRS and ipSIRS may be used in a plurality of patient populations including:

1) intensive care unit (ICU for internal medicine and surgery)

2) Postoperative and medical ward

3) Emergency room

4) A medical clinic.

Patients admitted to Intensive Care Unit (ICU) usually have ipSIRS, or develop ipSIRS during their ICU stay. The ultimate goal of intensive care is to ensure that patients survive and are transferred to the general ward in the shortest time. Patients in intensive care diagnosed with ipSIRS are often administered many therapeutic compounds-many of them have adverse effects on the immune system and many of them may be counterproductive depending on the severity of ipSIRS (mild sepsis, severe sepsis, septic shock). Regular monitoring of intensive care patients with the biomarkers of the invention will allow medical practitioners to distinguish inSIRS from ipSIRS and determine the stage of ipSIRS and therefore select therapies and patient management procedures when starting and stopping therapy, and ultimately the response to therapy. Thus, the information provided by these biomarkers will allow medical intensive care to customize and modify therapy, ensure patient survival and spend less time in intensive care. Less time in intensive care results in considerable medical cost savings, including through less time and reasonable use and timing of medications.

Surgical and general medical patients develop post-operative inSIRS, usually or as a result of their condition or procedure, and have a higher risk of developing ipSIRS. Therefore, post-operative and medical care in such patients includes monitoring signs of inSIRS and ipSIRS and distinguishing between these two conditions. Treatment and management of inSIRS and ipSIRS patients after surgery and in general wards are different, as inSIRS patients can supply mild anti-inflammatory or antipyretics and ipSIRS patients must start supplying antibiotics as early as possible for best results. Regular monitoring of post-operative and medical patients with the biomarkers of the invention will allow practitioners and medical practitioners to distinguish inSIRS from ipSIRS at an early stage and therefore make informed decisions on the selection of therapies and patient management procedures, as well as on the ultimate response to therapies. Thus, the information provided by these biomarkers will allow medical practitioners to customize and modify therapy, ensure that patients recover quickly from surgery or other conditions, and do not develop ipSIRS. Fewer hospital stays and fewer complications result in considerable medical cost savings, including through less time and reasonable use and timing of medications.

In addition, patients present in the emergency room are often febrile, one of the clinical signs (out of four) of inSIRS. Such patients need to be evaluated to determine whether they have inSIRS or ipSIRS. As mentioned above, the treatment and management of febrile, inSIRS and septic patients is different. For example, patients with fever without other inSIRS clinical signs and no apparent infection sources may be returned home, or provided other non-hospitalized services without further hospitalization. However, patients with fever may have early ipSIRS and not allowing such patients to stay in hospital may put their lives at risk. Since these biomarkers can distinguish inSIRS from ipSIRS, they will allow medical practitioners to triage emergency room patients quickly and efficiently. Making accurate triage decisions ensures that hospitalizations are given to patients who require hospitalization and that other appropriate services are provided to those who do not.

Still further, patients present in medical clinics typically have any of the four clinical signs of inSIRS (increased heart rate, increased respiratory rate, abnormal white blood cell count, fever, or hypothermia). Many different clinical conditions may exist with one of the four clinical signs of inSIRS, and such patients need to be evaluated to determine whether they have inSIRS or ipSIRS and exclude other differential diagnoses. For example, patients with angina may also be accompanied by clinical signs of increased heart rate. The differential diagnosis can be (but is not limited to) appendicitis, urinary calculus, cholecystitis, pancreatitis and enterocolitis. In each of these conditions, it would be important to determine whether a systemic inflammatory response (inSIRS) is present or whether the infection contributes to a systemic response to the condition (ipSIRS). Treatment and management of patients with and without systemic inflammation and/or infection is different. Since these biomarkers can distinguish patients with a systemic inflammatory response to infection from patients with a systemic inflammatory response without infection (inSIRS and ipSIRS) and determine the degree of systemic involvement, their use will allow medical practitioners to determine the next medical procedure to be performed to satisfactorily address the patient's problem.

Determining the extent of systemic inflammation in diseased patients and those with inSIRS and ipSIRS

As mentioned above, patients present in medical clinics typically have any of the four clinical signs of inSIRS. However, many different clinical conditions may exist with one of the four clinical signs of inSIRS, and such patients need to be evaluated to determine whether they have inSIRS (and if so, the degree of inSIRS) or ipSIRS (and if so, the degree of ipSIRS), and to exclude other differential diagnoses.

For example, patients with respiratory distress may be accompanied by clinical signs of increased respiratory rate. Differential diagnosis may be, but is not limited to, asthma, pneumonia, congestive heart failure, physical blockage of the airways, allergic reactions, lung collapse (collapsed lung), pneumothorax. In each of these conditions, it would be important to determine whether a systemic inflammatory response (inSIRS) is present or whether the infection contributes to the condition. Treatment and management of patients with and without systemic inflammation and/or infection is different. Since these biomarkers can distinguish patients with systemic inflammatory response to infection from patients with systemic inflammatory response without infection (inSIRS and ipSIRS) and determine the degree of systemic involvement, their use will allow medical practitioners to determine the next medical procedure to be performed to satisfactorily address the patient's problem. Patients with lung collapse, pneumothorax, or physical obstruction are less likely to have a large systemic inflammatory response, and patients with congestive heart failure, anaphylaxis, or asthma are less likely to have a large systemic inflammatory response due to infection. The extent of both inSIRS and ipSIRS, as indicated by the biomarkers set forth in this patent, also provides the clinician with information about the next treatment and management step. For example, a patient with respiratory distress and a strong biomarker response indicative of ipSIRS may be immediately hospitalized, provided with antibiotics, and subjected to chest X-ray. Patients with respiratory distress and a strong biomarker response indicative of inSIRS rather than ipSIRS may be hospitalized immediately and chest X-ray performed along with other research diagnostic procedures such as MRI, ECG, and angiography. Patients with a short history of respiratory distress without inSIRS or ipSIRS may undergo further examination at the local clinic without hospitalization.

Again, and as mentioned above, patients present in the emergency room are often febrile, one of the clinical signs (out of four) of inSIRS. Such patients need to be evaluated to determine whether they have inSIRS or ipSIRS. Furthermore, it is important to determine how uncomfortable they are to be able to make a judgment warning whether to allow the patient to be hospitalized. Making accurate triage decisions ensures that hospitalizations are given to patients who require hospitalization and other appropriate services are provided to those who do not.

Patients in ICUs often have inSIRS and ipSIRS, and it is important to distinguish between these two conditions because the treatment regimens are different. In patients with inSIRS, it is important to determine the extent of the inflammatory response so that appropriate treatment and management regimens can be properly implemented. For example, a patient newly determined to have inSIRS that is not widespread may be able to be provided with mild drugs such as non-steroidal anti-inflammatory drugs. Patients newly identified as having extensive inSIRS (e.g., trauma) may require stronger anti-inflammatory drugs such as steroids to reduce the potential impact of the side effects of inflammation (swelling). In patients with ipSIRS, it is also important to determine the extent of the inflammatory response to the infection so that appropriate treatment and management regimens can be properly implemented or discontinued. For example, for patients with a persistent strong ipSIRS response, clinicians may consider changes, or add antibiotic treatment regimens, in the absence of traditional bacterial culture and sensitivity outcomes. Furthermore, patients known to have ipSIRS and who are always undergoing antimicrobial therapy for extended periods but have shown (by using biomarker testing) that they no longer have inSIRS or ipSIRS can therefore safely discontinue intravenous antibiotics.

Determining severity of ipSIRS

Patients admitted to Intensive Care Unit (ICU) usually have ipSIRS, or develop ipSIRS during ICU stays. It is known that the severity of sepsis can be considered continuous, from less severe, or sepsis to more severe or severe sepsis to most severe or septic shock. More severe sepsis (ipSIRS) requires more urgent, immediate, and customized intervention (although all are acute conditions) than sepsis. Patients in intensive care diagnosed with ipSIRS are often administered many therapeutic compounds-many of them have adverse effects on the immune system, and many of them may be counterproductive depending on the severity of ipSIRS (sepsis, severe sepsis, septic shock). Monitoring an intensive care patient with the biomarkers of the invention on a regular basis will allow a medical practitioner to determine the severity of ipSIRS (mild, severe or shock) and therefore select therapy and patient management procedures, and ultimately response to therapy. Thus, the information provided by these biomarkers disclosed herein will allow medical practitioners to customize, modify, or stop therapy and/or care, ensure patient survival, and spend less time in intensive care. Less time in intensive care results in significant savings in medical costs, including through less time and reasonable use and timing of medications.

First exemplary workflow

A first example workflow will now be described. Depending on the availability of the automation platform, the workflow includes up to seven steps. The determination of the amount of each transcript in a sample was determined using quantitative, real-time based detection of fluorescence on a qRT-PCR instrument (e.g., Applied Biosystems 7500Fast Dx real-time PCR instrument, Applied Biosystems, Foster City, CA, Cat. No. 440685; K082562). Transcripts were each amplified, detected and quantified in separate reaction wells using probes visualized (by way of example) in FAM channels. The reported scores were calculated using explanatory software provided separately for the kit but designed to integrate with the RT-PCR machine.

Workflow the manual handling and use of pre-prepared kits are described below.

Before analysis

Blood collection

Total RNA isolation

Analysis of

Reverse transcription (production of cDNA)

qPCR preparation

qPCR

Software, interpretation of results and quality control

And (6) outputting.

Kit contents

Diluent liquid

RT buffer

RT enzyme mixture

qPCR buffer

Primer/probe mixtures

AmpliTaq(or the like)

High positive control

Low positive control

Negative control

Blood collection

The sample is PAXgene bloodLiquid RNA(Qiagen, kit catalog No. 762164; Becton Dickinson, Collection tube catalog No. 762165; K042613) The inner PAXgene collection tube collected 2.5mL of blood sample collected by venipuncture. An alternative collecting tube is(Life Technologies)。

Total RNA isolation

Blood (2.5mL) collected into PAXgene RNA tubes was processed according to the manufacturer's instructions. Briefly, the method is used in PAXgene^TMPAXgene in the blood RNA System (Qiagen, kit Cat No. 762164; Becton Dickinson, Collection tube Cat No. 762165; K042613)^TMThe collection tube collected 2.5mL of blood sample by venipuncture. Total RNA isolation was used in PAXgene^TMBlood RNA kit (PAXgene)^TMComponents of the blood RNA system). The extracted RNA is then tested for purity and yield (e.g., by use of(Thermo Scientific) run A_260/280Ratio) for which the minimum quality must be (ratio)>1.6). The RNA should be adjusted in concentration to allow for a constant input volume for the reverse transcription reaction (below). The RNA should be processed immediately or stored in a single use volume at or below-70 ℃ for later processing.

