WO2019110706A1

WO2019110706A1 - Methods for predicting the risk of developing a sepsis or a systemic inflammatory response syndrome (sirs)

Info

Publication number: WO2019110706A1
Application number: PCT/EP2018/083735
Authority: WO
Inventors: Catherine Nguyen; Yasmine LABIAD; Régis COSTELLO
Original assignee: Aix Marseille Universite; Institut National de la Sante et de la Recherche Medicale INSERM; Assistance Publique Hopitaux de Marseille APHM
Current assignee: Aix Marseille Universite; Institut National de la Sante et de la Recherche Medicale INSERM; Assistance Publique Hopitaux de Marseille APHM
Priority date: 2017-12-08
Filing date: 2018-12-06
Publication date: 2019-06-13
Anticipated expiration: 2020-06-08

Abstract

The present invention relates to methods for predicting the risk of developing a sepsis or a systemic inflammatory response syndrome (SIRS). Sepsis and SIRS are infectious complications representing major clinic issues. However, it is not possible to predict which patients will develop a SIRS and/or a sepsis and so to adjust the antibiotic or antifungal prophylaxis to the specific risk profile of each patient. Using high throughput transcriptomic and bioinformatics analysis, the inventors showed that eleven genes were differentially expressed and predicted the development of SIRS or sepsis at least 48 hours before its occurrence. In particular, the present invention relates to a method for predicting the risk of developing sepsis or/and systemic inflammatory response syndrome (SIRS) in a subject in need thereof, said method comprising determining the expression level of at least one gene among the eleven genes.

Description

METHODS FOR PREDICTING THE RISK OF DEVELOPING A SEPSIS OR A SYSTEMIC INFLAMMATORY RESPONSE SYNDROME (SIRS)

FIELD OF THE INVENTION:

The present invention relates to methods for predicting the risk of developing a sepsis or a systemic inflammatory response syndrome (SIRS).

BACKGROUND OF THE INVENTION:

Autologous hematopoietic stem cell transplantation (Auto-HSCT) is based on the administration of myelo suppressive high-dose chemotherapy, followed by infusion of autologous hematopoietic stem cells to obtain a faster hematologic reconstitution. Hematopoietic stem cells (HSCs) infusion reduces chemotherapy-induced myelosuppression period and procedure-related mortality rate below 3% (Kumick N. Autologous and Isologous Bone Marrow Storage and Infusion in the Treatment of Myelo - Suppression. Transfusion. 1962;2(3): 178-87) (McFarland W, et al. Autologous bone marrow infusion as an adjunct in therapy of malignant disease. Blood. 1959;14(5):503-21) (Clifford P, et al. Nitrogen-mustard therapy combined with autologous marrow infusion. The Lancet. 1961 ;277(7179):687-90). With few exceptions (solid tumors, autoimmune diseases), auto-HSCT is essentially indicated for hematological malignancies treatment and considered as a standard treatment in young patients with multiple myeloma and for relapsed or refractory lymphoma.

Immediately after auto-HSCT, hematologic reconstitution and infectious complications are the two major clinic issues to monitor in transplanted patients. Besides direct toxicity of conditioning regimens, deep and prolonged neutropenia exposes patients to significant risks of infection. Saprophytic gram-negative bacilli (such as Escherichia coli) are the most common cause of septic shock (Veeraputhiran M, et al. BEAM Conditioning Regimen Has Higher Toxicity Compared With High-Dose Melphalan for Salvage Autologous Hematopoietic Stem Cell Transplantation in Multiple Myeloma. Clinical lymphoma, myeloma & leukemia. 2015; l5(9):531-5.) and chronic immunosuppression exposes to the risk of fungal infection. An anti-fimgal prophylaxis is usually administered (Maertens J, et al. European guidelines for antifungal management in leukemia and hematopoietic stem cell transplant recipients: summary of the ECIL 3— 2009 update. Bone marrow transplantation. 2011 ;46(5):709- 18) but antimicrobial prophylaxis is less often given because its effectiveness is not clearly established and increases the risk of Clostridium difficile diarrhea (Sohn BS, et al. The role of prophylactic antimicrobials during autologous stem cell transplantation: a single-center experience. European journal of clinical microbiology & infectious diseases: official publication of the European Society of Clinical Microbiology. 20l2;31(7): 1653-61). Unfortunately, it is not possible to foresee which patients will develop a SIRS and/or a sepsis. Therefore, it remains impossible to adjust the antibiotic or antifungal prophylaxis to the specific risk profile of each patient.

SUMMARY OF THE INVENTION:

The present invention relates to methods for predicting the risk of developing a sepsis or a systemic inflammatory response syndrome (SIRS). In particular, the present invention is defined by the claims.

DETAILED DESCRIPTION OF THE INVENTION:

The goal of the inventors was to determine and identify a systemic inflammatory response syndrome (SIRS) and/or sepsis predictive transcriptomic signature in patients receiving auto-HSCT.

Using high throughput transcriptomic and bio informatics analysis, they analyzed gene expression modulation in peripheral blood mononuclear cells (PBMCs) in 21 patients undergoing auto-HSCT for hematological malignancies (lymphoma or multiple myeloma [MM]).

The inventors showed that eleven genes (CHAT, CNN3, ANKRD42, LOC100505725, EDAR, GPAT2, ENST00000390425, MTRM8, C6orfl92 and LOC 10289230 and XLOC- 005643) were differentially expressed and predicted the development of SIRS or sepsis at least 48 hours before its occurrence.

The possibility of SIRS or sepsis occurrence prediction opens up to new therapeutic strategies based on an antibiotic and/or antifungal prophylaxis adapted to the specific risk profile of each patient.

Prediction methods

Accordingly, a first aspect of the present invention relates to a method for predicting the risk of developing sepsis or/and systemic inflammatory response syndrome (SIRS) in a subject in need thereof, said method comprising the steps of i) determining the expression level of at least one gene among CHAT, CNN3, ANKRD42, LOC100505725, EDAR, GPAT2, ENST00000390425, MTRM8, C6orfl92, LOC 10289230 and XLOC-005643 in a biological sample obtained from said subject, ii) comparing the expression level of each gene determined at step i) with its respective predetermined reference level and iii) concluding the subject has a high risk of developing sepsis or/and systemic inflammatory response syndrome (SIRS) when the level determined at step i) for at least one gene among CHAT, CNN3, ANKRD42, LOC100505725, EDAR, GPAT2, ENST00000390425, MTRM8, C6orfl92 and LOC 10289230 is higher than the respective predetermined reference level and the level determined at step i) for XLOC-005643 is lower than its predetermined reference level, or concluding the subject has a low risk of developing sepsis or/and systemic inflammatory response syndrome (SIRS) when the level determined at step i) for at least one gene among CHAT, CNN3, ANKRD42, LOC100505725, EDAR, GPAT2, ENST00000390425, MTRM8, C6orfl92 and LOC 10289230 is lower than the respective predetermined reference level and the level determined at step i) for XLOC-005643 is higher than its predetermined reference level.

In one embodiment, the step i) of the method for predicting the risk of developing sepsis or/and systemic inflammatory response syndrome (SIRS) according to the invention comprises the determination of the expression level of 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10 genes selected from the list of CHAT, CNN3, ANKRD42, LOC100505725, EDAR, GPAT2, ENST00000390425, MTRM8, C6orfl92, LOC10289230 and XLOC-005643.

As used herein, the term“systemic inflammatory response syndrome (SIRS)” has its general meaning in the art and refers to the systemic inflammatory response to a variety of severe clinical symptoms with at least two of the following criteria: a) Temperature higher than 38°C or lower than 36°C b) Heart rate higher than 90 beats/min c) Respiratory rate higher than 20 breaths/min or PaC02 lower than 32 mmHg d) White blood cell counts higher than 12,000 cells/mm3 or lower than 4,000 cells/mm3, or the presence of more than 10% immature neutrophils (the American College of Chest Physicians society of Critical Care Medicine consensus).

As used herein, the term“sepsis” has its general meaning in the art and is used to identify the continuum of the clinical response to infection. Patients with sepsis present evidences of infection and clinical manifestations of inflammation. Sepsis is defined as SIRS secondary to documented or suspected infection. As used herein, the term“CHAT” refers to the gene coding for the protein CHAT (choline acetyltransferase) which is a transferase enzyme responsible for the synthesis of the neurotransmitter acetylcholine (CHAT protein Uniprot reference: P28329 for Homo sapiens and Q8BQV2 for Mus musculus ) (CHAT gene NCBI gene ID: 1103 for Homo sapiens and 12647 for Mus musculus).

As used herein, the term“CNN3” refers to the gene coding for the protein Calponin 3 which is a filament-associated protein that is implicated in the regulation and modulation of smooth muscle contraction (CNN3 protein Uniprot reference: Q 15417 for Homo sapiens and Q9DAW9 for Mus musculus) (CNN3 gene NCBI gene ID: 1266 for Homo sapiens and 71994 for Mus musculus).

As used herein, the term“ANKRD42” refers to the gene coding for the protein ANKRD42 which is an ankyrin repeat protein involved with calcium ion bonding (ANKRD42 protein Uniprot reference: Q8N9B4 for Homo sapiens and Q3V096 for Mus musculus) (ANKRD42 gene NCBI gene ID: 338699 for Homo sapiens and 73845 for Mus musculus).

As used herein, the term“LOCl00505725”also known as PLCG1-AS1 (PLCG1 Antisense RNA 1) refers to an RNA Gene, and is affiliated with the non-coding RNA class (Ensembl: ENSG00000226648). Genomic Location: Chromosome 20 Start: 41,098,329 bp End: 41,138,003 bp Size: 40,010 bases Orientation: Minus strand. LOC100505725 is express as ubiquitous expression in lymph node and 25 other tissues.

As used herein, the term“EDAR” refers to the gene coding for the protein Ectodysplasin A receptor (EDAR) which is cell surface receptor for ectodysplasin A (EDAR protein Uniprot reference: Q9UNE0 for Homo sapiens and Q9R187 for Mus musculus) (EDAR gene NCBI gene ID: 10913 for Homo sapiens and 13608 for Mus musculus).

As used herein, the term“GPAT2” refers to the gene coding for the protein Glycerol-3- phosphate acyltransferase 2, mitochondrial, which esterifies acyl-group from acyl-ACP to the sn-l position of glycerol-3 -phosphate, an essential step in glycerolipid biosynthesis (GPAT2 protein Uniprot reference: Q6NUI2 for Homo sapiens and Q14DK4 for Mus musculus) (GPAT2 gene NCBI gene ID: 150763 for Homo sapiens and 215456 for Mus musculus).

As used herein, the term“ENST00000390425” also known as TRAV3 (T-Cell Receptor Alpha Variable 3 (Gene/Pseudogene)) refers to a Protein Coding gene (Ensembl: ENSG00000211777). Location Chromosome 14: start :2l,723,7l3 end 21,724,321. As used herein, the term “MTRM8” refers to the gene coding for the protein myotubularin related protein 8 which is a phosphatase that acts on lipids with a phosphoinositol headgroup (MTRM8protein Uniprot reference: Q96EF0 for Homo sapiens) (MTRM8 gene NCBI gene ID: 55613 for Homo sapiens).

As used herein, the term“C6orfl92” (also known as“SLC18B1”) refers to the gene coding for the protein solute carrier family 18 member Bl (C6orfl92 protein Uniprot reference: Q6NT16 for Homo sapiens) (C6orfl92 gene NCBI gene ID: 116843 for Homo sapiens).

As used herein, the term“LOC 10289230” refers to the gene localized on Chr 5: 98,929,134-98,931,009.

As used herein, the term“XLOC-005643” refers to lnc-CMAHP-l : l / linc-FAM65B- 1/RP3-425P12.2 gene which is localized on Chr 6: 25,061,853-25,063,735.

As used herein, the term“subject” denotes a mammal, such as a rodent, a feline, a canine, and a primate. Preferably, a subject according to the invention is a human.

In one embodiment, the subject is a child.

In one embodiment, the subject is an adult.

In one embodiment, the subject is an elderly.

In one embodiment, the subject is a subject who has undergone a transplantation.

In one embodiment, the subject is a subject who has undergone an autologous hematopoietic stem cell transplantation.

In one embodiment, the subject is a subject who has undergone an autologous hematopoietic stem cell transplantation as a standard treatment in multiple myeloma or relapsed or refractory high-grade lymphoma (non-Hodgkin and Hodgkin lymphoma).

In one embodiment, the subject is a subject who has undergone a chemotherapy and a transplantation.

In one embodiment, the compound used in the chemotherapy according to the invention is the melphalan for patients with MM and BEAM (carmustin, etoposide, cytarabin and melphalan) for patients with lymphoma. In one embodiment, the subject is a subject who has undergone a chemotherapy and an autologous hematopoietic stem cell transplantation.

In one embodiment, the subject is a subject who has undergone a chemotherapy and an autologous hematopoietic stem cell transplantation as a standard treatment in multiple myeloma or relapsed or refractory high-grade lymphoma (non-Hodgkin and Hodgkin lymphoma).