Reverse transcription

Based on the plate map and the information provided directly below, the appropriate number of reaction equivalents to be prepared (master mix formulation) is determined. Each clinical specimen was run in singleton mode.

a) Each batch run must include the following samples:

b) High control, low control, negative control, and no template control (test dilutions, not samples) each in singleton format

Programming ABI 7500Fast Dx instrument as detailed below.

a) The software is started.

b) Clicking to create a new document

c) In the new document guide, the following options are selected:

i) and (3) determination: standard curve (Absolute ration)

ii) a vessel: transparent 96-well

iii) a template: blank document (or select laboratory defined template)

iv) operating mode: standard 7500

v) an operator: input operator abbreviations

vi) plate name: [ Default ]

f) Click completion

g) Selecting the Instrument tab in the upper left corner

h) In the thermocycler protocol area, the thermogram tab, the following time is entered:

i) at 25 ℃ for 10 minutes

ii)45 ℃ for 45 minutes

iii)93 ℃ for 10 minutes

iv) holding at 25 ℃ for 60 minutes

In the template-free region, the test dilutions and RT-qPCR test RT buffer were removed to room temperature for thawing. RT-qPCR test RT enzyme mix was retained in the refrigerator and/or on the freezer module.

In the template-free area, the master mix was assembled in the order listed below.

RT master mix-calculation:

the master mix was gently vortexed and then pulsed centrifuged. An appropriate volume (5 μ L) of RT master mix was added to each well at room temperature.

Clinical samples and control RNA were removed for thawing. (if the sample routinely requires longer thawing, this step can be moved upstream in the validation method.)

Vortex clinical samples and control RNA, then pulse centrifugation add 10 μ Ι _ of control RNA or RT-qPCR test dilution to each respective control well or negative well.

mu.L of sample RNA was added to each respective sample well (150 ng total input for RT; OD₂₆₀/OD₂₈₀The ratio is greater than 1.6). 10 μ L RT-qPCR test dilutions were added to each NTC well.

Note that: the final reaction volume per well was 15. mu.L.

Mixing by gentle blowing and beating. Avoiding the formation of bubbles in the pores.

The hole was covered with a seal.

Centrifuge the plate to remove any air bubbles (at 400x g, 1 min).

Transfer quickly to preprogrammed ABI 7500Fast Dx instrument as detailed above.

The click starts. Click to save and continue. Before leaving the instrument, it is recommended to verify that the run was successfully started by displaying the time under the estimated remaining time.

qPCR master mix can be prepared to roughly coincide with the end of the RT reaction. For example, this is initiated about 15 minutes before. See below.

When RT was complete (i.e. dwell at 25 ℃; hold was stopped at any time before completion for 60 minutes), the plates were centrifuged to collect the clot (at 400x g, 1 minute).

qPCR preparation

Based on the plate map and the information provided in the RT preparation above, the appropriate number of reaction equivalents to be prepared (master mix formulation) is determined.

ABI 7500Fast Dx with the following settings was programmed.

a) The software is started.

b) Single click creation of new document

c) In the new document guide, the following options are selected:

i) and (3) determination: standard curve (Absolute ration)

ii) a vessel: transparent 96-well

iii) a template: blank document (or select laboratory defined template)

iv) operating mode: standard 7500

v) an operator: input operator abbreviations

vi) plate name: input the desired file name

d) Click on next step

e) In selecting the test object dialog:

i) select the detector for the first biomarker and then click to add > >.

ii) selecting the detector second biomarker and then click-add > >, and so on.

iii) passive reference: ROX

f) Click on next step

g) The assay is dispensed to the appropriate wells according to the plate map.

i) Highlighting the aperture in which the first biomarker assay is to be dispensed

ii) click-through for first biomarker detector

iii) repeating the first two steps for other biomarkers

iv) click completion

h) Ensuring settings and board tabs are selected

i) Selecting the Instrument tab in the upper left corner

j) In the thermal cycler protocol zone, the thermogram tab, the following operations were performed, the results of which are shown in fig. 14:

i) delete stage 1 (unless this is done in a laboratory defined template).

ii) a volume of 25. mu.L of sample was input.

iii) 10 minutes at 95 ℃

iv) 40 cycles of 95 ℃ for 15 seconds and 63 ℃ for 1 minute

v) operating mode: standard 7500

vi) collecting data using the "stage 2, step 2(63.0@1: 00)" setting

k) The wells are marked using this procedure by right-clicking on the plate image and then selecting the well check. As the well check opens, one or more wells are selected. Click back to the well check and enter the sample name. When completed, the hole inspection object is closed.

i) CONH for high control

ii) CONL for Low control

iii) CONN for negative control

iv) NTC for no template control

v) for clinical specimens [ Login ID ]

l) ensure that the detector and quencher are selected, as listed below.

i) FAM for CEACAM biomarker 1; quenching agent ═ none

ii) FAM for LAMP1 biomarker 2; quencher is absent, and the like.

iii) FAM for PLA2G 7; quenching agent ═ none

iv) FAM for PLAC 8; quenching agent ═ none

v) selecting "ROX" for passive reference "

qPCR

In the template-free region, assay qPCR buffer and assay primer/probe mixtures for each target were removed to room temperature for thawing. The assay AmpliTaq Gold was kept in the refrigerator and/or on the freezer module.

The qPCR master mix for each target was prepared in the order listed, still in the template-free region, at room temperature.

qPCR Master mix-Per sample Calculations

The master mix was gently mixed by flicking or by vortexing and then pulsed centrifugation. At room temperature, 15 μ Ι _ of qPCR master mix was added to each well.

In the template region, 130. mu.L Septicyte Lab test dilution was added to each cDNA product from the RT reaction. The plates were resealed tightly and centrifuged to thoroughly mix.

According to the plate layout, 10 u L diluted cDNA product is added to each well.

The hole was covered with an optical seal.

Centrifuge the plate to remove any air bubbles (at 400x g, 1 min).

Placed on a real-time thermal cycler preprogrammed with the above settings.

Note that: the qPCR plate was not opened at any point after amplification had begun. When amplification is complete, the unopened plate is discarded.

Software, interpretation of results and quality control

The software was specifically designed to integrate with the output of the PCR machine and apply algorithms based on the use of multiple biomarkers. The software considers the appropriate controls and reports the results in the desired format.

When the run has been completed on the ABI 7500 fast Dx instrument, the following steps are completed in the application 7500 fast system with 21CFR Part 11 software, ABI software sdsv1.4.

Click the result tab in the upper left corner.

Click the amplification curve tab in the upper left corner.

In the analysis setup area, automatic baselines and manual thresholds are selected for all targets. 0.01 is input as a threshold.

The analysis button on the right side in the analysis setting area is clicked.

From the upper left menu bar, the file is selected and then closed.

Completing the form in the dialog box requesting the cause of the change. Click OK.

The data file (. sds) was transferred to a separate computer running the specific assay RT-qPCR test software.

The assay RT-qPCR test software was started. And (6) logging in.

From the upper left menu bar, a file is selected and then opened.

Browse the location of the transferred data file (. sds). Click OK.

The data file will then be analyzed using a software application for determination of interpretation of the results.

Interpretation of results and quality control

Results

The interpretation software is started. The software application instructions are provided separately.

Upon uploading the sds file, the software will automatically generate classification scores for the control and clinical samples.

Control

The software compares each CON (control) sample (CONH, CONL, CONN) to its expected result. The control was run in singleton mode.

If CONH, CONL and/or CONN fails, the batch run is invalid and will not be reported for clinical sample data. This determination is made automatically by the interpretation software. Batch runs should be repeated, starting with a new RNA preparation or starting at the RT reaction step.

If the NTC produces results other than failure (no Ct for all targets), the batch run is invalid and no data can be reported for the clinical sample. This determination is made by visual inspection of the operational data. Batch runs should be repeated, starting with a new RNA preparation or starting at the RT reaction step.

If the second batch fails, the technical service is contacted. If both the calibration and all controls are valid, the batch run is valid and the sample results will be reported.

Sample(s)

Note that a valid batch run may include both valid sample results and invalid sample results.

The analysis criteria (e.g., Ct value) that qualify each sample as either passing or failing (using predetermined data) are automatically invoked by the software.

Report-score out of range.

Quality control

The respective singleton patterns of negative control, low positive control and high positive control must be included in each batch run. If no marker is present for any of these controls, the batch is valid.

A singleton mode without a template control was included in each batch run, and failure (no Ct for all targets) was a valid result indicating that amplifiable material was not detected in the well.

Negative controls must produce negative results. If the negative control is marked as invalid, the entire batch run is invalid.

The low positive control and the high positive control must fall within the specified ranges. If one or both of the sample controls are marked as invalid, the entire batch run is invalid.

Exemplary output

Possible exemplary outputs from the software are shown below in fig. 15. The format of such reports depends on a number of factors, including; quality control, regulatory authorities, cutoff values, algorithms used, laboratory and clinician requirements, possibility of misinterpretation.

In this case, the assay is referred to as the "Septicyte laboratory test". Results are reported as numbers (5.8), calls ("sepsis positives"), positions on a scale of 0-12, and patients have a probability of sepsis and the use of a predetermined cutoff based on historical results (using results from clinical trials). The results of the controls within the assay are also reported. Other information that may be reported may include: the previous outcome and date and time of such outcome, the probability of severe sepsis or septic shock, provide historical test outcomes with a scale that separates healthy, inSIRS and ipSIRS (mild, severe and shock) conditions such that those patients with higher scores are considered to have a cut-off for more severe inSIRS or ipSIRS.

Second exemplary workflow

A second example workflow will now be described. Machines capable of processing patient samples with point-of-care or near point-of-care testing have been and are being developed. Such machine operations require little molecular biological skills and are directed to non-technical users. The idea is that the sample will be pipetted directly into a disposable cartridge which is then inserted into the machine. The user presses start and results are generated within 2-3 hours. The cassette contains all the reagents required to perform steps 2-5 in the example workflow above, and the machine has the appropriate software incorporated to allow steps 6 and 7 to be performed.

Fresh, anticoagulated whole blood can be pipetted into an Idylo cassette (Biocartis NV) or an analog (Unyvero, Curetis AG; Enigma ML, Enigma Diagnostics; Diagcore, STAT Diagnostics; Savannah, Quidel Corp; eFilex, Mark Dx) following the on-screen instructions for the Idylo machine to test for distinguishing insiRS from ipsIRRS (for example). Inside the Idylla machine, RNA is first extracted from whole blood and then converted to cDNA. The cDNA was then used in a qRT-PCR reaction. The reaction is followed in real time and the Ct value is calculated. The onboard software generates a result output (see fig. XX). Appropriate quality control measures for RNA quality, no template control, high and low template controls, and predicted Ct ranges ensure that results are not reported incorrectly.