In one embodiment, the subject is a subject who has undergone a high dose of chemotherapy and a transplantation.

In one embodiment, the subject is a subject who has undergone a high dose of chemotherapy and an autologous hematopoietic stem cell transplantation.

In one embodiment, the subject is a subject who has undergone a high dose of chemotherapy and an autologous hematopoietic stem cell transplantation as a standard treatment in multiple myeloma or relapsed or refractory high-grade lymphoma (non-Hodgkin and Hodgkin lymphoma).

In one embodiment, the subject is a subject having immune deficiency.

In one embodiment, the subject is a subject having acquired immune deficiency syndrome.

In one embodiment, the subject is a subject staying at the hospital or any medical center for a long period (several weeks, several months or several years).

The term "biological sample" is used herein in its broadest sense. A biological sample is generally obtained from a subject. A sample may be of any biological tissue or fluid with which biomarker of the present invention may be assayed. Frequently, a sample will be a "clinical sample", i.e., a sample derived from a patient. Such samples include, but are not limited to, bodily fluids which may or may not contain cells, e.g., blood (e.g., whole blood, serum or plasma), synovial fluid, saliva, tissue or fine needle biopsy samples, and archival samples with known diagnosis, treatment and/or outcome history. Biological samples may also include sections of tissues such as frozen sections taken for histological purposes. The term "biological sample" also encompasses any material derived by processing a biological sample. Derived materials include, but are not limited to, cells (or their progeny) isolated from the sample, or proteins extracted from the sample. Processing of a biological sample may involve one or more of: filtration, distillation, extraction, concentration, inactivation of interfering components, addition of reagents, and the like.

In one embodiment, the biological sample is blood sample. As used herein the term“blood sample” has its general meaning in the art and refers to a whole blood sample, a plasma sample or a serum sample.

In one embodiment, the biological sample is peripheral blood mononuclear cells (PBMCs) sample.

The term“PBMC” or“peripheral blood mononuclear cells” as used herein, refers to whole PBMC, i.e. to a population of white blood cells having a round nucleus, which has not been enriched for a given sub-population.

As used herein, the term "predicting" refers to a probability or likelihood for a subject to develop an event. Preferably, the event is herein sepsis or/and systemic inflammatory response syndrome.

As used herein, the term "risk" refers to the probability that an event will occur over a specific time period, such as the onset of sepsis or/and systemic inflammatory response syndrome, and can mean a subject's "absolute" risk or "relative" risk. Absolute risk can be measured with reference to either actual observation post-measurement for the relevant time cohort, or with reference to index values developed from statistically valid historical cohorts that have been followed for the relevant time period. Relative risk refers to the ratio of absolute risks of a patient compared either to the absolute risks of low risk cohorts or an average population risk, which can vary by how clinical risk factors are assessed.

Methods for determining the expression level of CHAT, CNN3, ANKRD42, LQC100505725. EDAR. GPAT2. ENST00000390425. MTRM8. C6orfl92. LQC10289230 and XLOC-005643:

Determination of the expression level of genes of interest may be performed by a variety of techniques. Generally, the expression level as determined is a relative expression level. For example, the determination comprises contacting the sample with selective reagents such as probes or ligands, and thereby detecting the presence, or measuring the amount, of nucleic acids or polypeptides of interest originally in said sample. Contacting may be performed in any suitable device, such as a plate, microtiter dish, test tube, well, glass, column, and so forth. In specific embodiments, the contacting is performed on a substrate coated with the reagent, such as a nucleic acid array or a specific ligand array. The substrate may be a solid or semi-solid substrate such as any suitable support comprising glass, plastic, nylon, paper, metal, polymers and the like. The substrate may be of various forms and sizes, such as a slide, a membrane, a bead, a column, a gel, etc. The contacting may be made under any condition suitable for a detectable complex, such as a nucleic acid hybrid or an antibody-antigen complex, to be formed between the reagent and the nucleic acids or polypeptides of the biological sample.

In a particular embodiment of the invention, the expression level of genes of interest gene may be determined by determining the quantity of mRNA.

Methods for determining the quantity of mRNA are well known in the art. For example the nucleic acid contained in the samples is first extracted according to standard methods, for example using lytic enzymes or chemical solutions or extracted by nucleic-acid-binding resins following the manufacturer's instructions. The extracted mRNA is then detected by hybridization (e.g., Northern blot analysis) and/or amplification (e.g., RT-PCR). Quantitative or semi-quantitative RT-PCR is preferred. Real-time quantitative or semi-quantitative RT-PCR is particularly advantageous.

Nucleic acids having at least 10 nucleotides and exhibiting sequence complementarity or homology to the mRNA of interest herein find utility as hybridization probes. It is understood that such nucleic acids need not be identical, but are typically at least about 80% identical to the homologous region of comparable size, more preferably 85% identical and even more preferably 90-95% identical. Probes typically comprise single- stranded nucleic acids of between 10 to 1000 nucleotides in length, for instance of between 10 and 800, more preferably of between 15 and 700, typically of between 20 and 500. The probes and primers are "specific" to the nucleic acids they hybridize to, i.e. they preferably hybridize under high stringency hybridization conditions (corresponding to the highest melting temperature Tm, e.g., 50 % formamide, 5x or 6x SCC. SCC is a 0.15 M NaCl, 0.015 M Na-citrate).

In the context of the invention, "hybridization" relates to the fact of obtaining a close interaction of the nucleotide probe and the target region that is expected to be revealed by the detection of the nucleotide probe. Such an interaction can be achieved by the formation of hydrogen bonds between the nucleotide probe and the target sequence, which is typical of the interactions between complementary nucleotide molecules capable of base pairing. Hydrogen bonds can be found, for example, in the annealing of two complementary strands of DNA. It will be advantageous to use nucleic acids in combination with appropriate means, such as a detectable label, for detecting hybridization. A wide variety of appropriate indicators are known in the art including, fluorescent, radioactive, enzymatic or other ligands.

Conventional methods and reagents for isolating RNA from a sample comprise High Pure miRNA Isolation Kit (Roche), Trizol (Invitrogen), Guanidinium thiocyanate-phenol- chloroform extraction, PureLink™ miRNA isolation kit (Invitrogen), PureLink Micro-to- Midi Total RNA Purification System (invitrogen), RNeasy kit (Qiagen), Oligotex kit (Qiagen), phenol extraction, phenol-chloroform extraction, TCA/acetone precipitation, ethanol precipitation, Column purification, Silica gel membrane purification, PureYield™ RNA Midiprep (Promega), PolyATtract System 1000 (Promega), Maxwell® 16 System (Promega), SV Total RNA Isolation (Promega), geneMAG-RNA / DNA kit (Chemicell), TRI Reagent® (Ambion), RNAqueous Kit (Ambion), ToTALLY RNA™ Kit (Ambion), Poly(A)Purist™ Kit (Ambion) and any other methods, commercially available or not, known to the skilled person.

In one embodiment, the expression level of one or more mRNAs is determined by the quantitative polymerase chain reaction (QPCR) technique. The QPCR may be performed using chemicals and/or machines from a commercially available platform. The QPCR may be performed using QPCR machines from any commercially available platform; such as Prism, geneAmp or StepOne Real Time PCR systems (Applied Biosystems), LightCycler (Roche), RapidCycler (Idaho Technology), MasterCycler (Eppendorf), BioMark™ HD System (Fluidigm), iCycler iQ system, Chromo 4 system, CFX, MiniOpticon and Opticon systems (Bio-Rad), SmartCycler system (Cepheid), RotorGene system (Corbett Fifescience), MX3000 and MX3005 systems (Stratagene), DNA Engine Opticon system (Qiagen), Quantica qPCR systems (Techne), InSyte and Syncrom cycler system (BioGene), DT-322 (DNA Technology), Exicycler Notebook Thermal cycler, TF998 System (lanlong), Fine-Gene-K systems (Bioer Technology), or any other commercially available platform. The QPCR may be performed using chemicals from any commercially available platform, such as NCode EXPRESS qPCR or EXPRESS qPCR (Invitrogen), Taqman or SYBR green qPCR systems (Applied Biosystems), Real-Time PCR reagents (Eurogentec), iTaq mix (Bio-Rad), qPCR mixes and kits (Biosense), and any other chemicals, commercially available or not, known to the skilled person. The QPCR reagents and detection system may be probe-based, or may be based on chelating a fluorescent chemical into double-stranded oligonucleotides. The QPCR reaction may be performed in a tube; such as a single tube, a tube strip or a plate, or it may be performed in a micro fluidic card in which the relevant probes and/or primers are already integrated.

In a particular embodiment, the expression level of genes of interest may be determined by determining of the quantity of proteins encoded by the genes of interest.

Such methods comprise contacting the sample with a binding partner capable of selectively interacting with the protein present in said sample. The binding partner is generally an antibody that may be polyclonal or monoclonal, preferably monoclonal.

As used herein, the term "monoclonal antibody" refers to a population of antibody molecules that contains only one species of antibody combining site capable of immunoreacting with a particular epitope. A monoclonal antibody thus typically displays a single binding affinity for any epitope with which it immunoreacts. A monoclonal antibody may therefore contain an antibody molecule having a plurality of antibody combining sites, each immunospecific for a different epitope, e.g. a bispecific monoclonal antibody. Although historically a monoclonal antibody was produced by immortalization of a clonally pure immunoglobulin secreting cell line, a monoclonally pure population of antibody molecules can also be prepared by the methods of the invention.

Laboratory methods for preparing monoclonal antibodies are well known in the art (see, for example, Harlow et al., 1988). Monoclonal antibodies (mAbs) may be prepared by immunizing purified proteins of interest into a mammal, e.g. a mouse, rat and the like mammals. The antibody-producing cells in the immunized mammal are isolated and fused with myeloma or heteromyeloma cells to produce hybrid cells (hybridoma). The hybridoma cells producing the monoclonal antibodies are utilized as a source of the desired monoclonal antibody. This standard method of hybridoma culture is described in Kohler and Milstein (1975).

While mAbs can be produced by hybridoma culture the invention is not to be so limited. Also contemplated is the use of mAbs produced by an expressing nucleic acid cloned from a hybridoma of this invention. That is, the nucleic acid expressing the molecules secreted by a hybridoma of this invention can be transferred into another cell line to produce a transformant. The transformant is genotypically distinct from the original hybridoma but is also capable of producing antibody molecules of this invention, including immunologically active fragments of whole antibody molecules, corresponding to those secreted by the hybridoma. See, for example, U.S. Pat. No. 4,642,334 to Reading; European Patent Publications No. 0239400 to Winter et al. and No. 0125023 to Cabilly et al.

Antibody generation techniques not involving immunisation are also contemplated such as for example using phage display technology to examine naive libraries (from non- immunised animals); see Barbas et al. (1992), and Waterhouse et al. (1993).

Alternatively, binding agents other than antibodies may be used for the purpose of the invention. These may be for instance aptamers, which are a class of molecule that represents an alternative to antibodies in term of molecular recognition. Aptamers are oligonucleotide or oligopeptide sequences with the capacity to recognize virtually any class of target molecules with high affinity and specificity. Such ligands may be isolated through Systematic Evolution of Ligands by Exponential enrichment (SELEX) of a random sequence library, as described in Tuerk C. and Gold L., 1990. The random sequence library is obtainable by combinatorial chemical synthesis of DNA. In this library, each member is a linear oligomer, eventually chemically modified, of a unique sequence. Possible modifications, uses and advantages of this class of molecules have been reviewed in Jayasena S.D., 1999. Peptide aptamers consists of a conformationally constrained antibody variable region displayed by a platform protein, such as E. coli Thioredoxin A that are selected from combinatorial libraries by two hybrid methods (Colas et al, 1996).

The binding partners of the invention such as antibodies or aptamers, may be labelled with a detectable molecule or substance, such as a fluorescent molecule, a radioactive molecule or any others labels known in the art. Labels are known in the art that generally provide (either directly or indirectly) a signal. As used herein, the term "labelled", with regard to the antibody or aptamer, is intended to encompass direct labeling of the antibody or aptamer by coupling (i.e., physically linking) a detectable substance, such as a radioactive agent or a fluorophore (e.g. fluorescein isothiocyanate (FITC) or phycoerythrin (PE) or lndocyanine (Cy5)) to the antibody or aptamer, as well as indirect labelling of the probe or antibody by reactivity with a detectable substance. An antibody or aptamer of the invention may be labelled with a radioactive molecule by any method known in the art. The aforementioned assays generally involve the coating of the binding partner (ie. antibody or aptamer) in a solid support. Solid supports which can be used in the practice of the invention include substrates such as nitrocellulose (e. g., in membrane or microtiter well form); polyvinylchloride (e. g., sheets or microtiter wells); polystyrene latex (e.g., beads or microtiter plates); polyvinylidine fluoride; diazotized paper; nylon membranes; activated beads, magnetically responsive beads, and the like.

In another embodiment of the invention, the measurement of proteins of interst in the sample may be achieved by a cytometric bead array system wherein the antibodies that bind to the biomarkers are coated directly or indirectly on beads. Typically, Luminex® technology which is a new technology based on fluorescent detection using a flow cytometer, microbeads dyed with multiple fluorescent colours and lasers detection may be used.