Exemplary biomarker ratios

Exemplary biomarker ratios (top 12 based on AUC) capable of separating different conditions are shown in box and whisker plots as listed below, each showing perfect separation.

FIGS. 16A to 16L show healthy vs. insiRS (post-operative)

FIGS. 17A to 17L show healthy vs ipSIRS (sepsis)

FIGS. 18A to 18L show that insiRS (post-operative) vs ipsIRS (sepsis)

FIGS. 19A to 19L show sepsis vs Severe sepsis

FIGS. 20A to 20L show severe sepsis versus septic shock

FIGS. 21A to 21L show sepsis versus septic shock

Exemplary Algorithm to combine biomarker ratios

Biomarker ratios (derived markers) may be used in combination to increase the diagnostic ability to isolate multiple conditions. The determination of which markers to use, and how many markers to use to separate multiple conditions, can be accomplished by calculating the area under the curve (AUC).

FIG. 22 shows the effect of adding a biomarker to a diagnostic marker on AUC (in this case, insiRS and ipSIRS were isolated). The diagnostic ability between single mRNA biomarkers (in this case PLA2G7, AUC of 0.88, 95% CI0.79-0.97) was significantly increased (corrected p-value ═ 0.0175) compared to the ability of the two best performing markers in combination (in this case PLA2G7 and PLAC8, AUC of 0.96, 95% CI 0.91-1.0). Combinations of two, three, four and five biomarkers also produced a good discrimination between inSIRS and ipSIRS without significant differences. For commercial development of derived markers, other factors come into play, such as cost effectiveness, assay complexity, and capacity of the qRT-PCR platform.

In this example, the addition of more than 3 or 4 markers did not significantly improve performance, and conversely, a decrease in AUC was observed in the identification of ≧ 5 genes, likely due to overfitting of the data and additive noise occurring when the statistical model was forced to include biomarkers that add little additional information.

Thus, and for example, 4 gene signatures (0.986, 95% CI 0.964-1.00) provide an appropriate balance between the simplicity, utility and commercial risk of isolating inSIRS and ipSIRS. Furthermore, the equation using the four markers measures each biomarker equally, which also provides additional robustness in the case of analytical or clinical variability.

One exemplary equation that provides good diagnostic capability to separate inSIRS and ipSIRS (among others) is:

diagnostic score (PLA2G 7-PLAC 8) + (CEACAM 4-LAMP 1)

The value for each biomarker is the Ct value from PCR. When clinical samples from patients with inSIRS and ipSIRS were tested in PCR using these four markers, the Ct values for each marker were found to fall between 26 and 34. In the patient population, the first biomarker within each parenthesis pair has a higher value than the second biomarker within each parenthesis pair. Thus, the "diagnostic score" was found to have a value between 0 and 12. However, in theory, a "diagnostic score may potentially be the highest Ct value +/-the highest Ct value.

In fig. 23, it is shown that the results of PCR and the use of the above algorithm have been calculated for both patient populations (N63 for "found" and N70 for "feasibility") each patient was clinically and retrospectively (note, not at the time of sample collection) confirmed as having inSIRS (black dots) or ipSIRS (red dots). Each patient sample also had a calculated SeptiCyte score (Y axis on left). On a scale of 0-12, it can be seen that patients with confirmed ipSIRS (red dots) obtained higher diagnostic scores than those with confirmed inSIRS. Furthermore, it can be seen that any cut-off line that more or less separates the two conditions can be drawn (compared to a retrospective diagnosis of inSIRS or ipSIRS using clinical data), depending on the desired false negative or false positive ratio. In this case, the line is drawn with a "Septicyte score" of 4, such that the number of false negative ipsIRS calls in the discovery dataset is 0 and the number of false negative ipsIRS calls in the feasibility dataset is 2. Conversely, the number of false positive ipSIRS calls in the dataset was found to be 4 and the number of false positive ipSIRS calls in the feasibility dataset was 9. Clearly, in this case, whether a patient sample is false positive or false negative depends on the retrospective clinical invoked artificial gold standard of inSIRS or ipSIRS.

Thus, in one example, when used to determine the likelihood of a subject having inSIRS or ipSIRS, the method may comprise determining a first pair of biomarker values indicative of the concentration of the polynucleotide expression products of the PLA2G7 gene and the PLAC8 gene, determining a second pair of biomarker values indicative of the concentration of the polynucleotide expression products of the CEACAM4 gene and the LAMP1 gene, and then determining an indicator using the first pair of biomarker values and the second pair of biomarker values.

As previously discussed, the indicator may then be compared to a specially established indicator reference to distinguish inSIRS from ipSIRS.

An exemplary process for establishing an indicator reference will now be described in more detail with reference to fig. 24.

In this example, at step 2400, the processing system 201 determines reference data in the form of measured biomarker values obtained for a reference population. Reference data may be obtained in any suitable manner, but typically this involves obtaining gene expression products from multiple individuals.

In one example, the measurements are received as raw data, which is then subjected to preliminary processing. Such raw data corresponds to information that has been unmodified from the source, such as output from an instrument such as a PCR instrument, an array (e.g., microarray) scanner, a sequencer, clinical notes or any other biochemical data, biological data, observed data, and the like. This step can be used to convert the raw data into a format more suitable for analysis. In one example, this is done to normalize the raw data and thereby help ensure that biomarker values show consistency even when measured using different techniques, different equipment, etc. The purpose of the normalization is therefore to remove variations within the sample that cannot be directly attributed to the specific analysis under consideration. For example, variations caused by differences in sample processing at different sites are removed. Classical examples of normalization include z-score conversion on generic data, or specific normalization of popular areas, such as RMA normalization for microarrays.

In one example, the preferred method is a pairing function method compared to log normalized data. Log normalization is a standard data transformation of microarray data because the data follows a log-normal distribution when leaving the machine. Logarithmic conversion is applied to convert the data to process-friendly standard data.

Individuals are selected to include individuals diagnosed as having one or more conditions of interest as well as healthy individuals. The condition is typically a medical condition, veterinary condition, or other health state condition, and may include any disorder, disease, stage of disease, subtype of disease, severity of disease, disease of different prognosis, etc., and in the present example, will include at least some individuals with inSIRS and some individuals with ipSIRS. In this regard, the individual also typically undergoes a clinical assessment that allows clinical identification of the condition, and any assessment or indication of the condition forms part of the reference data.

The biomarker values measured will depend on the primary condition being assessed, so that, for example, in the case of a determination that a subject has a likelihood of inSIRS or ipSIRS, the biomarkers used will be LAMP1, CEACAM4, PLAC8 and PLA2G7 as discussed above.

After collection, the reference data may be stored in database 211, allowing this to be subsequently retrieved by processing system 201 for subsequent analysis, or may be provided directly to processing system 201 for analysis.

As part of the above process, at step 2410, the measurement values are verified using conventional prior art techniques to ensure that the measurements have been successfully made and are therefore valid.

At step 2420, each individual is typically assigned as a group with the reference population. Groups may be defined in any suitable manner, and may be defined based on: any one or more of an indication of the presence, absence, degree, stage, severity, prognosis or progression of a condition, other tests or assays, or measured biomarkers associated with an individual.

For example, the first selection of groups may be one or more groups identifying individuals with SIRS, one or more groups of individuals with ipSIRS, and one or more groups with inSIRS. Additional groups may also be defined for individuals with other conditions. Groups may include overlapping groups, so for example, it may be desirable to define healthy individuals and groups of individuals with SIRS, with further definition to distinguish inSIRS patients from ipSIRS patients, and different degrees of inSIRS or ipSIRS, which groups have SIRS in common, but which groups differ in whether a clinician has determined the presence or absence of an infection. In addition, further sub-divisions may be made based on the characteristics of the individuals, phenotypic traits, measurement protocols, etc., so groups may be defined based on these parameters, such that multiple groups of individuals with a condition are defined, wherein each group relates to a different phenotypic trait, measurement protocol, etc.

However, it should be understood that the identification of the different groups may be performed in other ways, for example based on the specific activity of a biomarker within a biological sample of a reference individual, and thus, reference to a condition is not intended to be limiting and other information may be used as desired.

At step 2430, a first derived biomarker value and a second derived biomarker value representing a respective indicator value are determined. First and second indicator values In₁、In₂Determining based on the ratio of the concentrations of the first and second biomarkers, and the ratio of the concentrations of the third and fourth biomarkers, respectively:

In₁＝(PLA2G7/PLAC8)

In₂＝(CEACAM4/LAMP1)

the indicator value is then used to establish an indicator reference, which is then used to analyze the indicator value measured for the subject to establish a likelihood that the subject has the condition, step 2440.

In particular, the indicator values for each reference group are statistically analyzed to establish a range or distribution of indicator values indicative of each group, and an exemplary distribution is shown in fig. 26, as discussed in more detail below.

Further examples will now be described with reference to fig. 25A and 25B.

In this example, at step 2500, a sample is obtained from a subject. The sample may be any suitable sample, such as a peripheral blood sample, depending on the nature of the biomarker value to be determined. At step 2505, the sample undergoes preparation, allowing this to be provided to the measurement device and used in the quantification process at step 2510. For the purposes of this example, the quantitative process includes PCR amplification, where the measurement device is a PCR machine, although other suitable techniques may be used. In this case, for each of the four biomarkers, the fold amplification At (PLA2G7), At (PLAC8), At (CEACAM4), At (LAMP1) is determined At step 2515, wherein the fold amplification is transferred from the measurement device to the processing system 201, allowing the processing system 210 to perform an analysis of the corresponding biomarker values.

Thus, at step 2520, the processing system 201 calculates the ratio using the fold amplification. In this regard, when the fold amplification represents a log value, the ratio is determined by subtracting the fold amplifications, as will be understood by those skilled in the art.

Thus, in this example, the indicator value will be determined as follows:

In₁＝At(PLA2G7)-At(PLAC8)

In₂＝At(CEACAM4)-At(LAMP1)

at step 2525, the processing system 201 determines the indicator by combining the ratios for the indicator values as follows:

In＝In₁+In₂

the processing system 201 then compares the indicator value to one or more respective indicator references at step 2530.

As previously described, the indicator reference is derived for a reference population and is used to indicate a likelihood that the subject has inSIRS or ipSIRS. To accomplish this, the reference population is divided into groups with/without a condition or a measure of severity, risk or stage of progression of the condition based on clinical assessment, which is then used to assess threshold indicator values that can distinguish groups or provide measures of severity, risk or stage of progression.