For example, the level of a biomarker protein may be measured by using standard electrophoretic and immunodiagnostic techniques, including immunoassays such as competition, direct reaction, or sandwich type assays. Such assays include, but are not limited to, Western blots; agglutination tests; enzyme- labeled and mediated immunoassays, such as ELISAs; biotin/avidin type assays; radioimmunoassays; Immunoelectrophoresis; immunoprecipitation.

More particularly, an ELISA method can be used, wherein the wells of a microtiter plate are coated with a set of antibodies against proteins of interest. A sample containing or suspected of containing proteins of interest is then added to the coated wells. After a period of incubation sufficient to allow the formation of antibody-antigen complexes, the plate(s) can be washed to remove unbound moieties and a detectably labeled secondary binding molecule added. The secondary binding molecule is allowed to react with any captured sample marker protein, the plate washed and the presence of the secondary binding molecule detected using methods well known in the art.

Measuring the level of a bio marker protein (with or without immunoassay-based methods) may also include separation of the proteins: centrifugation based on the protein's molecular weight; electrophoresis based on mass and charge; HPLC based on hydrophobicity; size exclusion chromatography based on size; and solid-phase affinity based on the protein's affinity for the particular solid-phase that is use. Once separated, proteins of interest may be identified based on the known "separation profile" e. g., retention time, for that protein and measured using standard techniques.

Alternatively, the separated proteins may be detected and measured by, for example, a mass spectrometer.

Prevention methods

In one embodiment, the present invention relates to a method of preventing sepsis or/and systemic inflammatory response syndrome in a subject comprising:

Predicting the risk of developing sepsis or/and systemic inflammatory response syndrome by using the method of the present invention;

Administering to the subject a therapeutically effective amount of a pre-emptive therapy when it is concluded that the subject has a high risk of developing sepsis or/and systemic inflammatory response syndrome.

As used herein, the term“pre-emptive therapy” refers to a therapy that it is targeted toward high-risk patients, is timed to be maximally effective in aborting impending disease, and is administered for a defined, usually short duration.

The pre-emptive therapy comprises any drug or compound usually administrated to treat a sepsis or/and systemic inflammatory response syndrome.

In one embodiment, the pre-emptive drug therapy consists on administrating antifungal compound.

In one embodiment, the pre-emptive drug therapy consists on administrating antibiotics.

In one embodiment, the pre-emptive drug therapy consists on administrating antifungal compound and antibiotics.

In one embodiment, the antibiotics treatment include, but is not limited to, ceftriaxone, cefotaxime, vancomycin, meropenem, cefepime, ceftazidime, cefuroxime, nafcillin, oxacillin, ampicillin, ticarcillin, ticarcillin/clavulinic acid (Timentin), ampicillin/sulbactam (Unasyn), azithromycin, trimethoprim-sulfamethoxazole, clindamycin, ciprofloxacin, levofloxacin, synercid, amoxicillin, amoxicillin/clavulinic acid (Augmentin), cefuroxime, trimethoprim/sulfamethoxazole, azithromycin, clindamycin, dicloxacillin, ciprofloxacin, levofloxacin, cefixime, cefpodoxime, loracarbef, cefadroxil, cefabutin, cefdinir, and cephradine. As used herein, the term "preventing” refers to the reduction in the risk of acquiring or developing a given condition.

The terms "administer" or "administration" refer to the act of injecting or otherwise physically delivering a substance as it exists outside the body into the subject, such as by mucosal, intradermal, intravenous, subcutaneous, intramuscular, intra-articular delivery and/or any other method of physical delivery described herein or known in the art. When a disease, or a symptom thereof, is being treated, administration of the substance typically occurs after the onset of the disease or symptoms thereof. When a disease or symptoms thereof, are being prevented, administration of the substance typically occurs before the onset of the disease or symptoms thereof.

By a "therapeutically effective amount" is meant a sufficient amount of pre-emptive therapy drugs for use in a method for the prevention of sepsis or/and systemic inflammatory response syndrome at a reasonable benefit/risk ratio applicable to any medical treatment. It will be understood that the total daily usage of the compounds and compositions of the present invention will be decided by the attending physician within the scope of sound medical judgment. The specific therapeutically effective dose level for any particular subject will depend upon a variety of factors including the age, body weight, general health, sex and diet of the subject; the time of administration, route of administration, and rate of excretion of the specific compound employed; the duration of the treatment; and like factors well known in the medical arts. For example, it is well known within the skill of the art to start doses of the compound at levels lower than those required to achieve the desired therapeutic effect and to gradually increase the dosage until the desired effect is achieved. However, the daily dosage of the products may be varied over a wide range from 0.01 to 1,000 mg per adult per day. Typically, the compositions contain 0.01, 0.05, 0.1, 0.5, 1.0, 2.5, 5.0, 10.0, 15.0, 25.0, 50.0, 100, 250 and 500 mg of the active ingredient for the symptomatic adjustment of the dosage to the subject to be treated. A medicament typically contains from about 0.01 mg to about 500 mg of the active ingredient, typically from 1 mg to about 100 mg of the active ingredient. An effective amount of the drug is ordinarily supplied at a dosage level from 0.0002 mg/kg to about 20 mg/kg of body weight per day, especially from about 0.001 mg/kg to 7 mg/kg of body weight per day.

Kits of the invention A kit suitable for predicting the risk of developing sepsis or/and systemic inflammatory response syndrome (SIRS) in a subject in need thereof comprising:

- At least a means for determining the expression level of at least one gene among CHAT, CNN3, ANKRD42, LOC100505725, EDAR, GPAT2, ENST00000390425, MTRM8, C6orfl92, LOC10289230 and XLOC-005643 in a sample obtained from said subject,

- Instructions for use.

Typically the kit may include primers, probes, an antibody, or a set of antibodies. In a particular embodiment, the antibody or set of antibodies are labelled. The kit may also contain other suitably packaged reagents and materials needed for the particular detection protocol, including solid-phase matrices, if applicable, and standards.

The invention will be further illustrated by the following figures and examples. However, these examples and figures should not be interpreted in any way as limiting the scope of the present invention.

FIGURES:

Figure 1: Autologous stem cell transplantation protocol.

Three blood samples were collected for each patient, first sample (Tl) were collected before conditioning regimen. The second (T2) were collected after high dose chemotherapy and before the autologous stem cells transplantation and the last (T3) were collected at the end of the neutropenic phase. Abbreviation: G-CSF: Granulocyte-Colony Stimulating Factor, HSC: Hematopoietic Stem Cells, CXCR4: chemokine receptor type 4.