The comparison is determined by comparing the indicator to the indicator distribution determined for each group in the reference population. In the present example, there are two reference groups, one of which corresponds to an individual diagnosed with inSIRS and the other of which is an individual diagnosed with ipSIRS. In this case, the results of the comparison may be used to determine the likelihood that an individual has ipSIRS rather than inSIRS, which may be accomplished using a number of different methods, depending on the preferred implementation.

An example of a reference distribution is shown in figure 26, which shows the distribution of indicator values for a reference population containing both inSIRS and ipSIRS samples. The density (y-axis) describes how the common scores are distributed in the reference population. In fig. 26, the most common values for the inSIRS population are in the range 1 to 8, and the most common values for the ipSIRS population are mostly in the range 5 to 13. For example, let us assume that the indicator value calculated for the new sample is 4. A value of 4 in the inSIRS population has a high density (a) at that value, while the ipSIRS population has a low density (B) at that value, meaning that the sample is more likely to be inSIRS. Conversely, if the sample has an indicator score of 10, the value in the ipSIRS reference population has a high density (C) while the inSIRS population has a low density (D), meaning that it is more likely that the sample with an indicator value of 10 belongs to the ipSIRS population.

In practice, this process may be performed by determining the base probabilities based on the score bands. For a given score band (i.e., 4-6), the proportion of individuals with SIRS or sepsis is calculated. For example, if 40% of the scores between 4 and 6 are sepsis, then if the subjects have an indicator value between 4 and 6, they have a 40% probability of sepsis. Thus, for a given range within a reference profile, the probability of belonging to one group or another (SIRS/sepsis) may simply be the proportion of that group within the range.

An alternative technique is standard bayesian approach. In this case, the technique uses a distribution of inSIRS scores, a distribution of ipSIRS scores, and an indicator value for the subject. In this example, a standard score or equivalent is used to generate the probability that an indicator value belongs to an inSIRS distribution: pr (insirs), and individually indicates the probability that the value of an object belongs to an ipSIRS distribution: pr (ipsirs). Given the individual distribution, a bayesian approach is used to generate the probability of ipSIRS.

Thus, considering the derived biomarker distributions for two or more groups (i.e. inSIRS/ipSIRS), the probability of membership to each distribution for a single unknown sample can be calculated using, for example, a standard score (z-score) (p-value). The p-values for each distribution can then be combined into an overall probability for each class (i.e., inSIRS/ipSIRS) using, for example, bayes rules or any other probabilistic calculation method, including frequency or empirical or machine learning methods.

Thus, after the indicator value has been derived and compared to the indicator profile, the result of this comparison is used by the processing system 201 to calculate, at step 2535, the likelihood of the subject having ipSIRS, which is used to generate, at step 2540, a representation of the result, which is provided to be presented, for example, to a clinician or medical practitioner, at step 2545. This may be accomplished by exposing a representation, such as a portion of an email, dashboard indication, etc., on the client device.

An example of this is shown in fig. 27A and 27B.

In this example, the representation 2700 includes an indicator 2710 that moves relative to a linear scale 2720. The linear scale is divided into regions 2721, 2722, 2723, 2724 that indicate whether the subject has a level 1, 2, 3 or 4. The corresponding indicator value is displayed at step 2730 with an indication of whether the corresponding value represents a likelihood of sirs (insirs) or sepsis (ipSIRS) as shown at step 2740. The alphanumeric indications scored are shown at step 2751 along with the associated probability that the biological subject has sepsis at step 2752.

As shown in this example, the region of the linear scale in which the indicator is located is highlighted, with the diagnosis least likely to become grey attempting to make the scale in which the subject is located completely clear. This results in a representation that, when displayed at step 2545, is easily understandable to the clinician and makes a quick diagnosis.

From the above, it will be appreciated that there may be provided a method for use in assessing the likelihood of a biological subject having inSIRS or ipSIRS, the method comprising, in one or more processing devices:

c) retrieving from a database previously determined first and second indicator references, the first and second indicator references determined based on indicators determined by a first and second set of reference populations, one of the sets consisting of individuals diagnosed as having the medical condition;

Similarly, an apparatus for determining the likelihood of a biological subject having inSIRS or ipSIRS may be provided, the apparatus comprising:

c) at least one processing device that:

ii) using the biomarker values, determining an indicator using a ratio of the concentrations of the first polynucleotide expression product and the second polynucleotide expression product; and the number of the first and second groups,

An additional method that may be provided includes distinguishing inSIRS from ipSIRS in a biological subject, the method comprising:

b) in the measuring device:

i) amplifying at least some polynucleotide expression products in the sample;

ii) determining the amount of amplification representing the degree of amplification required to obtain a defined level of polynucleotide expression product, comprising:

(1) (ii) an amplification amount for a first pair of polynucleotide expression products of the PLA2G7 gene and the PLAC8 gene;

c) in a processing system:

i) retrieving the augmentation quantity;

ii) determining the indicator by:

(1) determining a first derived biomarker value indicative of a ratio of concentrations of the first pair of polynucleotide expression products by determining a difference between the amplification amounts for the first pair;

(2) determining a second derived biomarker value indicative of a ratio of concentrations of the second pair of polynucleotide expression products by determining a difference between the amplification amounts for the second pair;

Additionally, a method for determining an indicator for use in assessing the likelihood of a biological subject having the presence, absence, extent or prognosis of at least one medical condition may be provided, the method comprising:

Throughout this specification and the claims which follow, unless the context requires otherwise, the word "comprise", and variations such as "comprises" or "comprising", will be understood to imply the inclusion of a stated integer or group of integers or steps but not the exclusion of any other integer or group of integers.

It should be understood by those skilled in the art that many variations and modifications will become apparent. All such variations and modifications as would be obvious to one skilled in the art are intended to be included within the spirit and scope of the present invention as broadly described herein.

Claims

1. A method for determining an indicator for use in assessing the likelihood of a biological subject having the presence, absence, extent or prognosis of at least one medical condition, the method comprising:

c) determining the indicator using the derived biomarker value.

2. The method of claim 1, wherein the method comprises:

3. A method according to claim 2, wherein the method comprises combining the derived biomarker values using a combining function, the combining function being at least one of:

a) an additive model;

b) a linear model;

c) a support vector machine;

d) a neural network model;

e) a random forest model;

f) a regression model;

g) a genetic algorithm;

h) an annealing algorithm;

i) a weighted sum;

j) a nearest neighbor model; and the number of the first and second groups,

k) and (4) a probability model.

4. A method according to any one of claims 1 to 3, wherein the method is performed at least in part using an electronic processing device.

5. The method according to any one of claims 1 to 4, wherein the method comprises, in at least one electronic processing device:

a) obtaining the pair of biomarker values;

b) determining the first derived biomarker value;

c) determining the second derived biomarker value; and the number of the first and second groups,

d) determining the indicator by adding the first derived biomarker value and the second derived biomarker value.

6. The method of any one of claims 1 to 5, wherein the method comprises, in at least one electronic processing device, generating a representation of the indicator.

7. The method of claim 6, wherein the representing comprises:

a) an alphanumeric indication of the indicator;

8. The method according to any one of claims 1 to 7, wherein the method comprises:

b) determining a likelihood based on a result of the comparison.

9. The method of claim 8, wherein the indicator reference is based on at least one of:

a) an indicator threshold range;

b) an indicator threshold; and the number of the first and second groups,

c) the indicator distribution.

10. The method of claim 8 or claim 9, wherein the indicator reference is derived from indicators determined for a number of individuals in a reference population.

11. The method of claim 10, wherein the indicator reference is based on a distribution of indicators determined for a set of reference populations, the set consisting of individuals diagnosed as having the medical condition or lacking the medical condition.

12. The method of claim 10 or claim 11, wherein the reference population comprises:

a) A plurality of individuals of different genders;

b) a plurality of individuals of different ethnicities;

c) a plurality of healthy individuals;

d) a plurality of individuals having at least one diagnosed medical condition;

e) a plurality of individuals lacking at least one diagnosed medical condition;

g) a first group of individuals and a second group of individuals, each group of individuals having a respective diagnosed medical condition; and the number of the first and second groups,

13. The method of any one of claims 10 to 12, wherein the indicator is for determining a likelihood of a biological subject having at least one medical condition, and wherein the reference population comprises:

b) an individual diagnosed as having the at least one medical condition;

c) a healthy individual.

14. The method of any one of claims 8 to 13, wherein the indicator reference is retrieved from a database.

15. The method of any of claims 8 to 14, wherein the likelihood is based on a probability generated using a result of the comparison.

16. The method of any one of claims 8 to 15, wherein the indicator is for determining a likelihood that the subject has a first condition or a second condition, and wherein the method comprises:

b) determining the likelihood based on a result of the comparison.

17. The method of claim 16, wherein the method comprises:

a) determining a first indicator probability and a second indicator probability using the results of the comparison; and the number of the first and second groups,

b) combining the first indicator probability and the second indicator probability to determine a condition probability indicative of the likelihood.

18. The method of claim 16 or claim 17, wherein the first indicator reference and the second indicator reference are distributions of indicators determined for a first set of reference populations and a second set of reference populations, the first set and the second set consisting of individuals diagnosed as having the first condition or individuals of the second condition, respectively.

19. The method of any one of claims 1 to 18, wherein the method comprises:

a) obtaining a sample collected from the biological subject, the sample comprising a polynucleotide expression product; and the number of the first and second groups,

b) quantifying at least some of said polynucleotide expression products within said sample to determine the pair of biomarker values.

20. The method of claim 19, wherein the method comprises determining the indicator at least in part using a ratio of concentrations of the polynucleotide expression products.

21. The method of claim 19 or claim 20, wherein the method comprises:

a) quantifying the polynucleotide expression product by:

i) amplifying at least some polynucleotide expression products in the sample; and the number of the first and second groups,

ii) determining an amplification level representing the degree of amplification required to obtain a defined level of each of a pair of polynucleotide expression products; and the number of the first and second groups,

22. The method of claim 21, wherein the amplification amount is at least one of:

a) the cycle time;

b) the number of cycles;

c) a cycling threshold;

d) The amplification time; and the number of the first and second groups,

e) relative to the amount of amplification of another amplified product.

23. The method of claim 21 or claim 22, wherein the method comprises:

24. The method of any one of claims 1 to 23, wherein the immune system biomarker is a biomarker of the biological subject's immune system that is altered as part of an inflammatory response to injury or pathogenic damage, or the biological subject's immune system's expression level is altered as part of an inflammatory response to injury or pathogenic damage.

25. The method of any one of claims 1 to 24, wherein:

a) the at least two immune system biomarkers have a cross-correlation for the at least one condition within a cross-correlation range, the cross-correlation range being between ± 0.9; and the number of the first and second electrodes,

ii) the indicator has a performance value greater than or equal to a performance threshold representing the indicator's ability to diagnose the presence, absence, degree or prognosis of the at least one condition, the performance threshold being indicative of an interpretation variance of at least 0.3.