Figure 2: Differential genes expression flow-chart.

Pangenomic array for the transcriptomic analysis have been used, data were filtered and the controls were suppressed, co factors effects were tested based on their implication on variation of gene expression. A multivariate analysis - LIMMA - using the cofactors already tested was performed to define the gene differentially expressed using Bioconductor library. The LIMMA model is given in the following formula: Y = a + bΐ .T + b2.0 + b3.I + e, where Y is the explained variable, a is the global mean, bI .T, b2.0, b3.I, are the model coefficients, T, G, I, are the quantitative variables, bΐ T, is the treatment, b2.0, is the gene effects, b3.I is the infection effects and e is the error term. Than we performed multi-testing correction by fixing the threshold to 5 %, we adjusted the expression data based on the co-factors before to perform the hierarchical clustering * : Linear Models for Microarray data.

Figure 3: Transcriptomic validation signature by RT-qPCR.

Relative gene expression composed the transcriptomic signature of patients who develop SIRS or sepsis compared to patients who do not develop fever and SIRS * = 0.05; ** = 0.01; *** <0.01, NS = not significant.

EXAMPLE:

Material & Methods

Patients

The study was approved by the institutional review board of the Assistance Publique des Hopitaux de Marseille (AP-HM). Written informed consent was obtained from each patient. Patients were admitted in the hematology department of the Conception university hospital for undergoing auto-HSCT. All patients were under 65 years and were already followed in the hematology department for MM or high-grade lymphoma. Before auto-HSCT, patients were in complete remission (CR) or in partial remission (PR) after conventional chemotherapy. Inclusion criteria were the same as required for being eligible to auto-HSCT. Twenty fours patients were included in this study protocol. Twenty one were analyzed. Blood samples were collected for each patient at three moments: before the conditioning regimen (Tl), after the conditioning regimen and before the graft infusion (T2) and at the end of the neutropenic phase (T3) (Figure 1).

SIRS and sepsis definitions

SIRS and sepsis definition are based on the American College of Chest Physicians society of Critical Care Medicine consensus. SIRS is defined as the systemic inflammatory response to a variety of severe clinical symptoms with at least two of the following criteria: a) Temperature higher than 38°C or lower than 36°C b) Heart rate higher than 90 beats/min c) Respiratory rate higher than 20 breaths/min or PaC02 lower than 32 mmHg d) White blood cell counts higher than 12,000 cells/mm3 or lower than 4,000 cells/mm3, or the presence of more than 10% immature neutrophils. The last criterion cannot be considered in auto-HSCT context because of the aplasia phase following HSC infusion. Sepsis is defined as SIRS secondary to documented or suspected infection. Patients with severe sepsis are patients with sepsis and at least one organ dysfunction. Septic choc is defined by severe sepsis associated with refractory hypotension.

Conditioning regimens Conditioning regimen for therapeutic intensification was high dose of melphalan (200mg/m2) for patients with MM and BEAM for patients with lymphoma (carmustin 300 mg/m2 at day -6, etoposide 150 mg/m2 from day -5 to -2 twice daily, cytarabin 200mg/m2 from day -5 to -2 twice daily and melphalan l40mg/m2 day -1, with auto-HSCT on day 0).

Hematopoietic stem cell collection

HSCs mobilization required hematopoietic growth factors (G-CSF in most cases). Two procedures have been used to mobilize HSCs: steady-state collection: G-CSF was injected at the dose of 1 Opg/kg-a-day while bone marrow is in physiological conditions or mobilization chemotherapy: high doses of cyclophosphamide (between 1.5 to 4g / m2) associated to 5 Lig/kg- a-day of G-CSF. When HSCs collection fails with these usual procedures, plerixafor (SDF- 1/CXCR4 interaction inhibitor) was used at 240 pg / kg.

PBMCs isolation

Blood samples were centrifuged to separate plasma and other blood components. Concentrate blood was diluted and PBMCs were collected by Ficoll-Hystopaque density- gradient centrifugation. Isolated PBMCs were frozen at -80°C until RNA extraction.

RNA extraction, quantification quality controls

Depending on cell number, two kits were used for RNA extraction, the RNeasy Mini Kit Qiagen™ (Qiagen, Valencia, California), which accepts 10 million of cells and the RNeasy Midi Kit Qiagen™, which accepts up to 100 million cells. RNAs were extracted according to the Qiagen protocol. RNAs were quantified by NanoDrop 1000 (Nano Drop Technologies, San Diego, CA). Optical density was measured at 260 and 280 nm and the ratio 260/280 (> 1.8) indicates its purity.Extracted RNA quality was checked with Agilent 2100 Bioanalyzer™ (Agilent Technologies, Santa Clara, California). A score on a scale of 0 to 10 was automatically attributed to each sample and corresponded to RNA Integrity Number (RIN). Sample with a RIN under 7 was discarded. For samples presenting genomic DNA contamination, DNase treatment was performed using the RNase Free DNase Set kit of Qiagen®.

Pangenomic gene expression assay

One hundred nanogram of total RNA was labeled using One-Color Microarray-Based Gene Expression Analysis: Fow Input Quick Amp (FIQA) labeling protocol. 0.6 pg of the purified Cy3 labeled cRNA were hybridized for l7h at 65°C, at 60rpm, using the SurePrint G3 human GE 8x60K V2 chips (Agilent Technologies, Santa Clara, California). Microarrays were composed of 62 928 features. Probes synthesized on chips had a size of 60 nucleotides. Microarrays were washed using Gene Expression Wash Buffer Kit (Agilent Technologies) and scanned through standard Agilent protocol. Data were processed using Feature Extraction software.

Differential gene expression analysis

The library AgiND is implemented in R software in order to analyze and visualize data. AgiND was developed on Bioconductor library model (tagc.univ- mrs.fr/ComputationalBiology/AgiND/) and is used to diagnose data quality and data microarrays normalization. Quantile method was used to normalize data; the objective was to homogenize distribution of microarray intensity. A filter was applied on row data to delete controls, then a second filter was applied to delete genes which were expressed under the background in at least 80% of samples in each group (SIRS-, SIRS+, Sepsis+).