26. The method of claim 25, wherein the cross-correlation range is at least one of:

a)±0.8；

b)±0.7；

c)±0.6；

d)±0.5；

e)±0.4；

f)±0.3；

g) plus or minus 0.2; and the number of the first and second groups,

h)±0.1。

27. the method of claim 25 or claim 26, wherein each immune system biomarker has a condition correlation with the presence, absence, degree, or prognosis of at least one condition outside of a condition correlation range, the condition correlation range being between ± 0.3.

28. The method of claim 27, wherein the condition correlation range is at least one of:

a)±0.9；

b)±0.8；

c)±0.7；

d)±0.6；

e) plus or minus 0.5; and the number of the first and second groups,

f)±0.4。

29. the method of any of claims 25 to 28, wherein the performance threshold is indicative of an interpretation variance of at least one of:

a)0.4；

b)0.5；

c)0.6；

d)0.7；

e) 0.8; and the number of the first and second groups,

f)0.9。

30. the method of any one of claims 1 to 29, wherein the immune system biomarker value is indicative of the level or abundance of a molecule selected from one or more of a nucleic acid molecule and a proteinaceous molecule.

31. A method according to any one of claims 1 to 30, wherein the indicator is for determining a likelihood that the subject has inSIRS or ipSIRS, and wherein the method comprises:

32. A method according to any one of the claims 1 to 30, wherein the indicator is for determining a likelihood of the subject having inSIRS or a healthy condition, and wherein biomarker values are determined from at least one immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:

a) the first IRS immune system biomarker group consists of polynucleotide expression products and/or polypeptide expression products from group a IRS immune system biomarker genes as defined herein; and is

b) The second IRS immune system biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from group B IRS immune system biomarker genes as defined herein.

33. A method according to any one of claims 1 to 30, wherein the indicator is for determining a likelihood of the subject having ipSIRS or a healthy condition, and wherein biomarker values are determined from at least one immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:

a) the first IRS immune system biomarker group consists of polynucleotide expression products and/or polypeptide expression products of group C IRS immune system biomarker genes as defined herein; and the number of the first and second electrodes,

b) the second IRS immune system biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from group D IRS immune system biomarker genes as defined herein.

34. A method according to any one of the claims 1 to 30, wherein the indicator is for determining a likelihood of the subject having inSIRS or ipSIRS, and wherein biomarker values are determined from at least one immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:

a) the first IRS immune system biomarker group consists of polynucleotide expression products and/or polypeptide expression products of group E IRS immune system biomarker genes as defined herein; and the number of the first and second electrodes,

b) The second IRS immune system biomarker group consists of polynucleotide expression products and/or polypeptide expression products of group F IRS immune system biomarker genes as defined herein.

35. A method according to any one of the claims 1 to 30, wherein the indicator is for determining a likelihood of the subject having inSIRS or ipSIRS, and wherein biomarker values are determined from at least one immune system biomarker in each of a first IRS immune system biomarker group, a second IRS immune system biomarker group, a third IRS immune system biomarker group and a fourth IRS immune system biomarker group, wherein:

a) the first IRS immune system biomarker group consists of polynucleotide expression products and/or polypeptide expression products from group G IRS immune system biomarker genes as defined herein;

b) the second IRS immune system biomarker group consists of polynucleotide expression products and/or polypeptide expression products of group H IRS immune system biomarker genes as defined herein;

c) said third IRS immune system biomarker group consists of polynucleotide expression products and/or polypeptide expression products of group I IRS immune system biomarker genes as defined herein; and the number of the first and second electrodes,

d) The fourth IRS immune system biomarker panel consists of polynucleotide expression products and/or polypeptide expression products from group J IRS immune system biomarker genes as defined herein.

36. The method of claim 35, wherein the first IRS immune system biomarker is a PLA2G7 expression product, wherein the second IRS immune system biomarker is a PLAC8 expression product, wherein the third IRS immune system biomarker is a CEACAM4 expression product, and wherein the fourth IRS immune system biomarker is a LAMP1 expression product.

37. A method according to any one of claims 1 to 30, wherein the indicator is for determining a likelihood of the subject having mild sepsis or severe sepsis, and wherein biomarker values are determined from at least one immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:

a) the first IRS immune system biomarker group consists of polynucleotide expression products and/or polypeptide expression products from group K IRS immune system biomarker genes as defined herein; and the number of the first and second electrodes,

b) the second IRS immune system biomarker group consists of polynucleotide expression products and/or polypeptide expression products of group L IRS immune system biomarker genes as defined herein.

38. A method according to any one of claims 1 to 30, wherein the indicator is for determining a likelihood of the subject having mild sepsis or septic shock, and wherein biomarker values are determined from at least one immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:

a) the first IRS immune system biomarker group consists of polynucleotide expression products and/or polypeptide expression products of group M IRS immune system biomarker genes as defined herein; and the number of the first and second electrodes,

b) the second IRS immune system biomarker group consists of polynucleotide expression products and/or polypeptide expression products of the N-group IRS immune system biomarker genes as defined herein.

39. A method according to any one of claims 1 to 30, wherein the indicator is for determining the likelihood of the subject having severe sepsis or septic shock, and wherein biomarker values are determined from at least one immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:

a) the first IRS immune system biomarker group consists of polynucleotide expression products and/or polypeptide expression products of group O IRS immune system biomarker genes as defined herein; and the number of the first and second electrodes,

b) The second IRS immune system biomarker group consists of polynucleotide expression products and/or polypeptide expression products of the group P IRS immune system biomarker genes as defined herein.

40. Apparatus for determining an indicator for use in assessing the likelihood of a biological subject having the presence, absence, extent or prognosis of at least one medical condition, the apparatus comprising at least one electronic processing device that:

c) determining the indicator using the derived biomarker value.

41. A composition comprising at least one pair of reverse transcribed mrnas comprising a first pair of reverse transcribed mrnas and a second pair of reverse transcribed mrnas, wherein the first pair comprises a PLAC8 reverse transcribed mRNA and a PLA2G7 reverse transcribed mRNA, and wherein the second pair comprises a CEACAM4 reverse transcribed mRNA and a LAMP1 reverse transcribed mRNA, and at least one oligonucleotide primer or probe that hybridizes to a separate one of the reverse transcribed mrnas.

42. A composition comprising at least one pair of reverse transcribed mrnas comprising reverse transcribed mRNA from a first IRS immune system biomarker gene selected from group a IRS immune system biomarker genes as defined herein, and at least one oligonucleotide primer or probe that hybridizes to a separate one of said reverse transcribed mrnas; and reverse transcribed mRNA from a second IRS immune system biomarker gene selected from the group B IRS immune system biomarker genes as defined herein.

43. A composition comprising at least one pair of reverse transcribed mrnas comprising reverse transcribed mRNA from a first IRS immune system biomarker gene selected from group C IRS immune system biomarker genes as defined herein, and at least one oligonucleotide primer or probe that hybridizes to a separate one of said reverse transcribed mrnas; and reverse transcribed mRNA from a second IRS immune system biomarker gene selected from the group D IRS immune system biomarker genes as defined herein.

44. A composition comprising at least one pair of reverse transcribed mrnas comprising reverse transcribed mRNA from a first IRS immune system biomarker gene selected from group E IRS immune system biomarker genes as defined herein, and at least one oligonucleotide primer or probe that hybridizes to a separate one of said reverse transcribed mrnas; and reverse transcribed mRNA from a second IRS immune system biomarker gene selected from the group F IRS immune system biomarker genes as defined herein.

45. A composition comprising at least two pairs of reverse transcribed mRNA comprising a first pair of reverse transcribed mRNA and a second pair of reverse transcribed mRNA, wherein the first pair comprises reverse transcribed mRNA from a first IRS immune system biomarker gene and reverse transcribed mRNA from a second IRS immune system biomarker gene, and wherein the second pair comprises reverse transcribed mRNA from a third IRS immune system biomarker gene and reverse transcribed mRNA from a fourth IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group G IRS immune system biomarker genes as defined herein, wherein the second IRS immune system biomarker gene is selected from group H IRS immune system biomarker genes as defined herein, wherein the third IRS immune system biomarker gene is selected from group I IRS immune system biomarker genes as defined herein, and wherein the fourth IRS immune system biomarker gene is selected from group J IRS immune system biomarker genes as defined herein.

46. A composition comprising at least one pair of reverse transcribed mrnas comprising reverse transcribed mRNA from a first IRS immune system biomarker gene selected from the group of K IRS immune system biomarker genes as defined herein, and at least one oligonucleotide primer or probe that hybridizes to a separate one of said reverse transcribed mrnas; and reverse transcribed mRNA from a second IRS immune system biomarker gene selected from the group L IRS immune system biomarker genes as defined herein.

47. A composition comprising at least one pair of reverse transcribed mrnas comprising reverse transcribed mRNA from a first IRS immune system biomarker gene selected from the group M of IRS immune system biomarker genes as defined herein, and at least one oligonucleotide primer or probe that hybridizes to a separate one of said reverse transcribed mrnas; and reverse transcribed mRNA from a second IRS immune system biomarker gene selected from the group N IRS immune system biomarker genes as defined herein.

48. A composition comprising at least one pair of reverse transcribed mrnas comprising reverse transcribed mRNA from a first IRS immune system biomarker gene selected from group O IRS immune system biomarker genes as defined herein, and at least one oligonucleotide primer or probe that hybridizes to a separate one of said reverse transcribed mrnas; and reverse transcribed mRNA from a second IRS immune system biomarker gene selected from the group P IRS immune system biomarker genes as defined herein.

49. The composition of any one of claims 41 to 48, wherein the at least one oligonucleotide primer or probe hybridizes to a separate one of the reverse transcribed mRNA.

50. The composition of any one of claims 41-48, wherein the reverse transcribed mRNA is derived from a component of the immune system.

51. The composition of any one of claims 41-48, wherein the reverse transcribed mRNA is derived from a leukocyte.

52. The composition of any one of claims 41-48, wherein the reverse transcribed mRNA is derived from a blood cell.

53. The composition of any one of claims 41-48, wherein the reverse transcribed mRNA is derived from a peripheral blood cell.

54. The composition of any one of claims 41 to 48, further comprising a labeled reagent for detecting the reverse transcribed mRNA.

55. The composition of claim 54, wherein the labeled reagent is the at least one oligonucleotide or probe that is labeled.