To test co-factors effects (gender, treatment, infection) on gene expression, GeneANOVA software was used to perform ANalysis Of Variance (ANOVA) on normalized data to determine an estimation of the contribution of each factor (genes, gender, treatment, infection) in gene expression variation. Global ANOVA model is given in the following formula: Y = m + bϋ G + bT T + bΐ I + e, where U is explained variable, m is global mean, bϋ, bT, bΐ, are model coefficients, G, T, I, are the quantitative variables, bq G, is the gene effects, bT T, is the treatment effects, bΐ I, the infection effects e, is the error term. Differential gene expression analysis was performed using linear Models for Microarray Data (Limma). Limma is a multivariate analysis and takes into account co-factors-effect tested by ANOVA analysis (treatment and gender) (Table 2). In order to determine infection impact on gene expression and on the heat map, new gene expression was calculated after subtracting gender and treatment co-factors effects. Unsupervised hierarchical clustering was applied on adjusted gene expression median adjusted data to group genes and samples, according to their expression using «TMeV» (Tigr MultiExperiment Viewer) MeV: MultiExperiment Viewer | Part of the TM4 Microarray Software Suite [http://www.tm4.org/mev/] Pearson correlation was used, and clusters were grouped on basis of average linkage method.

Support vector machines (SVMs)

SVMs were applied to predict classification of patients according to the predictive signature. SVMs use a training set in which genes known to be related to each other by function or samples related to a group are described as positive examples and genes or samples known not to be members of that class are labeled as negative examples. In this study samples were attributed to two groups: SIRS and sepsis patients in the first group and patients with no temperature in the second. They were combined into a set of training examples used by SVM to distinguish class members from non-members on the basis of expression data. After learning the class expression features, the SVM can be used to recognize and classify each sample on the basis of their expression. SVMs tool is implemented in TMeV software MeV: MultiExperiment Viewer | Part of the TM4 Microarray Software Suite [http ://www.tm4.org/mev/] .

High-throughput quantitative PCR

RT-PCR was performed using Superscript™ VILO™ MasterMix protocol described by Invitrogen. Sixty nanogram of RNA of each sample was retrotranscribed, using 4pL of Superscript™ VILO™ MasterMix, and RNase DNase Free water for a final volume of 20pL. Mix was incubated 10 min at 25°C, 60min at 42°C and 5min at 85°C. Quantitative PCR was performed with FlexSix array on BioMark™ HD (Fluidigm). A pre-amplification of each sample was applied according to the protocol provided by Fluidigm. One micro liter of 100mM of each primer was added to 176 pL of DNA Suspension Buffer for a final concentration of 500 nM. Then, 0.5pL of the previous mix was added to 1 pL of PreAmplification mix, l .25pL of cDNA and RNase DNase Free water for a final volume of 5pL. Holding step was done 2min at 95°C followed by 20 cycles of 15s at 95°C and 4 min at 60°C. The pre-amplification step was followed by an Exonuclease I treatment (BioLegend) to remove unincorporated primers. 2pL of diluted Exonuclease at 4 U/pL was added to each pre-amplification reaction. The digestion was performed during 30min at 37°C and the inactivation during l5min at 80°C. The final product was diluted 5-fold using 18m1 of TE Buffer (10 mM Tris-HCl, lmM EDTA). The diluted reaction products were stored at -20°C. In a second part, 12 x 12 (samples x primers) qRT-PCR reactions are performed for each primer pair on each sample on the 12 x 12 array (FlexSix). We used the EvaGreen detection assay for following standard Fluidigm protocols. Ct values were calculated from the system software Bio mark Real-Time PCR Analysis (Fluidigm).

Results

Patients analysis

Twenty fours patients were included in the study, among these patients; only 21 validated the molecular criteria for transcriptomic analysis. Among these 21 patients, 6 patients did not develop a fever or SIRS. Nine patients developed SIRS, 5 a sepsis and 1 a severe sepsis. Patients’ clinical data are summarized in the table 1.

Pangenomic gene expression

The 21 samples were analyzed on Agilent microarrays "SurePrint G3 GE 8x60K human". After 17 hours of hybridization, the chips were washed and scanned. Results passed microarrays quality controls. Raw data were transformed into log2 and normalized with quantile method. 24 046 probes expressing a higher signal than background (in at least one group) have been selected. Two methods were used for statistical analysis: ANOVA analysis to measure the impact of each factor on gene expression variation (infection, gender, treatment) and Limma analysis on three groups (SIRS-, SIRS+, sepsis +) with the following co-factors: gender and treatment, to define differential gene expression (Figure 2). ANOVA analysis was performed on the 24 046 probes to estimate impact of each factor (infection, gender, treatment) in gene expression variation. P-value was calculated for each factor. For all factors (genes, infections, gender, and treatment) p-values were < 10-4 (Table 2). Infection F score was the highest, suggesting that "infection" had the greatest impact on gene expression. Genes F score was of 115.45 (p-value < 10-4) and explains genes fluctuation. Treatment and gender had an impact on the variation of gene expression with a F score of respectively 176.21 and 156.07 (p- value < 10-4). Unsupervised hierarchical clustering method was used to classify the differently expressed genes identified by Limma analysis. Expression similarity profiles of the genes were grouped on the horizontal axis and samples on the vertical axis. Gene expression profiles are shown in heat map (data not shown). With a FDR (False Discovery Rate) fixed to 5 %, 11 genes differentially expressed were identified between the patients who did not have temperature (controls) and the patients who developed SIRS and sepsis (taking account effect of gender and treatment). The eleven differentially expressed genes were: CHAT, CNN3, ANKRD42, LOC100505725, EDAR, GPAT2, ENST00000390425, MTRM8, LOC10289230 and XLOC-005643 (Table3). Gene expression profiling distinguished two groups: patients who did not develop temperature on one hand and patients with SIRS or sepsis on the other hand. CHAT, CNN3, C6orfl92, ANKRD42, LOC100505725, EDAR, GPATZ, ENST00000390425, MTRM8, LOC 10289230 were over expressed and XLOC-005643 was under-expressed in patient samples with SIRS or Sepsis in contrast to patients who did not develop fever and SIRS.

SVM

SVM and Leave-one-out cross-validation were used to classify patients according to their gene expression. 21 samples were separated into two groups, the first group was composed of 15 SIRS and septic patients and the second of the 6 patients who didn't have temperature. SIRS and sepsis patients were considered as positive experiments, and the other as negative. All 15 patients of the positive experiments were classified as positive, staying in positive class (True positives =15) and none was transferred from negative class to positive (False negatives = 0). On the 6 patients of the negative experiments, all were classified as negative, all retained in negative class (True negatives =15) and none was recruited into negative class from positives (False positives = 0). RT-qPCR

In order to confirm the microarray gene expression results, RT-qPCR was performed on 9 genes that composed the transcriptomic signature. The LOC 100289230 gene was differently expressed (p-value = 0.003) in patients who developed SIRS or sepsis compared with patients who did not. TRAV3, EDAR, PLCG1 -AS 1-001, GPAT2, MTRM8, CNN3 and SLC18B1 were also differentially expressed with p-values of 0.004, 0.01, 0.01, 0.005, 0.009, 0.09 and 0.01, respectively (Figure 3).

Discussion

This clinical study identified eleven genes significantly and differentially expressed in patients who developed SIRS or sepsis after the conditioning regimen for auto-HSCT. Ten of them were up-regulated (CHAT, CNN3, ANKRD42, LOC100505725, EDAR, GPATZ, ENST00000390425, MTRM8, C6orfl92 and LOC 10289230) while only one was down- regulated (XLOC-005643). All patients with this specific transcriptomic signature developed a SIRS or a sepsis within 48 hours (range 48 hours-7 days) following conditioning regimen. All patients were classified in the right group according to their gene expression and based on SVMs analysis. After a wide scientific literature review, genes composing our predictive signature are not directly involved in sepsis or infection pathways. In this cohort of 21 patients, 9 developed a SIRS and 5 a sepsis. Only one patient developed a severe sepsis, and no patient had septic shock, thus impeding the possibility to identify a specific transcriptomic signature predictive for these life-threatening conditions. In addition, since a SIRS/sepsis predictive signature before the conditioning regimen was not identified, the transcriptomic signature was not linked to the patient pre-auto HSCT status but depended on the conditioning regimen patient’s response.

Our transcriptomic signature predicts SIRS/sepsis profiles and is more robust than the main confounding factors, such as conditioning regimen, type or gender. BEAM conditioning regimen was more myelosuppressive than melphalan alone and men have had a more significant risk to develop infectious complications than women. Auto-HSCT patients affected by MM are conditioned by high dose of melphalan while patients with lymphoma by high dose BEAM chemotherapy, meaning that treatment and pathology are confounding factors. A SAM and an ANOVA analyzes were performed on the data of patient samples before chemotherapy, and no differentially-expressed genes were found between patients affected by lymphoma or MM.

This work proposes an original approach of the sepsis issue during auto-HSCT neutropenic phase with no similar analysis in the scientific literature. Vanska et al. have shown that high pentraxin 3 level predicted septic shock and bacteremia at the onset of febrile neutropenia after intensive chemotherapy of hematologic patients (Vanska M, et al. High pentraxin 3 level predicts septic shock and bacteremia at the onset of febrile neutropenia after intensive chemotherapy of hematologic patients. Haematologica. 2011 ;96(9): 1385-9.). Nonetheless, high pentraxin 3 level had only a predictive value for septic shock in patients who already had a febrile neutropenia. In contrast, our predictive transcriptomic signature identifies patients who have a major risk to develop SIRS and/or a sepsis at least 48 hours (range 48 hours - 7 days) before onset of fever. In order to confirm our results, our transcriptomic signature has been validated on a 10 patient’s prospective validation cohort (data not shown).

Early identification of patients who will develop SIRS and/or sepsis could lead to the development of antibiotic or antifungal prophylaxis adapted to the specific risk profile of each patient. RT-qPCR analysis on a blood sample of the eleven dysregulated genes identified in this study can be used in clinical routine. Indeed, RT-qPCR analysis is adapted to necessity for clinician to get a fast answer, essential to begin or not an adapted antibiotic and/or antifungal prophylaxis treatment.

Table 1: Patients, pathologies and auto-HSCT characteristics

Table 2: co-factors effect on gene expression.

ANOVA analysis was performed to test the co-factors impact (infection, treatment and gender) on genes expression for samples taken at T2 (after chemotherapy). With p-values < 0, 05 infection, treatment and gender have a significant effect on gene expression. DF: Degrees of Freedom.

Table 3: Differentially expressed gene characteristics

Ch : Chromosome, +1 : Forward strand, from 3’ to 5’, -1 : Reverse strand, from 5’ to 3’

REFERENCES Throughout this application, various references describe the state of the art to which this invention pertains. The disclosures of these references are hereby incorporated by reference into the present disclosure.

Claims

CLAIMS:

1. A method for predicting the risk of developing sepsis or/and systemic inflammatory response syndrome (SIRS) in a subject in need thereof, said method comprising the steps of i) determining the expression level of at least one gene among CHAT, CNN3, ANKRD42, LOC100505725, EDAR, GPAT2, ENST00000390425, MTRM8, C6orfl92, LOC10289230 and XLOC-005643 in a biological sample obtained from said subject, ii) comparing the expression level of each gene determined at step i) with its respective predetermined reference level and iii) concluding the subject has a high risk of developing sepsis or/and systemic inflammatory response syndrome (SIRS) when the level determined at step i) for at least one gene among CHAT, CNN3, ANKRD42, LOC100505725, EDAR, GPAT2,

ENST00000390425, MTRM8, C6orfl92 and LOC10289230 is higher than the respective predetermined reference level and the level determined at step i) for XLOC-005643 is lower than its predetermined reference level, or concluding the subject has a low risk of developing sepsis or/and systemic inflammatory response syndrome (SIRS) when the level determined at step i) for at least one gene among CHAT, CNN3, ANKRD42, LOC100505725, EDAR, GPAT2, ENST00000390425, MTRM8, C6orH92 and LOC10289230 is lower than the respective predetermined reference level and the level determined at step i) for XLOC-005643 is higher than its predetermined reference level.

2. The method according to claim 1 wherein the subject is a child, an adult, or an elderly.

3. The method according to claim 1 wherein the subject is a subject who has undergone a transplantation.

4. The method according to claim 1 wherein the subject is a subject who has undergone an autologous hematopoietic stem cell transplantation.

5. The method according to claim 1 wherein the subject is a subject having immune deficiency.

6. The method according to claim 1 wherein the subject is a subject staying at the hospital or any medical center for a long period.

7. The method according to claim 1 wherein the biological sample is peripheral blood mononuclear cells (PBMCs) sample.

8. A method of preventing sepsis or/and systemic inflammatory response syndrome in a subject comprising:

Predicting the risk of developing sepsis or/and systemic inflammatory response syndrome by using the method of claim 1 ; - Administering to the subject a therapeutically effective amount of pre-emptive therapy when it is concluded that the subject has a high risk of developing sepsis or/and systemic inflammatory response syndrome.

9. A kit suitable for predicting the risk of developing sepsis or/and systemic inflammatory response syndrome (SIRS) in a subject in need thereof comprising:

- At least a means for determining the expression level of at least one gene among CHAT, CNN3, ANKRD42, LOC100505725 , EDAR, GPAT2, ENST00000390425, MTRM8, C6orfl92, LOC 10289230 and XLOC-005643 in a biological sample obtained from said subject,

- Instructions for use.