56. The composition of claim 54, wherein the labeled agent is the labeled reverse transcribed mRNA.

57. A kit for determining an indicator indicative of the likelihood of the presence or absence of at least one condition selected from the group consisting of inSIRS and ipSIRS, said kit comprising at least one pair of reagents comprising a first pair of reagents and a second pair of reagents, wherein said first pair of reagents comprises (i) reagents that allow quantification of a polynucleotide expression product of a PLA2G7 gene; and (ii) reagents that allow quantification of the polynucleotide expression product of the PLAC8 gene, wherein the second pair of reagents comprises: (iii) reagents allowing the quantification of the polynucleotide expression product of the CEACAM4 gene; and (iv) reagents that allow quantification of the polynucleotide expression product of the LAMP1 gene.

58. A kit for determining an indicator indicative of the likelihood of the presence or absence of at least one condition selected from the group consisting of inSIRS and a health condition, the kit comprising at least one pair of reagents comprising (i) reagents that allow quantification of a polynucleotide expression product of a first IRS immune system biomarker gene; and (ii) an agent that allows quantification of a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group a IRS immune system biomarker genes as defined herein, and wherein the second IRS immune system biomarker gene is selected from group B IRS immune system biomarker genes as defined herein.

59. A kit for determining an indicator indicative of the likelihood of the presence or absence of at least one condition selected from the group consisting of ipSIRS and a health condition, said kit comprising at least one pair of reagents comprising (i) reagents allowing quantification of a polynucleotide expression product of a first IRS immune system biomarker gene; and (ii) an agent that allows quantification of a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group C IRS immune system biomarker genes as defined herein, and wherein the second IRS immune system biomarker gene is selected from group D IRS immune system biomarker genes as defined herein.

60. A kit for determining an indicator indicative of the likelihood of the presence or absence of at least one condition selected from the group consisting of inSIRS and ipSIRS, said kit comprising at least one pair of reagents comprising (i) reagents allowing quantification of a polynucleotide expression product of a first IRS immune system biomarker gene; and (ii) an agent that allows quantification of a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group E IRS immune system biomarker genes as defined herein, and wherein the second IRS immune system biomarker gene is selected from group F IRS immune system biomarker genes as defined herein.

61. A kit for determining an indicator indicative of the likelihood of the presence or absence of at least one condition selected from the group consisting of inSIRS and ipSIRS, the kit comprising at least two pairs of reagents comprising a first pair of reagents and a second pair of reagents, wherein the first pair of reagents comprises (i) reagents that allow quantification of a polynucleotide expression product of a first IRS immune system biomarker gene; and (ii) an agent that permits quantitation of a polynucleotide expression product of a second IRS immune system biomarker gene, and wherein the second pair of agents comprises (i) an agent that permits quantitation of a polynucleotide expression product of a third IRS immune system biomarker gene; and (ii) an agent that allows quantification of a polynucleotide expression product of a fourth IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group G IRS immune system biomarker genes as defined herein, wherein the second IRS immune system biomarker gene is selected from group H IRS immune system biomarker genes as defined herein, wherein the third IRS immune system biomarker gene is selected from group I IRS immune system biomarker genes as defined herein, and wherein the fourth IRS immune system biomarker gene is selected from group J IRS immune system biomarker genes as defined herein.

62. A kit for determining an indicator indicative of the likelihood of the presence or absence of at least one condition selected from the group consisting of mild sepsis and severe sepsis, the kit comprising at least one pair of reagents comprising (i) reagents allowing quantification of a polynucleotide expression product of a first IRS immune system biomarker gene; and (ii) an agent that allows quantification of a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from the K group of IRS immune system biomarker genes as defined herein, and wherein the second IRS immune system biomarker gene is selected from the L group of IRS immune system biomarker genes as defined herein.

63. A kit for determining an indicator indicative of the likelihood of the presence or absence of at least one condition selected from the group consisting of mild sepsis and septic shock, the kit comprising at least one pair of reagents comprising (i) reagents that allow quantification of a polynucleotide expression product of a first IRS immune system biomarker gene; and (ii) an agent that allows quantification of a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from the group M IRS immune system biomarker genes as defined herein, and wherein the second IRS immune system biomarker gene is selected from the group N IRS immune system biomarker genes as defined herein.

64. A kit for determining an indicator indicative of the likelihood of the presence or absence of at least one condition selected from the group consisting of severe sepsis and septic shock, the kit comprising at least one pair of reagents comprising (i) reagents that allow quantification of a polynucleotide expression product of a first IRS immune system biomarker gene; and (ii) an agent that allows quantification of a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from the group O IRS immune system biomarker genes as defined herein, and wherein the second IRS immune system biomarker gene is selected from the group P IRS immune system biomarker genes as defined herein.

65. A method for inhibiting the development or progression of at least one condition selected from the group consisting of inSIRS and ipSIRS in a subject, the method comprising: exposing the subject to a treatment regimen for treating at least one condition based on an indicator obtained from an indicator determination method, wherein the indicator indicates the presence of the at least one condition in the subject, the indicator determination method comprising: (a) determining at least one pair of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject, (b) determining at least one derived biomarker value using the at least one pair of biomarker values, the derived biomarker value being indicative of a ratio of the concentrations of the at least one pair of immune system biomarkers; and (c) determining the indicator based on the at least one derived biomarker value, wherein the pair of biomarker values includes at least one of:

a) A first pair of biomarker values comprising a first biomarker value and a second biomarker value corresponding to a first biomarker and a second biomarker, wherein the first immune system biomarker represents a polynucleotide expression product of the PLA2G7 gene and wherein the second immune system biomarker represents a polynucleotide expression product of the PLAC8 gene, and

66. The method of claim 65, wherein the indicator determination method comprises: determining the first pair of biomarker values and the second pair of biomarker values, and determining a first derived biomarker value calculated using the first pair of biomarker values and a second derived biomarker value calculated using the second pair of biomarker values; and determining the indicator based on a combination of the first derived biomarker value and the second derived biomarker value.

67. A method for inhibiting the development or progression of inSIRS in a subject, the method comprising: exposing the subject to a treatment regimen for treating inSIRS based on an indicator obtained from an indicator determination method, wherein the indicator indicates the presence of inSIRS in the subject, the indicator determination method comprising: (a) determining at least one pair of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject, (b) determining at least one derived biomarker value using the at least one pair of biomarker values, the derived biomarker value being indicative of a ratio of the concentrations of the pair of immune system biomarkers; and (c) determining the indicator based on the at least one derived biomarker value, wherein the at least one pair of biomarker values comprises a first biomarker value and a second biomarker value corresponding to a first immune system biomarker and a second immune system biomarker, respectively, wherein the first immune system biomarker represents a polynucleotide expression product of a first IRS immune system biomarker gene, and wherein the second immune system biomarker represents a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group a IRS immune system biomarker genes as defined herein, and wherein the second IRS immune system biomarker gene is selected from group B IRS immune system biomarker genes as defined herein.

68. A method for inhibiting the development or progression of ipSIRS in a subject, the method comprising: exposing the subject to a treatment regimen for treating ipSIRS based on an indicator obtained from an indicator determination method, wherein the indicator indicates the presence of ipSIRS in the subject, the indicator determination method comprising: (a) determining at least one pair of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject, (b) determining at least one derived biomarker value using the at least one pair of biomarker values, the derived biomarker value being indicative of a ratio of the concentrations of the at least one pair of immune system biomarkers; and (C) determining the indicator based on the at least one derived biomarker value, wherein the at least one pair of biomarker values comprises a first biomarker value and a second biomarker value corresponding to a first immune system biomarker and a second immune system biomarker, respectively, wherein the first immune system biomarker represents a polynucleotide expression product of a first IRS immune system biomarker gene, and wherein the second immune system biomarker represents a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from the group C IRS immune system biomarker genes as defined herein, and wherein the second IRS immune system biomarker gene is selected from the group D IRS immune system biomarker genes as defined herein.

69. A method for inhibiting the development or progression of at least one condition selected from the group consisting of inSIRS and ipSIRS in a subject, the method comprising: exposing the subject to a treatment regimen for treating the at least one condition based on an indicator obtained from an indicator determination method, wherein the indicator indicates the presence of the at least one condition in the subject, the indicator determination method comprising: (a) determining at least one pair of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject, (b) determining at least one derived biomarker value using the at least one pair of biomarker values, the derived biomarker value being indicative of a ratio of the concentrations of the at least one pair of immune system biomarkers; and (c) determining the indicator based on the at least one derived biomarker value, wherein the at least one pair of biomarker values comprises a first biomarker value and a second biomarker value corresponding to a first immune system biomarker and a second immune system biomarker, respectively, wherein the first immune system biomarker represents a polynucleotide expression product of a first IRS immune system biomarker gene, and wherein the second immune system biomarker represents a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from the group E IRS immune system biomarker genes as defined herein, and wherein the second IRS immune system biomarker gene is selected from the group F IRS immune system biomarker genes as defined herein.

70. A method for inhibiting the development or progression of at least one condition selected from the group consisting of inSIRS and ipSIRS in a subject, the method comprising: exposing the subject to a treatment regimen for treating the at least one condition based on an indicator obtained from an indicator determination method, wherein the indicator indicates the presence of the at least one condition in the subject, the indicator determination method comprising: (a) determining at least two pairs of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of the concentration of the immune system biomarker in a sample taken from the subject, (b) determining at least two derived biomarker values using the at least two pairs of biomarker values, the derived biomarker values being indicative of a ratio of the concentrations of each pair of immune system biomarkers; and (c) determining the indicator based on the at least two derived biomarker values, wherein the at least one pair of biomarker values comprises a first pair of biomarker values comprising a first biomarker value and a second biomarker value corresponding to a first immune system biomarker and a second immune system biomarker, respectively, wherein the first immune system biomarker represents a polynucleotide expression product of a first IRS immune system biomarker gene, and wherein the second immune system biomarker represents a polynucleotide expression product of a second IRS immune system biomarker gene, and the second pair of biomarker values comprises a third biomarker value and a fourth biomarker value corresponding to a third immune system biomarker and a fourth immune system biomarker, respectively, wherein the third immune system biomarker represents a polynucleotide expression product of a third IRS immune system biomarker gene And wherein the fourth immune system biomarker represents a polynucleotide expression product of a fourth IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group G IRS immune system biomarker genes as defined herein, wherein the second IRS immune system biomarker gene is selected from group H IRS immune system biomarker genes as defined herein, wherein the third IRS immune system biomarker gene is selected from group I IRS immune system biomarker genes as defined herein, and wherein the fourth IRS immune system biomarker gene is selected from group J IRS immune system biomarker genes as defined herein.

71. A method for inhibiting the development or progression of at least one condition selected from the group consisting of mild sepsis and severe sepsis in a subject, the method comprising: exposing the subject to a treatment regimen for treating the at least one condition based on an indicator obtained from an indicator determination method, wherein the indicator indicates the presence of the at least one condition in the subject, the indicator determination method comprising: (a) determining at least one pair of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject, (b) determining at least one derived biomarker value using the at least one pair of biomarker values, the derived biomarker value being indicative of a ratio of the concentrations of the at least one pair of immune system biomarkers; and (c) determining the indicator based on the at least one derived biomarker value, wherein the at least one pair of biomarker values comprises a first biomarker value and a second biomarker value corresponding to a first immune system biomarker and a second immune system biomarker, respectively, wherein the first immune system biomarker represents a polynucleotide expression product of a first IRS immune system biomarker gene, and wherein the second immune system biomarker represents a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from the K group of IRS immune system biomarker genes as defined herein, and wherein the second IRS immune system biomarker gene is selected from the L group of IRS immune system biomarker genes as defined herein.

72. A method for inhibiting the development or progression of at least one condition selected from the group consisting of mild sepsis and septic shock in a subject, the method comprising: exposing the subject to a treatment regimen for treating the at least one condition based on an indicator obtained from an indicator determination method, wherein the indicator indicates the presence of the at least one condition in the subject, the indicator determination method comprising: (a) determining at least one pair of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject, (b) determining at least one derived biomarker value using the at least one pair of biomarker values, the derived biomarker value being indicative of a ratio of the concentrations of the at least one pair of immune system biomarkers; and (c) determining the indicator based on the at least one derived biomarker value, wherein the at least one pair of biomarker values comprises a first biomarker value and a second biomarker value corresponding to a first immune system biomarker and a second immune system biomarker, respectively, wherein the first immune system biomarker represents a polynucleotide expression product of a first IRS immune system biomarker gene, and wherein the second immune system biomarker represents a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from the M group of IRS immune system biomarker genes as defined herein, and wherein the second IRS immune system biomarker gene is selected from the N group of IRS immune system biomarker genes as defined herein.

73. A method for inhibiting the development or progression of at least one condition selected from the group consisting of severe sepsis and septic shock in a subject, the method comprising: exposing the subject to a treatment regimen for treating the at least one condition based on an indicator obtained from an indicator determination method, wherein the indicator indicates the presence of the at least one condition in the subject, the indicator determination method comprising: (a) determining at least one pair of biomarker values, each biomarker value being a value measured or derived for at least one corresponding immune system biomarker of the biological subject and being at least partially indicative of a concentration of the immune system biomarker in a sample taken from the subject, (b) determining at least one derived biomarker value using the at least one pair of biomarker values, the derived biomarker value being indicative of a ratio of the concentrations of the at least one pair of immune system biomarkers; and (c) determining the indicator based on the at least one derived biomarker value, wherein the at least one pair of biomarker values comprises a first biomarker value and a second biomarker value corresponding to a first immune system biomarker and a second immune system biomarker, respectively, wherein the first immune system biomarker represents a polynucleotide expression product of a first IRS immune system biomarker gene, and wherein the second immune system biomarker represents a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from the O group IRS immune system biomarker genes as defined herein, and wherein the second IRS immune system biomarker gene is selected from the P group IRS immune system biomarker genes as defined herein.

74. The method of any one of claims 65 to 73, comprising: collecting the sample from the subject and obtaining the indicator according to the indicator determination method.

75. The method of any one of claims 65 to 73, comprising: sending the sample collected from the subject to a laboratory where the indicator is determined.

76. A method for distinguishing inSIRS from ipSIRS in a biological subject, the method comprising:

77. A method for differentiating inSIRS from a health condition in a biological subject, the method comprising:

b) quantifying the polynucleotide expression product within the sample to determine a pair of biomarker values indicative of the concentration of the polynucleotide expression product of a first IRS immune system biomarker gene and a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group a IRS immune system biomarker genes as defined herein, and wherein the second IRS immune system biomarker gene is selected from group B IRS immune system biomarker genes as defined herein;

78. A method for differentiating ipSIRS from a health condition in a biological subject, the method comprising:

b) quantifying the polynucleotide expression product within the sample to determine a pair of biomarker values indicative of the concentration of the polynucleotide expression product of a first IRS immune system biomarker gene and a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group C IRS immune system biomarker genes as defined herein, and wherein the second IRS immune system biomarker gene is selected from group D IRS immune system biomarker genes as defined herein;

79. A method for distinguishing inSIRS from ipSIRS in a biological subject, the method comprising:

b) quantifying the polynucleotide expression product within the sample to determine a pair of biomarker values indicative of the concentration of the polynucleotide expression product of a first IRS immune system biomarker gene and a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group E IRS immune system biomarker genes as defined herein, and wherein the second IRS immune system biomarker gene is selected from group F IRS immune system biomarker genes as defined herein;

80. A method for distinguishing inSIRS from ipSIRS in a biological subject, the method comprising:

i) a first pair of biomarker values indicative of a concentration of a polynucleotide expression product of a first IRS immune system biomarker gene and a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group G IRS immune system biomarker genes as defined herein, and wherein the second IRS immune system biomarker gene is selected from group H IRS immune system biomarker genes as defined herein;

ii) a second pair of biomarker values indicative of a concentration of a polynucleotide expression product of a third IRS immune system biomarker gene and a fourth IRS immune system biomarker gene, wherein the third IRS immune system biomarker gene is selected from group I IRS immune system biomarker genes as defined herein, and wherein the fourth IRS immune system biomarker gene is selected from group J IRS immune system biomarker genes as defined herein;

81. A method for differentiating mild sepsis from severe sepsis in a biological subject, the method comprising:

b) Quantifying the polynucleotide expression product within the sample to determine a pair of biomarker values indicative of the concentration of the polynucleotide expression product of a first IRS immune system biomarker gene and a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from the group of K IRS immune system biomarker genes as defined herein, and wherein the second IRS immune system biomarker gene is selected from the group of L IRS immune system biomarker genes as defined herein;

82. A method for differentiating mild sepsis from septic shock in a biological subject, the method comprising:

b) Quantifying the polynucleotide expression product within the sample to determine a pair of biomarker values indicative of the concentration of the polynucleotide expression product of a first IRS immune system biomarker gene and a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from the group M IRS immune system biomarker genes as defined herein, and wherein the second IRS immune system biomarker gene is selected from the group N IRS immune system biomarker genes as defined herein;

83. A method for differentiating severe sepsis from septic shock in a biological subject, the method comprising:

b) Quantifying the polynucleotide expression product within the sample to determine a pair of biomarker values indicative of the concentration of the polynucleotide expression product of a first IRS immune system biomarker gene and a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from the group O IRS immune system biomarker genes as defined herein, and wherein the second IRS immune system biomarker gene is selected from the group P IRS immune system biomarker genes as defined herein;

84. The method of claim 76 or claim 80, wherein the method comprises:

a) determining a first derived biomarker using the first pair of biomarker values;

b) Determining a second derived biomarker value using the second pair of biomarker values; and the number of the first and second groups,

85. A method according to any one of claims 76 to 84, wherein the first and second indicator references are distributions of indicators determined for a first and second set of reference populations, the first and second sets consisting of individuals diagnosed with inSIRS and ipSIRS, respectively.

86. A method for determining an indicator for use in assessing the likelihood of a biological subject having at least one medical condition, the method comprising:

b) amplifying at least some polynucleotide expression products in the sample;

87. A method for determining an indicator for use in assessing the likelihood of a biological subject having at least one medical condition, the method comprising:

b) amplifying at least some polynucleotide expression products in the sample;

c) determining an amount of amplification representing the degree of amplification required to obtain a defined level of each of a pair of polynucleotide expression products selected from the group consisting of: a polynucleotide expression product of a first IRS immune system biomarker gene and a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group a IRS immune system biomarker genes as defined herein, and wherein the second IRS immune system biomarker gene is selected from group B IRS immune system biomarker genes as defined herein;

88. A method for determining an indicator for use in assessing the likelihood of a biological subject having at least one medical condition, the method comprising:

b) amplifying at least some polynucleotide expression products in the sample;

c) determining an amount of amplification representing the degree of amplification required to obtain a defined level of each of a pair of polynucleotide expression products selected from the group consisting of: a polynucleotide expression product of a first IRS immune system biomarker gene and a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group C IRS immune system biomarker genes as defined herein, and wherein the second IRS immune system biomarker gene is selected from group D IRS immune system biomarker genes as defined herein;

89. A method for determining an indicator for use in assessing the likelihood of a biological subject having at least one medical condition, the method comprising:

b) amplifying at least some polynucleotide expression products in the sample;

c) determining an amount of amplification representing the degree of amplification required to obtain a defined level of each of a pair of polynucleotide expression products selected from the group consisting of: a polynucleotide expression product of a first IRS immune system biomarker gene and a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group E IRS immune system biomarker genes as defined herein, and wherein the second IRS immune system biomarker gene is selected from group F IRS immune system biomarker genes as defined herein;

90. A method for determining an indicator for use in assessing the likelihood of a biological subject having at least one medical condition, the method comprising:

b) amplifying at least some polynucleotide expression products in the sample;

i) a first pair of polynucleotide expression products of a first IRS immune system biomarker gene and a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from group G IRS immune system biomarker genes as defined herein, and wherein the second IRS immune system biomarker gene is selected from group H IRS immune system biomarker genes as defined herein;

ii) a second pair of polynucleotide expression products of a third IRS immune system biomarker gene and a fourth IRS immune system biomarker gene, wherein the third IRS immune system biomarker gene is selected from group I IRS immune system biomarker genes as defined herein, and wherein the fourth IRS immune system biomarker gene is selected from group J IRS immune system biomarker genes as defined herein;

91. A method for determining an indicator for use in assessing the likelihood of a biological subject having at least one medical condition, the method comprising:

b) amplifying at least some polynucleotide expression products in the sample;

c) determining an amount of amplification representing the degree of amplification required to obtain a defined level of each of a pair of polynucleotide expression products selected from the group consisting of: a polynucleotide expression product of a first IRS immune system biomarker gene and a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from the group K IRS immune system biomarker genes as defined herein, and wherein the second IRS immune system biomarker gene is selected from the group L IRS immune system biomarker genes as defined herein;

92. A method for determining an indicator for use in assessing the likelihood of a biological subject having at least one medical condition, the method comprising:

b) amplifying at least some polynucleotide expression products in the sample;

c) determining an amount of amplification representing the degree of amplification required to obtain a defined level of each of a pair of polynucleotide expression products selected from the group consisting of: a polynucleotide expression product of a first IRS immune system biomarker gene and a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from the group M IRS immune system biomarker genes as defined herein, and wherein the second IRS immune system biomarker gene is selected from the group N IRS immune system biomarker genes as defined herein;

93. A method for determining an indicator for use in assessing the likelihood of a biological subject having at least one medical condition, the method comprising:

b) amplifying at least some polynucleotide expression products in the sample;

c) determining an amount of amplification representing the degree of amplification required to obtain a defined level of each of a pair of polynucleotide expression products selected from the group consisting of: a polynucleotide expression product of a first IRS immune system biomarker gene and a polynucleotide expression product of a second IRS immune system biomarker gene, wherein the first IRS immune system biomarker gene is selected from the group O IRS immune system biomarker genes as defined herein, and wherein the second IRS immune system biomarker gene is selected from the group P IRS immune system biomarker genes as defined herein;

94. The method of any one of claims 86-93, wherein the method comprises:

a) Determining a first derived biomarker value by determining a difference between amplified amounts of the first pair of polynucleotide expression products;

95. The method of any one of claims 86-94, wherein the method comprises:

b) determining a likelihood that the subject has the medical condition based on a result of the comparison.

96. The method of any one of claims 76-95, wherein the amplification amount is at least one of:

a) the cycle time;

b) the number of cycles;

c) a cycling threshold;

d) The amplification time; and the number of the first and second groups,

e) relative to the amount of amplification of another amplified product.

97. A method for use in assessing the likelihood of a biological subject having a medical condition, the method comprising, in one or more processing devices:

98. The method of claim 97, wherein the method comprises:

a) determining a first derived biomarker value using the first pair of biomarker values;

99. An apparatus for determining an indicator for use in determining a likelihood of a biological subject having at least one medical condition, the apparatus comprising:

c) at least one processing device, the at least one processing device:

iv) determining a likelihood that the subject has the at least one medical condition using the results of the comparison.

100. A method for distinguishing inSIRS from ipSIRS in a biological subject, the method comprising:

b) in the measuring device:

i) Amplifying at least some polynucleotide expression products in the sample;

c) in a processing system:

i) retrieving the augmentation quantity;

ii) determining the indicator by:

101. A method for determining an indicator for use in assessing the likelihood of a biological subject having the presence, absence, extent or prognosis of at least one medical condition, the method comprising:

102. The method of claim 101, wherein the method comprises:

a) determining a plurality of measured biomarker values, each measured biomarker value being a measured value of a corresponding biomarker for the biological subject; and the number of the first and second groups,

b) determining the indicator by applying a function to at least one of the measured biomarker values to determine at least one derived biomarker value, the at least one derived biomarker value being indicative of a value of a corresponding derived biomarker.

103. The method of claim 102, wherein the function comprises at least one of:

a) multiplying the two biomarker values;

b) dividing the two biomarker values;

c) adding the two biomarker values;

d) subtracting the two biomarker values;

e) a ratio of two biomarker values;

f) a weighted sum of at least two biomarker values;

h) A sigmoid function of at least two biomarker values.

104. A method according to claim 102, wherein the method includes determining at least one derived biomarker value corresponding to a ratio of two measured biomarker values.

105. The method according to any one of claims 101 to 104, wherein the method comprises combining at least two biomarker values to determine an indicator value representative of the indicator.

106. A method according to claim 105, wherein the method includes combining at least two biomarker values using a combining function that is at least one of:

a) an additive model;

b) a linear model;

c) a support vector machine;

d) a neural network model;

e) a random forest model;

f) a regression model;

g) a genetic algorithm;

h) an annealing algorithm;

i) a weighted sum;

j) a nearest neighbor model; and the number of the first and second groups,

k) and (4) a probability model.

107. The method of claim 105 or claim 106, wherein at least one of the at least two biomarkers is a derived biomarker.

108. The method of any one of claims 101-107, wherein the method comprises:

109. The method of any one of claims 101-108, wherein the method is performed at least in part using an electronic processing device.

110. The method of any one of claims 101 to 109, wherein the method includes, in the electronic processing device:

111. The method of any of claims 101-110, wherein the cross-correlation range is at least one of:

a)±0.8；

b)±0.7；

c)±0.6；

d)±0.5；

e)±0.4；

f)±0.3；

g) Plus or minus 0.2; and the number of the first and second groups,

h)±0.1。

112. the method of any one of claims 101 to 111, wherein each biomarker has a condition correlation to the presence, absence, extent, or prognosis of the at least one condition that is outside of a condition correlation range, said condition correlation range being between ± 0.3.

113. The method of claim 112, wherein the condition relevance range is at least one of:

a)±0.9；

b)±0.8；

c)±0.7；

d)±0.6；

e) plus or minus 0.5; and the number of the first and second groups,

f)±0.4。

114. the method of any of claims 101-113, wherein the performance threshold is indicative of an interpretation variance of at least one of:

a)0.4；

b)0.5；

c)0.6；

d)0.7；

e) 0.8; and the number of the first and second groups,

f)0.9。

115. the method of any one of claims 101 to 114, wherein the biomarker value is indicative of the level or abundance of a molecule selected from one or more of a nucleic acid molecule and a proteinaceous molecule.

116. The method of any one of claims 101 to 115, wherein the method comprises generating a representation of the indicator.

117. The method of claim 116, wherein the representing comprises:

a) an alphanumeric indication of the indicator;

118. The method of any one of claims 101-117, wherein the method comprises:

b) determining a likelihood based on a result of the comparison.

119. The method of claim 118, wherein the indicator reference is based on at least one of:

a) an indicator threshold range;

b) an indicator threshold; and the number of the first and second groups,

c) the indicator distribution.

120. The method of claim 118 or claim 119, wherein the indicator reference is derived from indicators determined for a number of individuals in a reference population.

121. The method of claim 120, wherein the indicator reference is based on a distribution of indicators determined for a set of reference populations, the set consisting of individuals diagnosed as having the medical condition or lacking the medical condition.

122. The method of claim 120 or claim 121, wherein the reference population comprises:

a) a plurality of individuals of different genders;

b) a plurality of individuals of different ethnicities;

c) a plurality of healthy individuals;

d) a plurality of individuals having at least one diagnosed medical condition;

e) A plurality of individuals without the at least one diagnosed medical condition;

h) a first group of individuals and a second group of individuals, the first group of individuals having a diagnosed medical condition and the second group not having the diagnosed medical condition.

123. A method according to any one of claims 120 to 122, wherein the indicator is for use in determining a likelihood of a biological subject having at least one medical condition, and wherein the reference population comprises:

b) an individual diagnosed as having the at least one medical condition;

b) an individual diagnosed as free of the at least one medical condition; and the number of the first and second groups,

c) a healthy individual.

124. The method of any one of claims 118-123, wherein the indicator reference is retrieved from a database.

125. The method of any of claims 118-124, wherein the likelihood is based on a probability generated using a result of the comparison.

126. A method according to one of claims 118-125, wherein the indicator is for determining a likelihood of the subject having a first condition or a second condition, and wherein the method comprises:

b) determining the likelihood based on a result of the comparison.

127. The method of claim 126, wherein the method comprises:

128. The method of claim 126 or claim 127, wherein the first indicator reference and the second indicator reference are distributions of indicators determined for a first set of reference populations and a second set of reference populations, the first set and the second set consisting of individuals diagnosed as having the first condition or individuals of the second condition, respectively.

129. The method of any one of claims 101 to 128, wherein the method comprises:

c) determining an indicator at least in part using the pair of biomarker values;

130. the method of claim 129, wherein the method comprises determining the indicator at least in part using a ratio of concentrations of the polynucleotide expression products.

131. The method of claim 129 or claim 130, wherein the method comprises:

a) quantifying the polynucleotide expression product by:

132. The method of claim 131, wherein the amplification amount is at least one of:

a) The cycle time;

b) the number of cycles;

c) a cycling threshold;

d) the amplification time; and the number of the first and second groups,

e) relative to the amount of amplification of another amplified product.

133. The method of claim 131 or claim 132, wherein the method comprises:

134. The method of any one of claims 101 to 133, wherein the immune system biomarker is a biomarker of the biological subject's immune system that is altered as part of an inflammatory response to the injury or pathogenic damage, or the biological subject's immune system has an altered level of expression of a biomarker as part of an inflammatory response to the injury or pathogenic damage.

135. A method according to any one of claims 101 to 134, wherein the indicator is for determining a likelihood that the subject has at least one of inSIRS and ipSIRS, and wherein the method includes:

b) determining a second pair of biomarker values indicative of the concentration of the polynucleotide expression product of the CEACAM4 gene and the LAMP1 gene; and the number of the first and second groups,

136. A method according to any one of claims 101 to 134, wherein the indicator is for determining a likelihood of the subject having inSIRS or ipSIRS, and wherein the method includes:

137. A method according to one of the claims 101 to 134, wherein the indicator is for determining a likelihood of the subject having inSIRS or a healthy condition, and wherein biomarker values are determined from at least one immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:

b) The second IRS immune system biomarker group consists of polynucleotide expression products and/or polypeptide expression products from group B IRS immune system biomarker genes as defined herein.

138. A method according to any one of the claims 101 to 134, wherein the indicator is for determining a likelihood of the subject having ipSIRS or a healthy condition, and wherein biomarker values are determined from at least one immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:

139. A method according to one of the claims 101 to 134, wherein the indicator is for determining a likelihood of the subject having inSIRS or ipSIRS, and wherein biomarker values are determined from at least one immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:

140. A method according to any one of the claims 101 to 134, wherein the indicator is for determining a likelihood of the subject having inSIRS or ipSIRS, and wherein biomarker values are determined from at least one immune system biomarker in each of first, second, third and fourth IRS immune system biomarker groups, wherein:

141. The method of claim 131, wherein the first IRS immune system biomarker is a PLA2G7 expression product, wherein the second IRS immune system biomarker is a PLAC8 expression product, wherein the third IRS immune system biomarker is a CEACAM4 expression product, and wherein the fourth IRS immune system biomarker is a LAMP1 expression product.

142. A method according to any one of claims 101 to 134, wherein the indicator is for determining a likelihood of the subject having mild sepsis or severe sepsis, and wherein biomarker values are determined from at least one immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:

143. A method according to any one of claims 101 to 134, wherein the indicator is for determining a likelihood of the subject having mild sepsis or septic shock, and wherein biomarker values are determined from at least one immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:

144. A method according to any one of claims 101 to 134, wherein the indicator is for determining a likelihood of the subject having severe sepsis or septic shock, and wherein biomarker values are determined from at least one immune system biomarker in each of first and second IRS immune system biomarker groups, wherein:

145. Apparatus for determining an indicator for use in assessing the likelihood of a biological subject having the presence, absence, extent or prognosis of at least one medical condition, the apparatus comprising electronic processing means for: