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WO2011044927A1 - A method for the diagnosis or prognosis of an advanced heart failure - Google Patents

A method for the diagnosis or prognosis of an advanced heart failure Download PDF

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Publication number
WO2011044927A1
WO2011044927A1 PCT/EP2009/063297 EP2009063297W WO2011044927A1 WO 2011044927 A1 WO2011044927 A1 WO 2011044927A1 EP 2009063297 W EP2009063297 W EP 2009063297W WO 2011044927 A1 WO2011044927 A1 WO 2011044927A1
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heart failure
expression
gene
heart
samples
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Rémi HOULGATTE
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Institut National de la Sante et de la Recherche Medicale INSERM
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers

Definitions

  • a method for the diagnosis or prognosis of an advanced heart failure is provided.
  • the present invention relates to the field of diagnosis or prognosis of advanced heart failure by determining the expression profile of marker genes of interest.
  • Heart failure is not only a disease of the elderly or of persons who live unhealthy lifestyles. The highest incidence occurs between 25-45 years of age. Although more patients are surviving their first myocardial infarction, they often go on to develop progressive left ventricular dysfunction and end stage heart failure. As a result, the incidence of congestive heart failure is increasing.
  • Congestive heart failure is a common clinical syndrome, in particular in elderly individuals. It usually presents in the form of an insidious triggering of nonspecific symptoms such as coughing with exercise, fatigue, and the appearance of peripheral edemas. Diagnosis is conventionally based on the study of various parameters, such as clinical signs, (classified in four stages: stages I to IV of the NYHA (i.e. of the New York Heart Association), echocardiography, scintigraphy, exercise tests, etc.
  • Atrial natriuretic peptide a peptide of atrial origin
  • ANP atrial natriuretic peptide
  • BNP brain natriuretic peptide
  • the clinical syndrome of heart failure manifests when cellular respiration becomes Impaired because the heart cannot pump enough blood io support the metabolic demands of the body, or when normal cellular respiration can only be maintained with an elevated left ventricular filling pressure
  • the Frarningham, Duke and Boston criteria were established before noninvasive techniques for assessing systolic and diastolic dysfunction became widely available.
  • the three sets of criteria were designed to assist In the diagnosis of heart failure.
  • the Boston criteria have been shown to have the highest combined sensitivity (50 percent) and specificity (78 percent;. AH of these criteria are most heipfui in diagnosing advanced or severe heart failure, a condition that occurs in 20 to 40 percent of patients with a decreased ejection fraction. 9
  • Heart failure Early diagnosis of heart failure is essential for successfully addressing underlying diseases or causes and, in some patients, preventing further myocardial dysfunction and clinical deterioration. However, iniiial diagnosis may be difficult because the presentations of heart failure can change from no symptoms to pulmonary edema with cardiogenic shock. It is estimated that heart failure is correctly diagnosed initially In only 50 percent of affected patients.
  • LV filling pressure represents the diastolic pressure at which the left atrium (LA) and left ventricle (LV) equilibrate, at which time the LV fills with blood from the LA.
  • LA left atrium
  • LV left ventricle
  • LV filling pressure represents the diastolic pressure at which the left atrium (LA) and left ventricle (LV) equilibrate, at which time the LV fills with blood from the LA.
  • LA left atrium
  • LV left ventricle
  • LVEDP Left Ventricular End Diastolic Pressure
  • MLAP Mean Left Atrium Pressure
  • CO cardiac output
  • Patients diagnosed for a heart failure are subjected to various well known diet and lifestyle rules and are also subjected to pharmacological treatment with various classes of active ingredients including angiotensin-modulating agents, diuretics, beta blockers, positive inotropes, alternative vasodilatators, aldosterone receptor antagonists and vasopressin receptor antagonists.
  • active ingredients including angiotensin-modulating agents, diuretics, beta blockers, positive inotropes, alternative vasodilatators, aldosterone receptor antagonists and vasopressin receptor antagonists.
  • LVEF left ventricular ejection fraction
  • QRS interval 120 ms or more
  • LVEF left ventricular ejection fraction
  • Patients with NYHA class II, III or IV, and LVEF of 35% may also benefit from an implantable cardioverter-defibrillator.
  • Another current treatment involves the use of left ventricular assist devices (LVADs).
  • LVADs are battery-operated mechanical pump-type devices that are surgically implanted on the upper part of the abdomen. The final option, if other measures have failed, is heart transplantation or a temporary or prolonged implantation of an artificial heart.
  • Cardiac transplantation is an effective treatment option for patients with advanced heart failure (severe CHF).
  • advanced heart failure severe CHF
  • an increasing number of ambulatory patients with advanced heart failure are placed on transplant waiting lists while the supply of donor organs remains limited and fixed. Therefore, accurate identification of patients most likely to benefit from transplantation would be highly desirable, so as notably to limit as far as possible mortality in patients that would have required immediate transplantation. Indeed, knowledge of mortality predictors would be highly desirable to generate predictive models that can aid clinicians' decision making, in particular by identifying patients who are at high risk or low risk of death.
  • Liew et al. have performed a general overview of the molecular and genetic basis of heart failure and show how genomic tools may provide new perspectives relating to this complex disease (See Liew et al., 2004, Nat Rev Genet, Vol. (n °1 1 ) : 81 1 -825).
  • Steenman et al. have performed a study aimed at investigating whether a molecular profiling approach might be pertinent for the classification of heart failure patients. These authors have measured relative expression levels of selected genes in left ventricule tissue from 17 patients (15 failing hearts and 2 non-failing hearts) and have found that 159 genes distinguished between all patients. Further, these authors identified three major subgroups of patients, each with a specific molecular portrait. The results obtained by Steenman et al. were found to encourage further development of this approach in prospective studies on heart failure patients at earlier stages of the disease.
  • the present invention relates to a method for diagnosing an advanced heart failure, or for prognosis of the outcome of an advanced heart failure, in an individual, wherein the said method comprises the steps of :
  • This invention also relates to a kit for the in vitro diagnosis of an advanced heart dysfunction, or for prognosis of the outcome of an advanced heart dysfunction, in an individual, which kit comprises means for quantifying the expression level of one or more marker genes that are indicative of an advanced heart failure, which marker genes are selected from the group of marker genes that are cited above.
  • the instant invention also deals with a method for adapting a pharmaceutical treatment in a patient affected with an advanced heart failure comprising the steps of :
  • Figure 1 Prediction of HF severity based on gene expression profiles
  • LV ( Figure 1 A) and RV ( Figure 1 B) predictors of HF severity were identified after comparison of expression profiles of 'Deteriorating' (D) and 'Stable' (S) patients in left (LV) and right (RV) ventricles respectively.
  • the LV-RV predictor was determined as the combination of the LV severity and RV severity predictors.
  • Three patient classifications were constructed based on LV, RV, and LV-RV severity predictors.
  • Open and filled circles correspond to Stable and Deteriorating LV samples respectively.
  • Open and filled triangles correspond to Stable and Deteriorating RV samples respectively.
  • Dashed lines denote upper and lower limits of the unpredictable interval.
  • FIG. 1 Receiver Operating Characteristic (ROC) curves for the prediction of stable and deteriorating statuses in LV and RV samples.
  • ROC Receiver Operating Characteristic
  • Figure 3 Prediction of HF severity based on the Natriuretic Peptide Precursor B (NPPB) gene expression level.
  • Figure 5 Separate prediction of HF severity for coronary artery disease (Fig. 5A) and non-coronary arterydisease (Fig. 5B) related samples.
  • Open and black-filled circles correspond to Stable and Deteriorating samples respectively. samples respectively. Intermediate samples are shown in gray.
  • those relevant marker genes when included in expression profiling analysis techniques, can distinguish between heart failure patients with different risks of death.
  • the inventors have obtained gene expression profiles of 4217 distinct possibly cardiac-relevant genes from cardiac tissue samples originating from a cohort of patients having underwent a cardiac transplantation or the placement of a ventricular assist device.
  • the patients included in the studied cohort were classified into three heart failure-severity groups, respectively (i) Deteriorating, (ii) Intermediate and (iii) Stable.
  • the inventors By performing a two-class statistical analysis of gene expression profiles of Deteriorating and Stable patients, the inventors have identified a collection of marker genes that are highly significant for diagnosing heart failure, as well as, importantly, diagnosing the degree of advancement of heart failure.
  • the collection of marker genes that have been identified according to the invention reveals also a high significance for prognosis of the outcome of the tested patient.
  • the present invention provides for a method allowing (1 ) detecting in an individual a deregulation of the expression of one or more heart failure-specific marker genes described herein, and then (2) diagnosing the stage of progression of a heart failure in the said individual or performing a prognosis of the outcome of a heart failure in the said individual, wherein the said diagnosis or prognosis result is obtained by determining (i) the existence of a deregulated expression in one or more of the said marker genes and/or (ii) the level of the deregulated expression in one or more of the said marker genes.
  • the instant invention thus concerns a method for diagnosing an advanced heart failure in an individual, or for the prognosis of the outcome of an advanced heart failure in an individual, which comprises a step of determining, in a cardiac tissue sample originating from the said individual, the expression value of one or more heart failure-relevant marker genes, whereby, after comparison with a reference expression value for each of the said one or more heart failure-relevant marker genes, (i) an advanced heart failure is diagnosed, and/or (ii) the degree of advancement of the heart failure is diagnosed and/or (iii) a prognosis of the outcome of an advanced heart failure is determined.
  • An object of the present invention consists of an in vitro method for diagnosing an advanced heart failure, or for prognosis of the outcome of an advanced heart failure, in an individual, wherein the said method comprises the steps of :
  • a "heart failure” which encompasses a congestive heart failure (CHF) also termed congestive cardiac failure (CCF), consists of a condition that may result from any structural or functional cardiac disorder that impairs the ability of the heart to fill with blood or to pump a sufficient amount of blood through the body.
  • a heart failure encompasses left- sided heart failure and right-sided heart failure. Failure of the left ventricle causes congestion of the pulmonary capillaries. If left ventricular function is extremely compromised, symptoms of poor systemic circulation become manifest, leading to dizziness, confusion and diaphoresis and cool extremities at rest. Right ventricular failure leads to congestion of systemic capillaries which causes peripheral edema or anasacra and nocturia.
  • a heart failure consists of a disorder that is clinically identified as such using one of the known diagnosis systems including the "Framingham criteria” (McKee et al., N. Engl. J. Med., Vol. 285(26) : 1441 -1446), the “Boston criteria” (Carlson et al., 1995, Journal of chronic diseases, Vol. 38(9) : 73-739), the “Duke criteria” (Harlan et al., 1977, Ann. Intern. Med., Vol. 86(2) : 133-138) and the "Killip class” (Killip et al., 1967, Am. J.
  • Functional classification may be performed using the New York Heart Association Functional Classification (Criteria Committee, New York Heart Association. Diseases of the heart and blood vessels. Nomenclature and criteria for diagnosis, 6 th ed., Boston : Little, Brown and Co, 1964 : 1 14).
  • an individual undergoes an "advanced heart failure" when the heart clinical status of the said individual meets the criteria defined by the United Network for Organ Sharing (UNOS) medical urgency status relating to the allocation of thoracic organs, and specifically heart.
  • UNOS United Network for Organ Sharing
  • an individual having an advanced heart failure consists of an individual who is classified as belonging to status 1 A, 1 B, 2 or 7 according to the above cited UNOS classification, e.g. the UNOS classification dated of July 1 1 , 2007.
  • a cardiac tissue sample refers to a sample of the heart tissue from the patient to be tested, which heart tissue sample comprises at least the minimum number of cells allowing the production of an amount of nucleic acid expression products (e.g. mRNA or cDNA) for performing step b) of the diagnosis or prognosis method described above.
  • the heart tissue sample comprises at least 10 3 cells, and preferably at least 10 6 cells from the heart organ.
  • the heart tissue sample that is provided at step a) of the method may be any of the specimens such as those prepared by excision and extirpation of a small piece of heart tissue.
  • markers(s) markers
  • “heart marker(s)” markers gene(s)
  • “heart marker gene(s)” markers
  • “heart-relevant marker(s)” heart-relevant marker gene(s)
  • “heart-specific marker gene(s)” or “heart-specific marker gene(s)” may be interchangeably used herein.
  • a marker gene denotes a gene whose expression is deregulated only in patients undergoing an advanced heart failure as defined herein.
  • LV group ADAMTS5, ANXA10, BZW2, CKM, CMYA3, CTAGE1 , Gcoml , KIAA0859, LMOD3, MAPKAPK3, MRCL3, MRCL3, MYL5, MYL7, MYOM1 , NRAP, OR1 D5, PKP2, PLN, PLN, RRAS2, RYR2, SLMAP, TPM2, TXNIP, ZBTB16 and ZNF9, and
  • RV Group ACADM, ACTC, ADAMTS5, BZW2, C21 orf33, CAV1 , CCR2, CD36, CDH13, CFL1 , CFL2, CLIC5, CMYA3, CSDE1 , CTAGE1 , DBI, DSTN, EIF4A2, EPAS1 , FGF12, FHL1 , FHL1 , FKBP5, FLNC, GBAS, Gcoml , GLUL, GPNMB, HADHB, HADHSC, IGFBP5, JAK2, LIN10, LM07, LOC220729, MAPKAPK3, ME2, MGST3, MRCL3, MYL7, MYOM1 , NDUFB4, NEXN, NEXN, NRAP, OR1 D5, PALLD, PKIA, PKP2, PLN, PLN, POLR2L, PPP2CB, PRKAA2, PRKAG2, RGS5, RRAS2, RYR
  • the expression level of the marker genes listed in (i) and (ii) above are lower than the expression level of the same genes that is determined in patients which are not affected with an advanced heart failure, including patients which are not affected with any heart dysfunction.
  • LV group A2M, ADSL, AEBP1 , ANXA1 , ANXA1 1 , ANXA2, ARPC2, ATP1 B3, AZGP1 , BSG, BXDC2, C15orf41 , C1 GALT1 C1 , C1 orf63, C1 R, C4A, C6orf203, CAB39, CABYR, CBX5,
  • CCNG1 CD63, CHPF, COL1 A1 , COL1 A1 , COL1 A1 , COL1 A2, COL2A1 , COL3A1 , COL6A3, CTSB, CTSD, CXX1 , DMPK, DNAJB1 1 , DXS9879E, DYNLL1 , EDG1 , EEF1 A1 , EEF1 B2, EEF2, EFEMP1 , EN03, ERP29, FAM13A1 , FHL2, FLJ20152, FN1 , FXYD1 , G6PD, GAPDH, GOT1 , GPR83, GPX3, GUK1 , HCA1 12, HLA-A, HLA-DRB4, HRC, HSPA4L, IFITM1 , IFITM2, KCNA10, KCNJ8, KCNK10, KCNQ1 , KLF13, LAMA4, LDHA, LENG8, LGMN, LMNA, L
  • RV group ACAA1 , ARPC2, BSG, C15orf41 , C4A, CD63, CDH9, CHPF, COL1 A1 , COL1 A1 , COL1 A1 , COL1 A2, COL2A1 , COL3A1 , COL4A5, CTSB, CTSD, EN03, FABP4, FAM13A1 , FLJ22655, FN1 , GAPDH, GPR133, GPR83, GPX3, HRC, HTRA1 , IFI16, KCTD15, LAMA4, LDHA, MFSD5, MIF, MMACHC, MTHFD2, NPC2, NPPA, NPPB, NRG3, P4HB, PAM, PCOLCE2, PDIA3, PDLIM1 , PDLIM3, PLA2G2A, PPGB, PRKAG3, S100A10, S100A1 1 , SAT, SCD, SERPINB2, SLC6A6, SNX26,
  • the expression level of the marker genes listed in (iii) and (iv) above are higher than the expression level of the same genes that is determined in patients which are not affected with an advanced heart failure, including patients which are not affected with any heart dysfunction.
  • the method according to the invention that is described above consists of a "diagnosis” method since it is shown herein a specific relationship between (i) a deregulation of the expression of each marker gene disclosed in the present specification and (ii) the occurrence of an advanced heart failure for the patient tested.
  • the method according to the invention that is described above consists of a "prognosis” or a “prediction” method, since the marker gene expression deregulation, that is determined after having performed the comparison step c), is indicative of the outcome of the advanced heart failure, i.e. allows to predict if the patient tested ay be classified as (i) a patient having a stable advanced heart failure, (ii) a patient having an intermediate (slow progression ) advanced heart failure or (ii) a patient having a deteriorating advanced heart failure.
  • quantifying the expression level of marker genes encompasses determining an absolute or relative quantification value that illustrates the said marker genes expression activity.
  • Quantification encompasses determining a quantification value for the mRNA synthesized by each of the marker genes tested, or of the cDNA that may be obtained from the corresponding mRNA.
  • the quantification value that is determined at step b) of the method may consist of an absolute quantification value that reflects the amount of mRNA produced from each marker gene tested that is present in the patient's cardiac tissue sample.
  • the said quantification value may be expressed as a relative value, e.g.. the ratio between (i) the amount of mRNA produced by the marker gene tested and (ii) the amount of mRNA produced by a gene that is constitutively expressed, e.g. a house-keeping gene like actin.
  • a “gene” encompasses, or alternatively consists of, a nucleic acid that is contained in the human genome and which is expressible, i.e. which is able to give rise to a corresponding mRNA.
  • a “gene”, as used in the present specification consists of a human genomic nucleic acid encoding a mRNA, whether or not the encoded mRNA codes for a polypeptide.
  • nucleic acid sequence, especially the cDNA sequence, of each of the heart-specific marker genes that are described herein is easily available to the one skilled in the art.
  • the one skilled in the art may refer to the gene names listed in Tables 1 to 4, which gene names consist of the unequivocal name of each human gene that is attributed by the HUGO Gene Nomenclature Committee (HGCN).
  • Nucleic acid sequences of the heart-specific marker genes of the invention are thus available upon query at the HGCN database on the basis of the internationally recognized gene name, e.g. at the following Web address : http//www.gene.ucl.ac.uk/cgi-bin/nomenclature/searchgenes.pl.
  • the one skilled in the art may design detection and/or quantification means, including nucleic acid primers or probes, specific for every one of the marker genes of interest described herein, on the basis of the nucleic acid sequences of these marker genes which are available in various sequence databases, including the HGCN database cited above.
  • Illustratively pair of primers that specifically hybridise with the target nucleic acid gene marker of interest may be designed by any one of the numerous methods known in the art, based on the known partial or complete sequence of the said marker gene.
  • At least one pair of specific primers, as well as the corresponding detection nucleic acid probe is already referenced and entirely described in the public "Quantitative PCR primer database", notably at the following Internet address : "http://lpgws.nci.nih.gov/cgi-bin/PrimerViewer”.
  • nucleic acid primers or probes that specifically hybridise with each of the marker genes described herein, starting from their known 3'-end and/or 5'-end nucleic acid sequences.
  • a specific pair of primers may be designed using the method disclosed in the US Patent n ° US 6,892,141 to Nakae et al., the entire disclosure of which is herein incorporated by reference.
  • polynucleotides that are directly usable as primers or probes, or alternatively are usable for designing primers or probes, for the purpose of the invention consist of the polynucleotides having the nucleic acid sequences SEQ ID N ° 1 to 225, each polynucleotide being specific for a given heart-specific marker gene described herein, as it will be detailed elsewhere in the present specification.
  • Table 1 herein discloses references to nucleic acids that may be used as primers or probes that specifically hybridize with each of the heart-specific marker genes according to the invention.
  • left column contains the abbreviated designation (Internationally recognized gene symbol) of each of the heart-specific marker gene of the invention.
  • Right column contains the reference number (SEQ ID NO) of a nucleic acid that hybridises specifically with the corresponding marker gene expression product (e.g. mRNA or cDNA), as described in the Sequence Listing comprised in the present specification.
  • Table 2 contains data additional to those of Table 1 relating to the unequivocal identity of each of the heart-specific marker gene according to the invention.
  • the left column named "Symbol” indicates the abbreviated designation (Internationally recognized gene symbol) of each of the marker genes.
  • the column named "Accession Nb” indicates the accession number of the corresponding nucleic acid sequence in various sequence databases, including the EMBL and the GenBank databases.
  • the column named "EntrezGenelD” indicates the accession number of the corresponding nucleic acid sequence in the sequence database from the National Center for Biotechnology Information (NCBI).
  • NCBI National Center for Biotechnology Information
  • the column named "Gene Name” indicates the complete internationally recognized name of the corresponding heart- specific marker gene.
  • the column named "RefSeq” indicates the accession number of the corresponding nucleic acid sequence in various sequence databases, including the EMBL and the GenBank databases.
  • the column named "UniGenelD” indicates the accession number of the corresponding heart-specific marker gene in the NCBI Unigene database of transcribed sequences.
  • the inventors have performed a wide range differential expression analysis of more than 4000 candidate potentially heart-relevant genes on cardiac tissue samples previously collected from patients with advanced heart failure who underwent a cardiac transplantation or a total artificial heart placement.
  • the determination of a relevant relationship between the expression level value of the said candidate gene, at step (v-2) above, and the occurrence of an advanced heart failure may be performed by any one of the methods of the suitable statistical analysis that are well known from the one skilled in the art.
  • the patients undergoing an advanced heart failure from which originated the cardiac tissue samples were classified into three severity groups, respectively "Stable” (UNOS-2 status with no recent ADHF), "Intermediate” (UNOS-1 B status or UNOS-2 status with recent ADHF) and "Deteriorating” (UNOS-1 A status), thus according to the clinical status grades defined by UNOS, and further according to additional clinical data including the occurrence of hospitalizations for Acute Decompensated Heart Failure during the three months prior to the surgical procedure ("recent ADHF").
  • two spatially distinct transmural samples were obtained from both left ventricle (LV) and right ventricle (RV) immediately after cardiac transplantation, so as to evaluate the statistical relevance of a deregulation of each of the candidate genes for an advanced heart failure caused by a dysfunction of the left ventricle, the right ventricle, or both ventricles, respectively.
  • This step may be performed using the Significance Analysis of Microarrays method (SAM) that is described by Tusher et al. (2001 , Proc Natl Acad Sci USA, Vol. 98 : 51 16-5121 );
  • MSS Molecular Severity Score value
  • a candidate gene consists of a heart-relevant marker gene if its predictive score value, calculates as a p value, is lower than 0.01 , when using the method generally disclosed above and detailed in the examples herein.
  • Table 3 lists the heart-relevant marker genes according to the invention that are predictive for an advanced heart failure in general, as well as for an advanced heart failure due to a dysfunction of the left ventricle.
  • the left column indicates the identity of each of the marker genes by its internationally recognized HGCN Gene symbol.
  • the second column named "Up/Downregulated” indicates if the corresponding marker gene is over-expressed
  • P value indicates the statistical relevance of the said marker gene, expressed as a P value.
  • Table 4 lists the heart-relevant marker genes according to the invention that are predictive for an advanced heart failure in general, as well as for an advanced heart failure due to a dysfunction of the right ventricle.
  • the left column indicates the identity of each of the marker genes by its internationally recognized HGCN Gene symbol.
  • the second column named "Up/Downregulated” indicates if the corresponding marker gene is over-expressed ("UP") or under-expressed ("DOWN”) in the course of occurrence of an advanced heart failure.
  • the third column named "P value” indicates the statistical relevance of the said marker gene, expressed as a P value.
  • the results obtained by the inventors have shown that, starting from the marker genes, statistical relevance data determined from left ventricle (LV) and right ventricle (RV), respectively, the whole marker genes separately selected for LV and RV form a collection of marker genes wherein each of the marker genes comprised therein are useful as a marker gene of an advanced heart failure in general.
  • LV left ventricle
  • RV right ventricle
  • the general collection of heart-relevant marker genes according to the invention consists of the collection of marker genes that is initially specified above in the present specification, where the general features of the diagnosis or the prognosis method according to the invention are defined.
  • step b) consists of quantifying the expression level of one or more marker genes indicative of an advanced heart failure involving a left ventricle dysfunction that are selected from the group consisting of : A2M, ADAMTS5, ADSL, AEBP1, ANXA1, ANXA10, ANXA11, ANXA2, ARPC2, ATP1B3, AZGP1, BSG, BXDC2, BZW2, C15orf41, C1GALT1C1, C1orf63, C1R, C4A, C6orf203, CAB39, CABYR, CBX5, CCNG1, CD63, CHPF, CKM, CMYA3, COL1A1, COL1A2, COL2A1, COL3A1, COL6A3, CTAGE1, CTSB, CTSD, CXX1, DMPK, DNAJB11, DXS9879E, DYNLL1, EDG1, EEF1
  • step b) consists of quantifying the expression level of one or more marker genes indicative of an advanced heart failure involving a right ventricle dysfunction that are selected from the group consisting of : ACAA1, ACADM, ACTC, ADAMTS5, ARPC2, BSG, BZW2, C15orf41, C21orf33, C4A, CAV1, CCR2, CD36, CD63, CDH13, CDH9, CFL1, CFL2, CHPF, CLIC5, CMYA3, COL1A1, COL1A2, COL2A1 overwhelmCOL3A1 , COL4A5, CSDE1, CTAGE1, CTSB, CTSD, DBI, DSTN, EIF4A2, EN03, EPAS1, FABP4, FAM13A1, FGF12, FHL1, FKBP5, FLJ22655, FLNC, FN1, GAPDH, GBAS, Gcoml, GLUL, GPNMB, GPR133,
  • step b) consists of quantifying the expression level of one or more marker genes indicative of an advanced heart failure involving specifically the left ventricle dysfunction that are selected from the group consisting of : A2M, , ADSL, AEBP1 , ANXA1 , ANXA10, ANXA1 1 , ANXA2, , ATP1 B3, AZGP1 , , BXDC2, C1 GALT1 C1 , C1 orf63, C1 R, C6orf203, CAB39, CABYR, CBX5, CCNG1 , CKM, COL6A3, CXX1 , DMPK, DNAJB1 1 , DXS9879E, DYNLL1 , EDG1 , EEF1 A1 , EEF1 B2, EEF2, EFEMP1 , ERP29, FHL2, FLJ20152, FXYD1 , G6PD
  • step b) consists of quantifying the expression level of one or more marker genes indicative of an advanced heart failure involving specifically the right ventricle dysfunction that are selected from the group consisting of : ACAA1 , ACADM, ACTC, C21 orf33, CAV1 , CCR2, CD36, CDH13, CDH9, CFL1 , CFL2, , CLIC5, COL4A5, CSDE1 , DBI, DSTN, EIF4A2, EPAS1 , FABP4, FGF12, FHL1 , FKBP5, FLJ22655, FLNC, GBAS, GLUL, GPNMB, GPR133, HADHB, HADHSC, HTRA1 , IFI16, IGFBP5, JAK2, KCTD15, LIN10, LM07, LOC220729, ME2, MGST3, NDUFB4, NEXN, NEXN, PALLD, , P
  • the diagnosis or prognosis method according to the invention is preferably performed by quantifying, at step b) of the method, at least one left ventricle-specific marker gene and at least one right ventricle-specific marker gene.
  • step b) consists of quantifying the expression level of two or more marker genes, respectively : ( ⁇ ) one or more markers predictive of a left ventricle dysfunction selected from the group consisting of : A2M, ADSL, AEBP1, ANXA1 , ANXA10, ANXA11 , ANXA2, , ATP1B3, AZGP1, , BXDC2, C1GALT1C1, C1orf63, C1R, C6orf203, CAB39, CABYR, CBX5, CCNG1, CKM, COL6A3, CXX1, DMPK, DNAJB11, DXS9879E, DYNLL1, EDG1, EEF1A1, EEF1B2, EEF2, EFEMP1, ERP29, FHL2, FLJ20152, FXYD1, G6PD, GOT1, GUK1 , HCA112, HLA-A, HLA- D
  • one or more markers predictive of a right ventricle dysfunction selected from the group consisting of : ACAA1, ACADM, ACTC, C21orf33, CAV1 , CCR2, CD36, CDH13, CDH9, CFL1, CFL2, , CLIC5, COL4A5, CSDE1, DBI, DSTN, EIF4A2, EPAS1 , FABP4, FGF12, FHL1, FKBP5, FLJ22655, FLNC, GBAS, GLUL, GPNMB, GPR133, HADHB, HADHSC, HTRA1, IFI16, IGFBP5, JAK2, KCTD15, LINK), LM07, LOC220729, ME2, MGST3, NDUFB4, NEXN, NEXN, PALLD, , PCOLCE2, PDIA3, PKIA, PLN, POLR2L, PPGB, PPP2CB, PRKAA2, PRKAG2, RGS5, SCD, SCNN1D, SD
  • every one of the heart-relevant marker genes has a high statistical relevance for diagnosing or predicting an advanced heart failure, with a P value which always lower than 0.01 (1x10 ⁇ 2 ) and up to less than 1x10 "11 .
  • the diagnosis or prognosis method according to the invention by quantifying, at step b) of the method, the expression level of only one marker gene or of only a low number of marker genes, e.g. less than 10 marker genes, then a good accuracy of the diagnosis or prognosis of an advanced heart failure may nevertheless be obtained if highly relevant marker(s) are quantified, e.g. marker(s) having a P value lower than 0.0001 (1x10 ⁇ 4 ) or even lower than 1x10 ⁇ 6 .
  • the one or more left ventricle-specific markers that are quantified at step b) of the method are selected from the group consisting of : MYL7, VTN, LDHA, CD63, A2M, CTSD, BSG, FXYD1, VWF, PDLIM3, C4A, S100A11, C15orf41, PLN, EDG1, RYR2, MYOM1, ANXA11, PAM, ADAMTS5, MIF, HCA112, LAMA4, PLN, AZGP1, BXDC2, DYNLL1, KCNJ8, NPPA, NPPB, EN03, NRG3, LENG8, CAB39, CTSB, LMOD3, COL1A1, CMYA3, BZW2, MMACHC, Gcoml, GUK1, CHPF, COL1A1, PKM2, P4HB, HRC, MYL5, SSR4, MTHFD2, NKX2-5, COL
  • the one or more left ventricle-specific marker genes that are quantified at step b) of the method are selected from the group consisting of : PLN, LDHA, ZNF9, MYOM1 , MYL7, PKP2, SLMAP, NRAP, BZW2, VTN, MMACHC, NDUFB4, NPPA, PDLIM3, BSG, ACTC, EPAS1 , MAPKAPK3, NPPB, MFSD5, COL4A5, PLA2G2A, CHPF, MIF, ARPC2, PKIA, CD63, PRKAA2, ADAMTS5, SERPINB2, HADHSC, HRC, PDLIM1 , NEXN, C4A, CLIC5, CFL2, PPGB, DBI, SSR4, TXNIP, SLC6A6, CSDE1 , GPR83, Gcoml , MTHFD2 and CMYA3.
  • the one or more marker genes that are quantified at step b) of the method may be selected from :
  • one or more marker genes predictive of an advanced heart failure involving a left ventricle dysfunction that are selected from the group consisting of : MYL7, VTN, LDHA, CD63, A2M, CTSD, BSG, FXYD1 , VWF, PDLIM3, C4A, S100A1 1 , C15orf41 , PLN, EDG1 , RYR2, MYOM1 , ANXA1 1 and PAM, and
  • one or more marker genes predictive of an advanced heart failure involving a right ventricle dysfunction that are selected from the group consisting of : PLN, LDHA, ZNF9, MYOM1 , MYL7, PKP2, SLMAP, NRAP, BZW2, VTN, MMACHC and NDUFB4.
  • the accuracy of the prediction or the diagnosis results obtained at step d) of the in vitro method may increase with an increasing number of heart-relevant marker genes that are quentified at step b).
  • step b) the one skilled in the art will adapt step b), so as to use a suitable combination of (1 ) the number of heart-specific marker genes to be quantified and (2) the predictive score (e.g. the P value) of the heart-specific marker genes to be quantified.
  • the number of heart-specific marker genes that are quantified may be of 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40,41 , 42, 43, 44, 45, 46, 47, 48, 49, 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, 100, 101 , 102
  • the comparison step c) is performed using a "control" expression value for each heart-specific marker gene that is tested.
  • the said control expression value consists of the mean expression value for the said heart-specific marker gene that is found in individuals that are not affected with an advanced heart failure to which a deregulation of the said marker gene is associated.
  • the comparison step c) consists of comparing the expression value of a plurality of marker genes that are known to be deregulated in patients affected with an advanced heart failure, with the reference expression values for each of the said marker genes that are previously determined in patients that are not affected with an advanced heart failure, including healthy individuals.
  • the collection of expression values for the tested marker genes may also be termed an "expression profile" of these genes.
  • step c) encompasses comparing (i) the expression profile of the gene markers of interest that has been determined from the biological sample of the patient under testing with (ii) at least one reference expression profile of the same marker genes that has been previously determined from patients that are not affected with an advanced heart failure.
  • step c) consists of comparing (i) the expression profile of the gene markers of interest that has been determined from the biological sample of the patient under testing with (ii) every reference expression profile of the same marker genes that has been previously determined from patients that are not affected with an advanced heart failure, which may also include healthy individuals.
  • the expression level value of a heart-specific marker gene can be provided as a relative expression level value.
  • the level of expression of the said marker gene is previously determined for 10 or more samples of cardiac tissue originating from patients who are not affected with an advanced heart failure, prior to the determination of the expression level for the sample in question.
  • the median expression level of the said heart-specific marker gene which has been determined in the larger number of samples, is determined and this median expression value is used as a baseline expression level, that may also be termed "control" value for the said heart-specific marker gene.
  • the expression level of the said heart-specific marker gene determined for the test sample absolute level of expression
  • a deregulated expression level is determined at step c) if there exists a relevant difference between (i) the expression value obtained at step b) and (ii) the reference control value.
  • the said relevant difference may be any expression level difference that is found statistically significant, irrespective of the statistical method that is used. Generally, a relevant difference is found if, either :
  • the deregulated expression of the heart-specific marker gene consists of an under- expression, as compared to the control expression value, the deregulated expression value being 0.98 fold or less the control expression value;
  • the deregulated expression of the heart-specific marker gene consists of an over- expression, as compared to the control expression value, the deregulated expression value being 1 .02 fold or more the control expression value.
  • the one skilled in the art may select any combination of more than one heart-specific marker genes for performing the in vitro diagnosis or prognosis method according to the invention.
  • - Cb consists of a specific combination of heart-specific marker genes according to the invention
  • M 2 denotes a given heart-specific marker gene described in the instant specification and "z" denotes the maximum number of marker genes described herein, with z being presently equal to 225,
  • - xi , x 2, x 3 Xz consists of an integer equal to 0 or 1 , which denotes the status of the corresponding heart-specific marker gene in the combination, wherein (i) the said integer being equal to 0 if the corresponding marker gene is absent in the said combination or (ii) the said integer being equal to 1 if the corresponding marker gene is present in the said combination.
  • the in vitro diagnosis or prognosis method according to the invention is both highly sensitive and highly reproducible.
  • a 95% correct prediction is reached, based on a comparison with the actual clinical classification of the said patients.
  • patients finally classified in-between the Stable status and the Deteriorating status when performing the in vitro diagnosis or prognosis method according to the invention are those who were actually clinically classified in the Intermediate group.
  • MSS Molecular Severity Score
  • any one of the methods known by the one skilled in the art for quantifying a nucleic acid biological marker encompassed herein may be used for performing the in vitro prediction or diagnosis method of the invention.
  • any one of the standard and non-standard (emerging) techniques well known in the art for detecting and quantifying a protein or a nucleic acid in a sample can readily be applied.
  • Such techniques include detection and quantification of nucleic acid biological markers with nucleic probes or primers.
  • the expression level of a heart-specific marker gene described herein may be quantified by any one of a wide variety of well known methods for detecting expression of a transcribed nucleic acid.
  • Non-limiting examples of such methods include nucleic acid hybridisation methods, nucleic acid reverse transcription methods, and nucleic acid amplification methods.
  • the expression level of a heart-specific marker gene is assessed by preparing mRNA cDNA (i.e. a transcribed polynucleotide) from cells originating from a cardiac tissue sample of a patient to be tested, and by hybridising the mRNA cDNA with a reference polynucleotide which is a complement of a marker nucleic acid, or a fragment thereof, the said marker nucleic acid being comprised in the expression product of a heart- specific marker gene described herein.
  • cDNA can, optionally, be amplified using any of a variety of polymerase chain reaction methods prior to hybridisation with the reference polynucleotide.
  • step b) of expression level quantification of two or more heart-specific marker genes is performed using DNA microarrays.
  • a mixture of transcribed polynucleotides obtained from the cardiac tissue sample, or alternatively a mixture of the corresponding cDNAs is contacted with a substrate having fixed thereto a plurality of polynucleotides, each of these polynucleotides consisting of a polynucleotide complementary to, or homologous with, at least a portion (e.g.
  • a disease-specific marker gene at least 7, 10, 15, 20, 25, 30, 40, 50, 100, 500, or more consecutive nucleotide residues of a disease-specific marker gene. If polynucleotides complementary to or homologous with are differentially detectable on the substrate (e.g. detectable using different chromophores or fluorophores, or fixed to different selected positions), then the levels of expression of a plurality of heart-specific marker genes can be quantified simultaneously using a single substrate (e.g. a "gene chip" microarray of polynucleotides fixed at selected positions). When a method of assessing marker expression is used which involves hybridisation of one nucleic acid with another, it is preferred that the hybridisation be performed under stringent hybridisation conditions.
  • step b) comprises the steps of :
  • nucleic acid b1 providing two or more sets of nucleic acids, each nucleic acid contained in a set hybridizing specifically with a nucleic acid expression product of a heart-specific marker gene described in the present specification;
  • b2) reacting the sets of nucleic acids provided at step b1 ) with nucleic acid expression products that are previously extracted from the cardiac tissue sample provided at step a); b3) detecting and quantifying the nucleic acid complexes formed between (i) the sets of nucleic acids provided at step b1 ) and (ii) the nucleic acid expression products that are extracted from the cardiac tissue sample provided at step a);
  • nucleic acids that are provided at step b1 may also be conventionally termed nucleic acid probes, each nucleic acid probe having the ability to specifically hybridize with an expression product (mRNA or cDNA) from a heart-specific marker gene selected from the group consisting of the heart-specific marker genes described in the present specification.
  • mRNA or cDNA an expression product from a heart-specific marker gene selected from the group consisting of the heart-specific marker genes described in the present specification.
  • a "set" of nucleic acids that is provided at step b1 ) consists of one or more nucleic acids
  • nucleic acid probes that all hybridize with an expression product from the same heart- specific marker gene.
  • a set of nucleic acids comprises two or more nucleic acids
  • the said nucleic acids may be identical or distinct.
  • the said distinct nucleic acids preferably hybridize with distinct nucleic acid portions, most preferably non-overlapping portions, of the expression product of the same heart-specific marker gene.
  • steps b1 ) to b3) are performed with DNA microarrays.
  • the expression level quantification of the heart-specific marker genes is performed by using suitable DNA microarrays.
  • the mRNA is immobilised on a solid surface and contacted with a probe, for example by running the isolated mRNA on an agarose gel and transferring the mRNA from the gel to a membrane, such as nitrocellulose.
  • the probe(s) are immobilized on a solid surface and the mRNA is contacted with the probe(s), for example, in an Affymetrix gene chip array.
  • an array of "probe" nucleic acids that includes a probe for each of the phenotype determinative genes whose expression is being assayed is contacted with target nucleic acids as described above.
  • Contact is carried out under hybridization conditions, e.g., stringent hybridization conditions as described above, and unbound nucleic acid is then removed.
  • the resultant pattern of hybridized nucleic acid provides information regarding expression for each of the genes that have been probed, where the expression information is in terms of whether or not the gene is expressed and, typically, at what level, where the expression data, i.e., expression profile, is both qualitative and quantitative.
  • Suitable carriers or solid phase supports for such assays include any material capable of binding the class of molecule to which the marker or probe belongs.
  • Well-known supports or carriers include, but are not limited to, glass, polystyrene, nylon, polypropylene, nylon, polyethylene, dextran, amylases, natural and modified celluloses, polyacrylamides, gabbros, and magnetite.
  • the non-immobilised component is added to the solid phase upon which the second component is anchored.
  • uncomplexed components may be removed (e.g., by washing) under conditions such that any complexes formed will remain immobilised upon the solid phase.
  • the detection of marker/probe complexes anchored to the solid phase can be accomplished in a number of methods outlined herein.
  • the probe when it is the unanchored assay component, can be labelled for the purpose of detection and readout of the assay, either directly or indirectly, with detectable labels discussed herein and which are well-known to one skilled in the art.
  • determination of the ability of a probe to recognise a marker can be accomplished without labelling either assay component (probe or marker) by utilising a technology such as real-time Biomolecular Interaction Analysis (BIA) (see, e.g., Sjolander, S. and Urbaniczky, C, 1991 , Anal. Chem. 63:2338-2345 and Szabo et al., 1995, Curr. Opin. Struct. Biol. 5:699-705).
  • BIOA Biomolecular Interaction Analysis
  • surface plasmon resonance is a technology for studying biospecific interactions in real time, without labelling any of the interactants (e.g., BIAcore).
  • marker/probe complexes may be separated from uncomplexed assay components through a series of centrifugal steps, due to the different sedimentation equilibria of complexes based on their different sizes and densities (see, for example, Rivas, G., and Minton, A. P., 1993, Trends Biochem Sci. 18(8):284-7).
  • Standard chromatographic techniques may also be utilized to separate complexed molecules from uncomplexed ones. For example, gel filtration chromatography separates molecules based on size, and through the utilization of an appropriate gel filtration resin in a column format, for example, the relatively larger complex may be separated from the relatively smaller uncomplexed components.
  • the relatively different charge properties of the marker/probe complex as compared to the uncomplexed components may be exploited to differentiate the complex from uncomplexed components, for example through the utilization of ion-exchange chromatography resins.
  • ion-exchange chromatography resins Such resins and chromatographic techniques are well known to one skilled in the art (see, e.g., Heegaard, N. H., 1998, J. Mol. Recognit. Winter 1 1 (1 -6):141 -8; Hage, D. S., and Tweed, S. A. J Chromatogr B Biomed Sci Appl 1997 Oct. 10;699(1 -2):499-525).
  • Gel electrophoresis may also be employed to separate complexed assay components from unbound components (see, e.g., Ausubel et al., ed., Current Protocols in Molecular Biology, John Wiley & Sons, New York, 1987-1999).
  • protein or nucleic acid complexes are separated based on size or charge, for example.
  • non- denaturing gel matrix materials and conditions in the absence of reducing agent are typically preferred.
  • SELDI-TOF technique may also be employed on matrix or beads coupled with active surface, or not, or antibody coated surface, or beads.
  • RNA isolation technique that does not select against the isolation of mRNA can be utilised for the purification of RNA from cardiac tissue sample (see, e.g., Ausubel et al., ed., Current Protocols in Molecular Biology, John Wiley & Sons, New York 1987-1999).
  • large numbers of tissue samples can readily be processed using techniques well known to those of skill in the art, such as, for example, the single-step RNA isolation process of Chomczynski (1989, U.S. Pat. No. 4,843,155).
  • An alternative method for determining the level of mRNA marker in a sample involves the process of nucleic acid amplification, e.g., by rtPCR (the experimental embodiment set forth in Mullis, 1987, U.S. Pat. No. 4,683,202), ligase chain reaction (Barany, 1991 , Proc. Natl. Acad. Sci. USA, 88:189-193), self sustained sequence replication (Guatelli et al., 1990, Proc. Natl. Acad. Sci. USA 87:1874-1878), transcriptional amplification system (Kwoh et al., 1989, Proc. Natl. Acad. Sci.
  • rtPCR the experimental embodiment set forth in Mullis, 1987, U.S. Pat. No. 4,683,202
  • ligase chain reaction Barany, 1991 , Proc. Natl. Acad. Sci. USA, 88:189-193
  • self sustained sequence replication (Guatelli et al., 1990, Proc. Natl.
  • amplification primers are defined as being a pair of nucleic acid molecules that can anneal to 5' or 3' regions of a gene (plus and minus strands, respectively, or vice-versa) and contain a short region in between.
  • amplification primers are from about 10 to 30 nucleotides in length and flank a region from about 50 to 200 nucleotides in length. Under appropriate conditions and with appropriate reagents, such primers permit the amplification of a nucleic acid molecule comprising the nucleotide sequence flanked by the primers.
  • determinations may be based on the normalised expression level of the marker.
  • Expression levels are normalised by correcting the absolute expression level of a marker by comparing its expression to the expression of a gene that is not a marker, e.g., a housekeeping gene that is constitutively expressed. Suitable genes for normalisation include housekeeping genes such as the actin gene and the ribosomal 18S gene. This normalisation allows the comparison of the expression level of one or more tissue-specific biological marker of interest in one sample.
  • a microarray may be constructed based on the disease- specific marker genes that are disclosed throughout the present specification.
  • oligonucleotide probes that specifically hybridize with the expression products (mRNA or cDNA) from each of the heart-specific marker genes tested are immobilized on a solid support, most preferably on an ordered arrangement, so as to manufacture the DNA microarray.
  • These marker gene-specific detection probes should be designed and used in conditions such that only nucleic acids having a heart-specific marker gene sequence may hybridize and give a positive result.
  • microarrays such as those provided by Affymetrix (California), may be used with the present invention.
  • the high density array will typically include a number of probes that specifically hybridize to the sequences of interest. See WO 99/32660 for methods of producing probes for a given gene or genes.
  • the array will include one or more control probes. Nucleic acid probes immobilized on the microarrav devices
  • High density array chips include « test probes » that specifically hybridize with mRNAs or cDNAs consisting of the products of expression of the heart-specific biological markers that are described herein.
  • Test probes may be oligonucleotides that range from about 5 to about 500 or about 5 to about 50 nucleotides, more preferably from about 10 to about 40 nucleotides and most preferably from about 15 to about 40 nucleotides in length. In other particularly preferred embodiments, the probes are about 20 or 25 nucleotides in length. In another preferred embodiment, test probes are double or single strand DNA sequences. DNA sequences may be isolated or cloned from natural sources or amplified from natural sources using natural nucleic acid as templates. These probes have sequences complementary to particular subsequences of the heart-specific markers whose expression they are designed to detect.
  • the high density array can contain a number of control probes.
  • the control probes fall into three categories referred to herein as normalization controls; expression level controls; and mismatch controls.
  • Normalization controls are oligonucleotide or other nucleic acid probes that are complementary to labeled reference oligonucleotides or other nucleic acid sequences that are added to the nucleic acid sample.
  • the signals obtained from the normalization controls after hybridization provide a control for variations in hybridization conditions, label intensity, "reading" efficiency and other factors that may cause the signal of a perfect hybridization to vary between arrays.
  • signals e.g.
  • any probe may serve as a normalization control.
  • Preferred normalization probes are selected to reflect the average length of the other probes present in the array; however, they can be selected to cover a range of lengths.
  • the normalization control(s) can also be selected to reflect the (average) base composition of the other probes in the array, however in a preferred embodiment, only one or a few probes are used and they are selected such that they hybridize well (i.e., no secondary structure) and do not match any target-specific probes.
  • Expression level controls are probes that hybridize specifically with constitutively expressed genes in the biological sample. Virtually any constitutively expressed gene provides a suitable target for expression level controls. Typical expression level control probes have sequences complementary to subsequences of constitutively expressed "housekeeping genes" including the .beta.-actin gene, the transferrin receptor gene, and the GAPDH gene. Mismatch controls may also be provided for the probes to the target genes, for expression level controls or for normalization controls. Mismatch controls are oligonucleotide probes or other nucleic acid probes identical to their corresponding test or control probes except for the presence of one or more mismatched bases.
  • a mismatched base is a base selected so that it is not complementary to the corresponding base in the target sequence to which the probe would otherwise specifically hybridize.
  • One or more mismatches are selected such that under appropriate hybridization conditions (e.g., stringent conditions) the test or control probe would be expected to hybridize with its target sequence, but the mismatch probe would not hybridize (or would hybridize to a significantly lesser extent).
  • Preferred mismatch probes contain a central mismatch. Thus, for example, where a probe is a twenty- mer, a corresponding mismatch probe may have the identical sequence except for a single base mismatch (e.g., substituting a G, a C or a T for an A) at any of positions 6 through 14 (the central mismatch).
  • Mismatch probes thus provide a control for non-specific binding or cross hybridization to a nucleic acid in the sample other than the target to which the probe is directed. Mismatch probes also indicate whether hybridization is specific or not.
  • Solid supports containing oligonucleotide probes for differentially expressed genes can be any solid or semisolid support material known to those skilled in the art. Suitable examples include, but are not limited to, membranes, filters, tissue culture dishes, polyvinyl chloride dishes, beads, test strips, silicon or glass based chips and the like. Suitable glass wafers and hybridization methods are widely available. Any solid surface to which oligonucleotides can be bound, either directly or indirectly, either covalently or non-covalently, can be used. In some embodiments, it may be desirable to attach some oligonucleotides covalently and others non- covalently to the same solid support.
  • a preferred solid support is a high density array or DNA chip.
  • oligonucleotide probes contain a particular oligonucleotide probe in a predetermined location on the array.
  • Each predetermined location may contain more than one molecule of the probe, but each molecule within the predetermined location has an identical sequence.
  • Such predetermined locations are termed features. There may be, for example, from 2, 10, 100, 1000 to 10,000, 100,000 or 400,000 of such features on a single solid support. The solid support or the area within which the probes are attached may be on the order of a square centimeter. Methods of forming high density arrays of oligonucleotides with a minimal number of synthetic steps are known.
  • the oligonucleotide analogue array can be synthesized on a solid substrate by a variety of methods, including, but not limited to, light-directed chemical coupling, and mechanically directed coupling (see U.S. Pat. No. 5,143,854 to Pirrung et al.; U.S. Pat. No. 5,800,992 to Fodor et al.; U.S. Pat. No. 5,837,832 to Chee et al.
  • a glass surface is derivatized with a silane reagent containing a functional group, e.g., a hydroxyl or amine group blocked by a photolabile protecting group.
  • a functional group e.g., a hydroxyl or amine group blocked by a photolabile protecting group.
  • Photolysis through a photolithographic mask is used selectively to expose functional groups which are then ready to react with incoming 5' photoprotected nucleoside phosphoramidites.
  • the phosphoramidites react only with those sites which are illuminated (and thus exposed by removal of the photolabile blocking group).
  • the phosphoramidites only add to those areas selectively exposed from the preceding step. These steps are repeated until the desired array of sequences has been synthesized on the solid surface. Combinatorial synthesis of different oligonucleotide analogues at different locations on the array is determined by the pattern of illumination during synthesis and the order of addition of coupling reagents.
  • High density nucleic acid arrays can also be fabricated by depositing premade or natural nucleic acids in predetermined positions. Synthesized or natural nucleic acids are deposited on specific locations of a substrate by light directed targeting and oligonucleotide directed targeting. Another embodiment uses a dispenser that moves from region to region to deposit nucleic acids in specific spots.
  • Oligonucleotide probe arrays for expression monitoring can be made and used according to any techniques known in the art (see for example, Lockhart et al., Nat. Biotechnol. 14, 1675-1680 (1996); McGall et al., Proc. Nat. Acad. Sci. USA 93, 13555-13460 (1996).
  • Such probe arrays may contain at least two or more oligonucleotides that are complementary to or hybridize to two or more of the genes described herein.
  • Such arrays may also contain oligonucleotides that are complementary to or hybridize to at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40,41 , 42, 43, 44, 45, 46, 47, 48, 49, 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, 100, 101 , 102, 103, 104, 105, 106
  • Nucleic acid hybridization simply involves contacting a probe and target nucleic acid under conditions where the probe and its complementary target can form stable hybrid duplexes through complementary base pairing (see WO 99/32660 to Lockhart). The nucleic acids that do not form hybrid duplexes are then washed away leaving the hybridized nucleic acids to be detected, typically through detection of an attached detectable label. It is generally recognized that nucleic acids are denatured by increasing the temperature or decreasing the salt concentration of the buffer containing the nucleic acids. Under low stringency conditions (e.g., low temperature and/or high salt) hybrid duplexes (e.g., DNA-DNA, RNA-RNA or RNA-DNA) will form even where the annealed sequences are not perfectly complementary.
  • low stringency conditions e.g., low temperature and/or high salt
  • hybridization conditions may be selected to provide any degree of stringency.
  • hybridization is performed at low stringency, in this case in 6.times.SSPE-T at 37.degree. C. (0.005% Triton x-100) to ensure hybridization and then subsequent washes are performed at higher stringency (e.g., 1 .times. SSPE-T at 37.degree. C.) to eliminate mismatched hybrid duplexes. Successive washes may be performed at increasingly higher stringency (e.g.
  • Hybridization specificity may be evaluated by comparison of hybridization to the test probes with hybridization to the various controls that can be present (e.g., expression level controls, normalization controls, mismatch controls, etc.).
  • the wash is performed at the highest stringency that produces consistent results and that provides a signal intensity greater than approximately 10% of the background intensity.
  • the hybridized array may be washed at successively higher stringency solutions and read between each wash. Analysis of the data sets thus produced will reveal a wash stringency above which the hybridization pattern is not appreciably altered and which provides adequate signal for the particular oligonucleotide probes of interest.
  • the hybridized nucleic acids are typically detected by detecting one or more labels attached to the sample nucleic acids.
  • the labels may be incorporated by any of a number of means well known to those of skill in the art (see WO 99/32660 to Lockhart). Any suitable methods can be used to detect one or more of the markers described herein.
  • gas phase ion spectrometry can be used. This technique includes, e.g., laser desorption/ionization mass spectrometry.
  • the sample can be prepared prior to gas phase ion spectrometry, e.g., pre-fractionation, two-dimensional gel chromatography, high performance liquid chromatography, etc. to assist detection of markers.
  • the expression level of a heart-specific marker gene, or of a set of heart-specific marker genes may be quantified with any one of the nucleic acid amplification methods known in the art.
  • PCR polymerase chain reaction
  • a pair of primers that specifically hybridise with the target mRNA or with the target cDNA is required.
  • a pair of primers that specifically hybridise with the target nucleic acid biological marker of interest may be designed by any one of the numerous methods known in the art.
  • primers that specifically hybridize with a heart-specific marker gene described herein may be easily designed by the one skilled in the art, on the basis of the nucleic acid sequence of the said heart-specific marker gene, like it is found for example in the HGCN database.
  • At least one pair of specific primers, as well as the corresponding detection nucleic acid probe is already referenced and entirely described in the public "Quantitative PCR primer database", notably at the following Internet address : http://lpgws.nci.nih.gov/cgi-bin/PrimerViewer.
  • a specific pair of primers may be designed using the method disclosed in the US Patent n ° US 6,892,141 to Nakae et al., the entire disclosure of which is herein incorporated by reference.
  • PCR and RT-PCR methods have been developed which are capable of measuring the amount of a nucleic acid in a sample.
  • One approach measures PCR product quantity in the log phase of the reaction before the formation of reaction products plateaus (Kellogg, D. E., et al., Anal. Biochem.
  • QC-PCR quantitative competitive PCR
  • An internal control competitor in each reaction (Becker-Andre, M., Meth. Mol. Cell Biol. 2:189-201 (1991 ); Piatak, M. J., et al., BioTechniques 14:70-81 (1993); and Piatak, M. J., et al., Science 259:1749-1754 (1993)).
  • the efficiency of each reaction is normalised to the internal competitor.
  • a known amount of internal competitor is typically added to each sample.
  • the unknown target PCR product is compared with the known competitor PCR product to obtain relative quantitation.
  • a difficulty with this general approach lies in developing an internal control that amplifies with the same efficiency of the target molecule.
  • the nucleic acid amplification method that is used may consist of Real- Time quantitative PCR analysis.
  • QPCR Real-time or quantitative PCR
  • Fluorescent reporter molecules include dyes that bind double-stranded DNA (i.e. SYBR Green I) or sequence-specific probes (i.e. Molecular Beacons or TaqMan® Probes).
  • Preferred nucleic acid amplification methods are quantitative PCR amplification methods, including multiplex quantitative PCR method such as the technique disclosed in the published US patent Application n ° US 2005/0089862, to Therianos et al., the entire disclosure of which is herein incorporated by reference.
  • tumor tissue samples are snap-frozen shortly after biopsy collection. Then, total RNA from a "cardiac tissue sample” is isolated and quantified. Then, each sample of the extracted and quantified RNA is reverse- transcribed and the resulting cDNA is amplified by PCR, using a pair of specific primers for each biological marker that is quantified. Control pair of primers are simultaneously used as controls, such as pair of primers that specifically hybridise with TBP cDNA, 18S cDNA and GADPH cDNA, or any other well known "housekeeping" gene.
  • the invention also relates to a kit for the in vitro prediction or diagnosis of the occurrence of an advanced heart failure in a patient (e.g. in a cardiac tissue sample previously collected from a patient to be tested).
  • the kit comprises a plurality of reagents, each of which is capable of binding specifically with a nucleic acid that is comprised in an expression product (mRNA or cDNA) from a disease-specific marker gene selected from the heart-specific marker genes included in groups (i) to (xi) described herein.
  • Suitable reagents for binding with a marker nucleic acid include complementary nucleic acids.
  • the nucleic acid reagents may include oligonucleotides (labelled or non-labelled) fixed to a substrate, labelled oligonucleotides not bound with a substrate, pairs of PCR primers, molecular beacon probes, and the like.
  • kits for the in vitro prediction or diagnosis of the occurrence of an advanced heart failure in a patient which kit comprises means for quantifying the expression level of two or more heart-specific marker genes that are indicative of the risk of occurrence of, or of the occurrence of, an advanced heart failure disease.
  • the present invention also encompasses various alternative embodiments of the said prediction or diagnosis kit, wherein the said kit comprises combination of marker quantification means, for quantifying the expression level of various combinations of the disease-specific marker genes that are described in the present specification.
  • a prediction or diagnosis kit consists of a
  • DNA microarray comprising probes hybridizing to the nucleic acid expression products (mRNAs or cDNAs) of the heart-specific gene markers described herein.
  • This invention also pertains to a collection of nucleic acids that is useful for predicting or diagnosing the occurrence of an advanced heart failure in a patient, wherein the said collection of nucleic acids comprises a combination of at least two distinct nucleic acids, each distinct nucleic acid hybridizing specifically with a heart-specific marker gene described herein.
  • each of the said nucleic acids consists of a nucleic acid probe that specifically hybridises with an expression product of a given heart-specific gene.
  • each of the said nucleic acid is selected from the group of nucleic acids consisting of SEQ ID ⁇ to SEQ ID N ° 225 that are described elsewhere in the present specification.
  • a kit according to the invention comprises (i) a combination or a set of specific nucleic acid probes or (ii) a combination or a set of nucleic acid primers, each kind of probes of primers hybridising specifically with the expression product (mRNA or cDNA) of a heart-specific marker gene selected from the heart-specific marker genes described herein.
  • nucleic acid probes are used, including when these probes are immobilised in an arrayed ordering on a solid support.
  • specifically dedicated DNA microarrays may be manufactured by immobilising on a solid support the suitable set of gene- specific probes, the said gene-specific probes being nucleic acid fragments comprising 12 or more consecutive nucleotides of the corresponding gene-specific mRNA or cDNA.
  • the said kit comprises a combination or a set of pair of primers comprising at least two kind of pair of primers, each kind of pair of primers being selected from the group consisting of pair of primers hybridising with each of the selected heart-specific marker genes among those disclosed in the present specification.
  • a primer kit according to the invention may comprise 2 or more kinds of pair or primers, each kind of pair of primers hybridising specifically with one heart-specific marker gene of the invention.
  • a primer kit according to the invention may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40,41 , 42, 43, 44, 45, 46, 47, 48, 49, 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,
  • At least one pair of specific primers, as well as the corresponding detection nucleic acid probe, that hybridise specifically with one disease-specific marker gene of interest, is already referenced and entirely described in the public "Quantitative PCR primer database", notably at the following Internet address : http://lpgws.nci.nih.gov/cgi-bin/PrimerViewer.
  • Illustrative nucleic acids that are directly usable as primers, or alternatively that are usable for designing primers consist of the nucleic acids of SEQ ID N ° 1 to SEQ ID N ° 225 that are disclosed herein.
  • SEQ ID N ° 12 40, 81 , 1 13, 128, 130, 135, 154, 194, 214 and 216 longer nucleic acid sequences are provided that are usable by the one skilled in the art for designing the appropriate corresponding nucleic acid primers.
  • This invention also pertains to methods for selecting one or more heart-specific marker genes that are indicative of the occurrence of, or of the risk of occurrence of, an advanced heart failure in a patient.
  • the said heart-specific marker gene selection method according to the invention preferably comprises the steps of :
  • step d) selecting, in the group of candidate marker genes whose expression level is quantified at step c), those marker genes that are under-expressed or over-expressed in tissue samples provided at step b), whereby a set of marker genes is selected, the said set of marker genes comprising heart-specific marker genes, the under-expression of which, or the over- expression of which, is indicative of the occurrence of, or of the risk of occurrence of, an advanced heart failure in the said patient.
  • the marker quantification means encompass means for quantifying marker gene-specific nucleic acids, such as oligonucleotide primers or probes.
  • DNA microarrays may be used at step a) of the selection method above.
  • Means for specifically quantifying any one of the known potential marker gene, e.g. any gene-specific nucleic acid, may be provided at step a) of the selection method.
  • the plurality or the collection of cardiac tissue samples comprises samples originating from at least 5 distinct individuals affected with an advanced heart failure, and most preferably at least 20, 25 or 30 distinct individuals affected with an advanced heart failure.
  • the statistical relevance of the heart-specific marker genes that are finally selected at the end of the selection method generally increases with the number of distinct individuals tested, and thus with the number of cardiac tissue samples comprised in the plurality of tissue samples or in the collection of tissue samples that is provided at step b).
  • quantification of the candidate marker genes on the cardiac tissue samples provided at step b), using the quantification means provided at step a), may be performed according to any one of the quantification methods that are described elsewhere in the present specification.
  • each candidate marker gene quantified at step c) in the collection of cardiac tissue samples is compared to the quantification results found for the same marker in tissue samples originating from individuals not affected with an advanced heart disease. Then, only those candidate marker genes that are differentially expressed (i.e. (i) under-expressed, (ii) not expressed, (iii) over-expressed in the said collection of cardiac tissue samples, as compared to the tissue samples originating form non-affected individuals, are positively selected as heart- specific marker genes indicative of the occurrence of an advanced heart failure.
  • the selection of statistically relevant heart-specific marker genes by comparing the expression level of a candidate marker gene in one collection of cardiac tissue samples with the expression level of the said candidate marker gene in cardiac tissue samples originating from non-affected individuals, is termed a "One Versus AH" ("OVA”) pairwise comparison.
  • OVA One Versus AH
  • the statistical relevance of each candidate marker gene tested, at step d), may be performed by calculating the P value for the said marker, for example using a univariate t-test.
  • a marker is selected at step d) of the selection method above, when its P value is lower than 0.01 .
  • the statistical relevance of the marker selection, at step d) of the method may be further increased by using other statistical methods, wherein the said other statistical methods may consist of performing a multivariate permutation test, so as to provide 90% confidence that a false marker selection rate is less than 10%.
  • those markers that were initially selected as described above may be submitted to a further cycle of selection, for example by assaying the initially selected markers on further collections of cardiac tissue samples.
  • This further cycle of selection may consist of, for example, performing a further expression analysis of the initially selected markers, for example by technique of quantitative RT-PCR expression analysis or by using DNA microarrays.
  • the quantification measure of expression of each initially selected marker may be normalised against a control value, e.g. the quantification measure of expression of a control gene such as TBP.
  • the results may be expressed as N-fold difference of each marker relative to the value in normal cardiac tissues or to the value in all other cardiac tissues (normal and disease).
  • Statistical relevance of each initially selected marker is then confirmed, for example at confidence levels of more than 95% (P of less than 0.05) using the Mann-Whitney U Test.
  • Cardiac tissue was obtained from explanted hearts of 44 patients with advanced HF who underwent a cardiac transplantation or a total artificial heart placement at the France University Hospital between 1998 and 2002.
  • Pre-transplant evaluation including coronary artery angiography, cardiac catheterization, and echocardiography, confirmed the diagnosis, etiology and severity of the disease.
  • patient evaluation included physical examination, laboratory tests, and echocardiography. Macroscopic and histological examination of the explanted hearts confirmed the previously diagnosed etiology for all patients. Extensive individual clinical information can be found in Table 6.
  • UNOS status corresponds to the medical urgency status as defined.
  • An ADHF episode was defined as recent if it occurred during the 3 months preceding the heart transplantation/total artificial heart placement.
  • Heart failure duration was defined as the delay between onset of heart failure symptoms and heart transplantation/total artificial heart placement.
  • Values for HR, SAP, LVEF, LVEDD, BUN, and serum creatinine corresponded to pre-operative measurements.
  • Values for BNP were obtained within the 2 months before heart transplantation. All patients were treated with loop diuretics (furosemide and/or bumetanide). Only medications related to HF therapy are presented.
  • Unsupervised hierarchical clustering was applied to the entire data set median-centered on genes, using the Pearson correlation as a similarity metric and average linkage clustering. Results were displayed using TreeView (Eisen et al.,. Proc Natl Acad Sci U S A. 1998; 95:
  • Gene clusters were selected using 10 and 0.6 as minimal gene number and minimal correlation respectively. GoMiner was used to identify functional categories that were over- or underrepresented in specific clusters compared to the list of all analyzed genes (Zeeberg et al.,. Genome Biol. 2003; 4: R28).
  • LV- and RV-specific data were separated into distinct datasets and analyzed separately using an identical strategy:
  • the 'Predictor' was defined as a list of genes differentially expressed between Stable and Deteriorating patient groups. These genes were identified using 'Significance Analysis of Microarrays' (SAM) (Tusher et al.,. Proc Natl Acad Sci U S A. 2001 ; 98: 51 16-21 ). For each analysis we arbitrarily fixed the threshold of statistical significance so that the false-discovery rate ⁇ 1 %
  • LV and RV predictors and an LV-RV predictor were used to calculate a transcriptome- based 'molecular severity score' (MSS) for each sample.
  • the LV-RV predictor was a combination of the genes of the LV and the RV predictor.
  • expression profiles were mean- centered and standard deviation-scaled on genes. The mean profile was calculated for Stable (Ms) and for Deteriorating (Md) samples.
  • the molecular severity score (MSS) of a specific sample was defined as the normalized Euclidean distance (ranging from 0 to 1 ) between the sample and the stable mean profile and was calculated as described below:
  • Each data set was partitioned into a test set consisting of one sample and a learning set consisting of the 99 other samples.
  • the learning set was used to calculate an LV-RV-MSS using the strategy described.
  • the obtained MSS was employed to predict the MSS value of the test sample. This process was repeated so that the MSS value of each sample was predicted using an MSS estimated from all other samples in the data set.
  • Each of the 176 biological samples was compared to a common reference sample consisting of a pool of equal quantities of mRNA from all 176 biological samples. This complex mRNA pool was used as a standard or a common point of measurement that enabled a comparison between the individual mRNA samples.
  • Microarrays were prepared in-house using human-specific 50-mer oligonucleotide probes (MWG Biotech®). The probes were spotted onto epoxy-silane coated glass slides using the Lucidea Array Spotter (Amersham®). The 4217 human genes that were represented on the microarray had been selected for involvement in cardiovascular and/or skeletal muscle normal and pathological functioning. Selection was based on 1 - subtractive hybridization experiments (Rouger K, et al., Am J Physiol Cell Physiol. 2002; 283: C773-C784; Steenman et al.,. Eur J Heart Fail. 2005; 7: 157-65), 2- genome-wide microarray hybridizations (Steenman et al.,. Physiol Genomics. 2003; 12: 97-1 12), 3- literature data. Each probe was spotted in quadruplicate. For more information, see the following Web address:
  • human Cot-I DNA Gibco-BRL
  • yeast tRNA yeast tRNA
  • polyA RNA polyA RNA
  • Hybridized arrays were scanned by fluorescence confocal microscopy (Scanarray 4000XL,
  • the threshold to select which genes should be included in the predictor was arbitrarily set to 2 for all the experiments. This predictor was then applied to the test set. We then calculated the sensitivity, the specificity, and the positive and negative predictive values of PAM for the prediction of Deteriorating status after averaging the results obtained for the 100 different partitions.
  • Example 1 Selection and validation of the heart-specific markers
  • CAD Coronary Artery Disease
  • DCM Dilated Cardiomyopathy
  • LVEF Left Ventricle Ejection Fraction
  • LVEDD Left Ventricle End Diastolic Diameter
  • ACEI Angiotensin Converting Enzyme Inhibitors
  • ARB Angiotensin Receptor Blockers
  • ADHF Acute Decompensated Heart Failure.
  • SD 'mean (SD)' when appropriate.
  • P-value indicates the result of a comparison between the three patient groups using Fisher's exact test or Kruskal-Wallis rank sum test. If p ⁇ 0.05, groups were compared two-by-two.
  • An ADHF episode was defined as recent if it occurred during the 3 months before the heart transplantation/total artificial heart placement.
  • HF duration was defined as the delay between onset of HF symptoms and heart transplantation/total artificial heart placement.
  • Values for LVEF, LVEDD, blood urea nitrogen, and serum creatinine corresponded to pre-operative measurements. All patients were treated with loop diuretics (furosemide and/or bumetanide). Only medications related to HF therapy are presented. The clinical profile was determined based on the patients' medical urgency status in the UNOS classification and the occurrence of recent ADHF episodes.
  • the 176 cardiac samples and the 4035 selected genes were clustered according to their expression profiles using a hierarchical clustering procedure (Data not shown). Samples were grouped in 2 major clusters mainly based on the expression profile of a 387-gene cluster (white bar). This patient molecular clustering was not correlated with the clinical severity classification. However, within each of the 2 major clusters, Stable and Deteriorating samples were preferentially classified into distinct sub-clusters (p ⁇ 0.001 within each major cluster, ⁇ 2 test).
  • Gene clusters were selected by automated analysis of the gene classification. Functional annotation revealed enrichment of genes involved in a specific biological process or tissue-type for most of the clusters. Clusters that were too small to obtain a statistically significant annotation using Gominer software (annotations 'natriuretic peptides' and 'cell metabolism') were functionally annotated based on literature analysis. Several of the clusters showed marked differential expression between Stable and Deteriorating samples for LV and/or RV samples. 'Cell metabolism', 'natriuretic peptides', and 'extracellular matrix' gene clusters displayed higher expression for Deteriorating samples than for Stable samples in both LV and RV.
  • 'Cytoskeleton' and 'cell death' gene clusters displayed higher expression for Stable samples than for Deteriorating samples in both LV and RV.
  • the 'mitochondrion' gene cluster displayed higher expression for Stable samples than for Deteriorating samples in RV but not LV.
  • Stable samples resulted in the identification of 167 and 126 differentially expressed genes for LV and RV samples respectively.
  • Sixty-six genes were present in both LV and RV predictors.
  • a 225-gene LV-RV predictor was also created by combining the genes of the LV and RV predictors ( Figure 1 ). MSS values of patients were calculated based on their individual expression profile for these three predictors.
  • Figure 1 shows MSS values calculated for the Stable and Deteriorating groups based on the different predictors.
  • the training set 95 out of 100 samples were predicted in concordance with the clinical classification, whereas one Stable sample was predicted as Deteriorating and four samples were in the unpredictable interval. These five samples were all LV samples, whereas all RV samples were correctly predicted.
  • a cross-validation strategy was also employed to account for data over-fitting due to reclassification of the samples used to define the predictors.
  • the overall good classification rate was 94/100, whereas one Stable sample was predicted as Deteriorating and five samples were in the unpredictable interval.
  • the LV molecular predictor identified patients with stable and deteriorating status with a sensitivity of 88% and 92%, and a specificity of 100% and 96%, respectively.
  • the RV molecular predictor identified patients with stable and deteriorating status with a sensitivity of 100% and 96%, and a specificity of 100% and 100%, respectively.
  • Clinical characteristics of the three patient groups are summarized in Table 1 .
  • the groups were comparable regarding sex, age, HF etiology, and LV ejection fraction. As expected, differences in severity levels were associated with significant inter-group variations regarding treatment with adrenergic agonists, phospho-diesterase inhibitors, beta-blockers, and angiotensin converting enzyme inhibitors/angiotensin receptor blockers.
  • adrenergic agonists phospho-diesterase inhibitors
  • beta-blockers beta-blockers
  • angiotensin converting enzyme inhibitors/angiotensin receptor blockers are included in Table 1 .
  • the three patient groups were comparable regarding sex, age, HF etiology, and LV ejection fraction (Table 1 ). As expected, differences in severity levels were associated with significant inter-group variations regarding treatment with adrenergic agonists, phospho-diesterase inhibitors, beta-blockers, and angiotensin converting enzyme inhibitors/angiotensin receptor blockers. Significant differences in expression related to these medications were found for only 0-7.0% of the genes included in the LV and RV predictors. Furthermore, significant differences in expression related to age and HF etiology were found for 1 .2 to 2.9% and for 0.6 to 2.3% of the genes respectively. Removing these genes from the predictors did not modify the good classification rates of the samples (data not shown). We also performed distinct prediction analysis for ischemic and non-ischemic patients. The results show that our classification can be accurately applied to both ischemic and non-ischemic patients (Figure 5).
  • Figure 6A shows a comparison of patient MSS values obtained from LV and RV data using the LV-RV predictor.
  • a significant correlation between LV and RV MSS values was observed irrespective of the sample severity group.
  • a significant correlation between MSS values obtained for the duplicate sets was observed ( Figure 6B), with a better correlation for RV samples than for LV samples.
  • Heidecker et al. identified a transcriptomic signature that could predict clinical outcome of new-onset idiopathic dilated cardiomyopathy patients (Heidecker et al; Circulation. 2008; 1 18: 238-46).
  • these findings offer valuable information regarding the molecular basis of HF related to distinct etiologies and they could lead to individualized therapeutic strategies in HF.
  • microarrays are a unique tool to screen the largest number possible of potential biomarkers, which was the aim of this study, other techniques such as quantitative RT-PCR will be of greater interest to develop a clinically relevant outcome predictor based on a set of selected biomarkers.
  • the UNOS-1 A status at the time of listing is associated with a 1 month-mortality >30% whereas UNOS-2 patients have a 1 month-mortality ⁇ 10% (Smits et al., Transplantation. 2003; 76: 1 185-9).
  • the mortality rate on the UNOS waiting list is more than 4 fold higher for UNOS-1 A than for UNOS-2 patients (Deng et al., Curr Opin Cardiol. 2002; 17: 137-44).
  • To better define our group of Stable patients we combined the UNOS medical urgency status with the occurrence of ADHF episodes. Frequent rehospitalizations have been recognized as a strong predictor of HF patient mortality ( Metra et al., Eur J Heart Fail. 2007; 9: 684-94).

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Abstract

The present invention relates to the field of diagnosis or prognosis of advanced heart failure by determining the expression profile of marker genes of interest.

Description

TITLE OF THE INVENTION
A method for the diagnosis or prognosis of an advanced heart failure.
FIELD OF THE INVENTION
The present invention relates to the field of diagnosis or prognosis of advanced heart failure by determining the expression profile of marker genes of interest.
BACKGROUND OF THE INVENTION
Heart failure is not only a disease of the elderly or of persons who live unhealthy lifestyles. The highest incidence occurs between 25-45 years of age. Although more patients are surviving their first myocardial infarction, they often go on to develop progressive left ventricular dysfunction and end stage heart failure. As a result, the incidence of congestive heart failure is increasing.
Congestive heart failure is a common clinical syndrome, in particular in elderly individuals. It usually presents in the form of an insidious triggering of nonspecific symptoms such as coughing with exercise, fatigue, and the appearance of peripheral edemas. Diagnosis is conventionally based on the study of various parameters, such as clinical signs, (classified in four stages: stages I to IV of the NYHA (i.e. of the New York Heart Association), echocardiography, scintigraphy, exercise tests, etc.
Due to the seriousness of heart disease, and also to the high costs of treating it, an early diagnosis of this syndrome is, obviously, extremely desirable: it would contribute to preventing the rapid progression of the syndrome to severe heart failure. Identifying the individuals at risk of heart failure is therefore a necessity. This would also make it possible to adapt a faster, easier and less expensive therapeutic monitoring. Unfortunately, no method for diagnosing heart failure exists that is entirely satisfactory and completely informative.
Presymptomatic markers that predict heart failure have been sought for a long time. In this regard, the fact that cardiomyocytes produce and secrete peptides with natriuretic activity has been demonstrated: a peptide of atrial origin, ANP (atrial natriuretic peptide) discovered in rats by Bold et al. Life Science 1981 , vol. 28(1 ): 89-94, and a natriuretic peptide of atrioventricular origin called BNP (brain natriuretic peptide) discovered by Sudoh et al., Nature 1988, vol. 332: 78-81 in pigs, and then in humans.
The clinical syndrome of heart failure manifests when cellular respiration becomes Impaired because the heart cannot pump enough blood io support the metabolic demands of the body, or when normal cellular respiration can only be maintained with an elevated left ventricular filling pressure
The Frarningham, Duke and Boston criteria were established before noninvasive techniques for assessing systolic and diastolic dysfunction became widely available. The three sets of criteria were designed to assist In the diagnosis of heart failure. The Boston criteria have been shown to have the highest combined sensitivity (50 percent) and specificity (78 percent;. AH of these criteria are most heipfui in diagnosing advanced or severe heart failure, a condition that occurs in 20 to 40 percent of patients with a decreased ejection fraction.9
Early diagnosis of heart failure is essential for successfully addressing underlying diseases or causes and, in some patients, preventing further myocardial dysfunction and clinical deterioration. However, iniiial diagnosis may be difficult because the presentations of heart failure can change from no symptoms to pulmonary edema with cardiogenic shock. It is estimated that heart failure is correctly diagnosed initially In only 50 percent of affected patients.
Left ventricular (LV) filling pressure is a key factor in the progression of CHF. LV filling pressure represents the diastolic pressure at which the left atrium (LA) and left ventricle (LV) equilibrate, at which time the LV fills with blood from the LA. As the heart ages, cardiac tissue becomes less compliant, causing the LV filling pressure to increase. This means that higher pressures are required from the LA in order to fill the LV. The heart must compensate for this in order to maintain adequate cardiac output (CO); however, increasing the LA pressure strains the heart and over time irreversible alteration will occur.
Left Ventricular End Diastolic Pressure (LVEDP) and Mean Left Atrium Pressure (MLAP) are the primary factors physicians use to evaluate CHF patients. MLAP and LVEDP correspond directly with LV filling pressure and are easy for physicians to identify from LV pressure data. The physician's ultimate goal is to increase cardiac output (CO) while reducing LVDEP.
Patients diagnosed for a heart failure are subjected to various well known diet and lifestyle rules and are also subjected to pharmacological treatment with various classes of active ingredients including angiotensin-modulating agents, diuretics, beta blockers, positive inotropes, alternative vasodilatators, aldosterone receptor antagonists and vasopressin receptor antagonists.
However, patients affected with an advanced heart failure need stronger and more invasive medical interventions. For instance, patients with NYHA classlll or IV, with left ventricular ejection fraction (LVEF) of 35% or less and a QRS interval of 120 ms or more may benefit from cardiac resynchronization therapy, through implantation of a bi-ventricular pacemaker, or surgical remodelling of the heart. Patients with NYHA class II, III or IV, and LVEF of 35% may also benefit from an implantable cardioverter-defibrillator. Another current treatment involves the use of left ventricular assist devices (LVADs). LVADs are battery-operated mechanical pump-type devices that are surgically implanted on the upper part of the abdomen. The final option, if other measures have failed, is heart transplantation or a temporary or prolonged implantation of an artificial heart.
Cardiac transplantation is an effective treatment option for patients with advanced heart failure (severe CHF). However, an increasing number of ambulatory patients with advanced heart failure are placed on transplant waiting lists while the supply of donor organs remains limited and fixed. Therefore, accurate identification of patients most likely to benefit from transplantation would be highly desirable, so as notably to limit as far as possible mortality in patients that would have required immediate transplantation. Indeed, knowledge of mortality predictors would be highly desirable to generate predictive models that can aid clinicians' decision making, in particular by identifying patients who are at high risk or low risk of death.
Various prior art works relate to the determination of novel clinical data that would be relevant for diagnosing heart failure or for prognosis of the outcome of a heart failure (See for example : Lee et al., 2003, JAMA, Vol. 290 (n °19) : 2581 -2587; Levy et al., 2006, Circulation, Vol. 1 13 : 1424-433; Aaronson et al., 1997, Circulation, Vol. 95 (n 0 12) : 2597-2599)
Also, several prior art studies relate to the determination of gene-based predictors of heart failure.
For instance, Liew et al. have performed a general overview of the molecular and genetic basis of heart failure and show how genomic tools may provide new perspectives relating to this complex disease (See Liew et al., 2004, Nat Rev Genet, Vol. (n °1 1 ) : 81 1 -825).
Also, Steenman et al. have performed a study aimed at investigating whether a molecular profiling approach might be pertinent for the classification of heart failure patients. These authors have measured relative expression levels of selected genes in left ventricule tissue from 17 patients (15 failing hearts and 2 non-failing hearts) and have found that 159 genes distinguished between all patients. Further, these authors identified three major subgroups of patients, each with a specific molecular portrait. The results obtained by Steenman et al. were found to encourage further development of this approach in prospective studies on heart failure patients at earlier stages of the disease.
It still remains that risk stratification in advanced heart failure is crucial for the individualization of therapeutic strategy, in particular for heart transplantation and ventricular assist device implantation.
There is still a need in the art for novel methods for the diagnosis or for the prognosis of heart failure in individuals.
SUMMARY OF THE INVENTION
The present invention relates to a method for diagnosing an advanced heart failure, or for prognosis of the outcome of an advanced heart failure, in an individual, wherein the said method comprises the steps of :
a) providing a cardiac tissue sample previously collected from the said individual;
b) quantifying, in the said cardiac tissue sample, the expression level of one or more marker genes that are indicative of the risk of occurrence of, or of the occurrence of, an advanced heart failure, wherein the said one or more marker genes are selected from the group consisting of :
ACAA1 , ACADM, ACTC, A2M, ADAMTS5, ADSL, AEBP1 , ANXA1 , ANXA10, ANXA1 1 , ANXA2, ARPC2, ATP1 B3, AZGP1 , BSG, BXDC2, BZW2, C15orf41 , C21 orf33, C1 GALT1 C1 , C1 orf63, C1 R, C4A, CAV1 , C6orf203, CCR2, CD36, CAB39, CABYR, CBX5, CCNG1 , CD63, CDH13, CDH9, CFL1 , CFL2, CHPF, CLIC5, CKM, CMYA3, COL1 A1 , COL1 A2, COL2A1 , COL3A1 , COL4A5, CSDE1 , COL6A3, CTAGE1 , CTSB, CTSD, DBI, DSTN, EIF4A2, CXX1 , DMPK, DNAJB1 1 , DXS9879E, DYNLL1 , EDG1 , EEF1 A1 , EEF1 B2, EEF2, EFEMP1 , EN03, EPAS1 , ERP29, FABP4, FAM13A1 , FGF12, FHL1 , FKBP5, FLJ22655, FLNC, FHL2, FLJ20152, FN1 , FXYD1 , G6PD, GAPDH, GBAS, Gcoml , GLUL, GPNMB, GPR133, G0T1 , GPR83, GPX3, GUK1 , HADHB, HADHSC, HCA1 12, HLA-A, HLA-DRB4, HRC, HTRA1 , HSPA4L, IFI16, IGFBP5, JAK2, KCTD15, IFITM1 , IFITM2, KCNA10, KCNJ8, KCNK10, KCNQ1 , KIAA0859, KLF13, LAMA4, LDHA, LIN10, LM07, LOC220729, LENG8, LGMN, LMNA, LM0D3, LOC284393, LOC649550, LRRFIP2, LTBP2, MAPKAPK3, ME2, MFSD5, MGST3, MGP, MIF, MMACHC, MMP2, MRCL3, MTHFD2, MYL5, MYL7, MY015A, MY0M1 , NDUFB4, NEXN, NIFIE14, NKX2-5, NPC2, NPPA, NPPB, NRAP, NRG3, NRN1 , 0R1 D5, P4HB, PALLD, PAM, PCOLCE, PCOLCE2, PDIA3, PDK4, PDLIM1 , PDLIM3, PKIA, PKM2, PKP2, PLA2G2A, PLN, POLR2L, POPDC2, PPGB, PPP2CB, PRKAA2, PRKAG2, PRAF2, PRDX1 , PRKAG3, PSMB1 , PSMB10, PTMAP1 , PTP4A2, RANBP1 , RPL12, RPL18, RPL3, RPL35, RPLPO, RPN2, RPS12, RPS19, RPS2, RPS6, RPSA, RRAS2, RYR2, S100A10, S100A1 1 , SAT, SCD, SCNN1 D, SDHA, SCNN1 A, SLC6A6, SLC9A3R2, SERPINB2, SLC1 A3, SLC40A1 , SLMAP, SMG1 , SNX26, SSR3, SPARC, SPP1 , SSR4, STAT6, TIMP1 , TLN1 , TNFRSF17, TNNI1 , TPM2, TRA1 , TTC25, TXNIP, VIM, VTN, VWF, YWHAE, ZNF189, WIF1 , ZBTB16, ZC3H7A, and ZNF9
whereby an expression value is obtained for each quantified marker gene;
c) comparing (i) the expression value obtained at step b) for each quantified marker gene with (ii) a control expression value for the said marker gene, whereby a deregulated expression level of each marker gene may be determined; and
d) predicting or diagnosing the occurrence of an advanced heart failure in the said patient if one or more of the said marker genes comprised has a deregulated expression level,;
This invention also relates to a kit for the in vitro diagnosis of an advanced heart dysfunction, or for prognosis of the outcome of an advanced heart dysfunction, in an individual, which kit comprises means for quantifying the expression level of one or more marker genes that are indicative of an advanced heart failure, which marker genes are selected from the group of marker genes that are cited above.
The instant invention also deals with a method for adapting a pharmaceutical treatment in a patient affected with an advanced heart failure comprising the steps of :
a) performing, on at least one cardiac tissue sample collected from the said patient, the in vitro diagnosis or prognosis method described above; and
b) adapting the pharmaceutical treatment of the said patient. BRIEF DESCRIPTION OF THE DRAWINGS
Figure 1 : Prediction of HF severity based on gene expression profiles
LV (Figure 1 A) and RV (Figure 1 B) predictors of HF severity were identified after comparison of expression profiles of 'Deteriorating' (D) and 'Stable' (S) patients in left (LV) and right (RV) ventricles respectively. The LV-RV predictor was determined as the combination of the LV severity and RV severity predictors. Three patient classifications were constructed based on LV, RV, and LV-RV severity predictors.
Open and filled circles correspond to Stable and Deteriorating LV samples respectively. Open and filled triangles correspond to Stable and Deteriorating RV samples respectively. Dashed lines denote upper and lower limits of the unpredictable interval.
Ordinates represent the Molecular Severity Score Value.
Figure 2 : Receiver Operating Characteristic (ROC) curves for the prediction of stable and deteriorating statuses in LV and RV samples.
Figure 3 : Prediction of HF severity based on the Natriuretic Peptide Precursor B (NPPB) gene expression level.
Individual Molecular Severity Score values obtained for the NPPB predictor are presented for Stable and Deteriorating samples. Open and filled circles correspond to Stable and Deteriorating LV samples respectively. Open and filled triangles correspond to Stable and Deteriorating RV samples respectively. The classification was performed using the strategy used for the multi-gene predictors. No unpredictable interval was calculated.
Figure 4. Prediction of HF severity in all samples.
Individual Molecular Severity Score values obtained for the LV-RV predictor are presented for all 176 analyzed samples. Open and black-filled circles correspond to Stable and Deteriorating LV samples respectively. Open and black-filled triangles correspond to Stable and Deteriorating RV samples respectively. Intermediate samples are shown in gray. Dashed lines denote upper and lower limits of the unpredictable interval calculated as 2.5th and 97.5th percentiles of the random-Molecular Severity Score).
Figure 5 :Separate prediction of HF severity for coronary artery disease (Fig. 5A) and non-coronary arterydisease (Fig. 5B) related samples.
Open and black-filled circles correspond to Stable and Deteriorating samples respectively. samples respectively. Intermediate samples are shown in gray.
Figure 6. Between-chamber and between-sample reproducibility.
Between-sample reproducibility was assessed using MSS values calculated from biological replicates. Subgroup analysis based on the origin of the sample (LV for Figure 6A or RV for Figure 6B) is shown. The correlation coefficient was used as a between-sample reproducibility index. Squares: LV samples; Triangles: RV samples. Open symbols: Stable samples; grey-filled symbols: Intermediate samples; black-filled symbols: Deteriorating samples. Abscissa : Molecular Severity Score for sample 1 ; Ordinates : Molecular Severity Score for sample 2. DETAILED DESCRIPTION OF THE INVENTION
Using a gene expression-based strategy, a collection of marker genes that are relevant for diagnosis or prognosis of an advanced heart failure has been found according to the invention.
Notably, it has been found according to the present invention that those relevant marker genes, when included in expression profiling analysis techniques, can distinguish between heart failure patients with different risks of death.
More precisely, the inventors have obtained gene expression profiles of 4217 distinct possibly cardiac-relevant genes from cardiac tissue samples originating from a cohort of patients having underwent a cardiac transplantation or the placement of a ventricular assist device.
Based on the analysis of clinical information, the patients included in the studied cohort were classified into three heart failure-severity groups, respectively (i) Deteriorating, (ii) Intermediate and (iii) Stable.
By performing a two-class statistical analysis of gene expression profiles of Deteriorating and Stable patients, the inventors have identified a collection of marker genes that are highly significant for diagnosing heart failure, as well as, importantly, diagnosing the degree of advancement of heart failure. The collection of marker genes that have been identified according to the invention reveals also a high significance for prognosis of the outcome of the tested patient.
Thus, the present invention provides for a method allowing (1 ) detecting in an individual a deregulation of the expression of one or more heart failure-specific marker genes described herein, and then (2) diagnosing the stage of progression of a heart failure in the said individual or performing a prognosis of the outcome of a heart failure in the said individual, wherein the said diagnosis or prognosis result is obtained by determining (i) the existence of a deregulated expression in one or more of the said marker genes and/or (ii) the level of the deregulated expression in one or more of the said marker genes.
The instant invention thus concerns a method for diagnosing an advanced heart failure in an individual, or for the prognosis of the outcome of an advanced heart failure in an individual, which comprises a step of determining, in a cardiac tissue sample originating from the said individual, the expression value of one or more heart failure-relevant marker genes, whereby, after comparison with a reference expression value for each of the said one or more heart failure-relevant marker genes, (i) an advanced heart failure is diagnosed, and/or (ii) the degree of advancement of the heart failure is diagnosed and/or (iii) a prognosis of the outcome of an advanced heart failure is determined.
An object of the present invention consists of an in vitro method for diagnosing an advanced heart failure, or for prognosis of the outcome of an advanced heart failure, in an individual, wherein the said method comprises the steps of :
a) providing a cardiac tissue sample previously collected from the said individual; b) quantifying, in the said cardiac tissue sample, the expression level of one or more marker genes that are indicative of the risk of occurrence of, or of the occurrence of, an advanced heart failure, wherein the said one or more marker genes are selected from the group consisting of :
ACAA1 , ACADM, ACTC, A2M, ADAMTS5, ADSL, AEBP1 , ANXA1 , ANXA10, ANXA1 1 , ANXA2, ARPC2, ATP1 B3, AZGP1 , BSG, BXDC2, BZW2, C15orf41 , C21 orf33, C1 GALT1 C1 , C1 orf63, C1 R, C4A, CAV1 , C6orf203, CCR2, CD36, CAB39, CABYR, CBX5, CCNG1 , CD63, CDH13, CDH9, CFL1 , CFL2, CHPF, CLIC5, CKM, CMYA3, COL1 A1 , COL1 A2, COL2A1 , COL3A1 , COL4A5, CSDE1 , COL6A3, CTAGE1 , CTSB, CTSD, DBI, DSTN, EIF4A2, CXX1 , DMPK, DNAJB1 1 , DXS9879E, DYNLL1 , EDG1 , EEF1 A1 , EEF1 B2, EEF2, EFEMP1 , EN03, EPAS1 , ERP29, FABP4, FAM13A1 , FGF12, FHL1 , FKBP5, FLJ22655, FLNC, FHL2, FLJ20152, FN1 , FXYD1 , G6PD, GAPDH, GBAS, Gcoml , GLUL, GPNMB, GPR133, GOT1 , GPR83, GPX3, GUK1 , HADHB, HADHSC, HCA1 12, HLA-A, HLA-DRB4, HRC, HTRA1 , HSPA4L, IFI16, IGFBP5, JAK2, KCTD15, IFITM1 , IFITM2, KCNA10, KCNJ8, KCNK10, KCNQ1 , KIAA0859, KLF13, LAMA4, LDHA, LIN10, LM07, LOC220729, LENG8, LGMN, LMNA, LMOD3, LOC284393, LOC649550, LRRFIP2, LTBP2, MAPKAPK3, ME2, MFSD5, MGST3, MGP, MIF, MMACHC, MMP2, MRCL3, MTHFD2, MYL5, MYL7, MY015A, MYOM1 , NDUFB4, NEXN, NIFIE14, NKX2-5, NPC2, NPPA, NPPB, NRAP, NRG3, NRN1 , OR1 D5, P4HB, PALLD, PAM, PCOLCE, PCOLCE2, PDIA3, PDK4, PDLIM1 , PDLIM3, PKIA, PKM2, PKP2, PLA2G2A, PLN, POLR2L, POPDC2, PPGB, PPP2CB, PRKAA2, PRKAG2, PRAF2, PRDX1 , PRKAG3, PSMB1 , PSMB10, PTMAP1 , PTP4A2, RANBP1 , RPL12, RPL18, RPL3, RPL35, RPLPO, RPN2, RPS12, RPS19, RPS2, RPS6, RPSA, RRAS2, RYR2, S100A10, S100A1 1 , SAT, SCD, SCNN1 D, SDHA, SCNN1 A, SLC6A6, SLC9A3R2, SERPINB2, SLC1 A3, SLC40A1 , SLMAP, SMG1 , SNX26, SSR3, SPARC, SPP1 , SSR4, STAT6, TIMP1 , TLN1 , TNFRSF17, TNNI1 , TPM2, TRA1 , TTC25, TXNIP, VIM, VTN, VWF, YWHAE, ZNF189, WIF1 , ZBTB16, ZC3H7A, and ZNF9
whereby an expression value is obtained for each quantified marker gene;
c) comparing (i) the expression value obtained at step b) for each quantified marker gene with (ii) a control expression value for the said marker gene, whereby a deregulated expression level of each marker gene may be determined; and
d) predicting or diagnosing the occurrence of an advanced heart failure in the said patient if one or more of the said marker genes comprised has a deregulated expression level.
As intended herein, a "heart failure", which encompasses a congestive heart failure (CHF) also termed congestive cardiac failure (CCF), consists of a condition that may result from any structural or functional cardiac disorder that impairs the ability of the heart to fill with blood or to pump a sufficient amount of blood through the body. A heart failure encompasses left- sided heart failure and right-sided heart failure. Failure of the left ventricle causes congestion of the pulmonary capillaries. If left ventricular function is extremely compromised, symptoms of poor systemic circulation become manifest, leading to dizziness, confusion and diaphoresis and cool extremities at rest. Right ventricular failure leads to congestion of systemic capillaries which causes peripheral edema or anasacra and nocturia. In more severe cases, ascites and hepatomegaly may develop. As intended herein, a heart failure consists of a disorder that is clinically identified as such using one of the known diagnosis systems including the "Framingham criteria" (McKee et al., N. Engl. J. Med., Vol. 285(26) : 1441 -1446), the "Boston criteria" (Carlson et al., 1995, Journal of chronic diseases, Vol. 38(9) : 73-739), the "Duke criteria" (Harlan et al., 1977, Ann. Intern. Med., Vol. 86(2) : 133-138) and the "Killip class" (Killip et al., 1967, Am. J. Cardiol., Vol. 20(4) : 457-464). Functional classification may be performed using the New York Heart Association Functional Classification (Criteria Committee, New York Heart Association. Diseases of the heart and blood vessels. Nomenclature and criteria for diagnosis, 6th ed., Boston : Little, Brown and Co, 1964 : 1 14).
As intended herein, an individual undergoes an "advanced heart failure" when the heart clinical status of the said individual meets the criteria defined by the United Network for Organ Sharing (UNOS) medical urgency status relating to the allocation of thoracic organs, and specifically heart. Even more specifically, an individual having an advanced heart failure consists of an individual who is classified as belonging to status 1 A, 1 B, 2 or 7 according to the above cited UNOS classification, e.g. the UNOS classification dated of July 1 1 , 2007.
A used herein, a "cardiac tissue sample" refers to a sample of the heart tissue from the patient to be tested, which heart tissue sample comprises at least the minimum number of cells allowing the production of an amount of nucleic acid expression products (e.g. mRNA or cDNA) for performing step b) of the diagnosis or prognosis method described above. Generally, the heart tissue sample comprises at least 103 cells, and preferably at least 106 cells from the heart organ. Typically, the heart tissue sample that is provided at step a) of the method may be any of the specimens such as those prepared by excision and extirpation of a small piece of heart tissue.
The terms or expressions "marker(s)", "heart marker(s)", "marker gene(s)", "heart marker gene(s)", "heart-relevant marker(s)", "heart-relevant marker gene(s)", "heart-specific marker(s)" or "heart-specific marker gene(s)" may be interchangeably used herein.
According to the present invention, a marker gene denotes a gene whose expression is deregulated only in patients undergoing an advanced heart failure as defined herein.
The following groups of marker genes according to the invention are "under-expressed" in patients affected with an advanced heart failure :
(i) LV group : ADAMTS5, ANXA10, BZW2, CKM, CMYA3, CTAGE1 , Gcoml , KIAA0859, LMOD3, MAPKAPK3, MRCL3, MRCL3, MYL5, MYL7, MYOM1 , NRAP, OR1 D5, PKP2, PLN, PLN, RRAS2, RYR2, SLMAP, TPM2, TXNIP, ZBTB16 and ZNF9, and
(ii) RV Group : ACADM, ACTC, ADAMTS5, BZW2, C21 orf33, CAV1 , CCR2, CD36, CDH13, CFL1 , CFL2, CLIC5, CMYA3, CSDE1 , CTAGE1 , DBI, DSTN, EIF4A2, EPAS1 , FGF12, FHL1 , FHL1 , FKBP5, FLNC, GBAS, Gcoml , GLUL, GPNMB, HADHB, HADHSC, IGFBP5, JAK2, LIN10, LM07, LOC220729, MAPKAPK3, ME2, MGST3, MRCL3, MYL7, MYOM1 , NDUFB4, NEXN, NEXN, NRAP, OR1 D5, PALLD, PKIA, PKP2, PLN, PLN, POLR2L, PPP2CB, PRKAA2, PRKAG2, RGS5, RRAS2, RYR2, SCNN1 D, SDHA, SLC9A3R2, SLMAP, SSR3, TXNIP, TXNIP, YWHAE, ZNF189 and ZNF9.
Thus, in patients affected with an advanced heart failure, the expression level of the marker genes listed in (i) and (ii) above are lower than the expression level of the same genes that is determined in patients which are not affected with an advanced heart failure, including patients which are not affected with any heart dysfunction.
The following groups of marker genes according to the invention are "over-expressed" in patients affected with an advanced heart failure :
(iii) LV group : A2M, ADSL, AEBP1 , ANXA1 , ANXA1 1 , ANXA2, ARPC2, ATP1 B3, AZGP1 , BSG, BXDC2, C15orf41 , C1 GALT1 C1 , C1 orf63, C1 R, C4A, C6orf203, CAB39, CABYR, CBX5,
CCNG1 , CD63, CHPF, COL1 A1 , COL1 A1 , COL1 A1 , COL1 A2, COL2A1 , COL3A1 , COL6A3, CTSB, CTSD, CXX1 , DMPK, DNAJB1 1 , DXS9879E, DYNLL1 , EDG1 , EEF1 A1 , EEF1 B2, EEF2, EFEMP1 , EN03, ERP29, FAM13A1 , FHL2, FLJ20152, FN1 , FXYD1 , G6PD, GAPDH, GOT1 , GPR83, GPX3, GUK1 , HCA1 12, HLA-A, HLA-DRB4, HRC, HSPA4L, IFITM1 , IFITM2, KCNA10, KCNJ8, KCNK10, KCNQ1 , KLF13, LAMA4, LDHA, LENG8, LGMN, LMNA, LOC284393, LOC649550, LRRFIP2, LTBP2, MFSD5, MGP, MIF, MMACHC, MMP2, MTHFD2, MY015A, NIFIE14, NKX2-5, NPC2, NPPA, NPPB, NRG3, NRN1 , P4HB, PAM, PCOLCE, PDK4, PDLIM1 , PDLIM3, PKM2, PLA2G2A, POPDC2, PPGB, PRAF2, PRDX1 , PRKAG3, PSMB1 , PSMB10, PTMAP1 , PTP4A2, RANBP1 , RPL12, RPL18, RPL3, RPL35, RPLPO, RPN2, RPS12, RPS19, RPS2, RPS6, RPSA, S100A10, S100A1 1 , SAT, SCNN1 A, SERPINB2, SLC1 A3, SLC40A1 , SMG1 , SNX26, SPARC, SPP1 , SSR4, STAT6, TIMP1 , TLN1 , TNFRSF17, TNNI1 , TRA1 , TTC25, VIM, VTN, VWF, WIF1 and ZC3H7A.
(iv) RV group : ACAA1 , ARPC2, BSG, C15orf41 , C4A, CD63, CDH9, CHPF, COL1 A1 , COL1 A1 , COL1 A1 , COL1 A2, COL2A1 , COL3A1 , COL4A5, CTSB, CTSD, EN03, FABP4, FAM13A1 , FLJ22655, FN1 , GAPDH, GPR133, GPR83, GPX3, HRC, HTRA1 , IFI16, KCTD15, LAMA4, LDHA, MFSD5, MIF, MMACHC, MTHFD2, NPC2, NPPA, NPPB, NRG3, P4HB, PAM, PCOLCE2, PDIA3, PDLIM1 , PDLIM3, PLA2G2A, PPGB, PRKAG3, S100A10, S100A1 1 , SAT, SCD, SERPINB2, SLC6A6, SNX26, SSR4, TIMP1 , TRA1 , VTN and VWF.
Thus, in patients affected with an advanced heart failure, the expression level of the marker genes listed in (iii) and (iv) above are higher than the expression level of the same genes that is determined in patients which are not affected with an advanced heart failure, including patients which are not affected with any heart dysfunction.
The method according to the invention that is described above consists of a "diagnosis" method since it is shown herein a specific relationship between (i) a deregulation of the expression of each marker gene disclosed in the present specification and (ii) the occurrence of an advanced heart failure for the patient tested.
The method according to the invention that is described above consists of a "prognosis" or a "prediction" method, since the marker gene expression deregulation, that is determined after having performed the comparison step c), is indicative of the outcome of the advanced heart failure, i.e. allows to predict if the patient tested ay be classified as (i) a patient having a stable advanced heart failure, (ii) a patient having an intermediate (slow progression ) advanced heart failure or (ii) a patient having a deteriorating advanced heart failure.
At step b) of the method, quantifying the expression level of marker genes encompasses determining an absolute or relative quantification value that illustrates the said marker genes expression activity. Quantification encompasses determining a quantification value for the mRNA synthesized by each of the marker genes tested, or of the cDNA that may be obtained from the corresponding mRNA. As it will be specified further in the present specification, the quantification value that is determined at step b) of the method may consist of an absolute quantification value that reflects the amount of mRNA produced from each marker gene tested that is present in the patient's cardiac tissue sample. In other embodiments of the method, the said quantification value may be expressed as a relative value, e.g.. the ratio between (i) the amount of mRNA produced by the marker gene tested and (ii) the amount of mRNA produced by a gene that is constitutively expressed, e.g. a house-keeping gene like actin.
As used herein, a "gene" encompasses, or alternatively consists of, a nucleic acid that is contained in the human genome and which is expressible, i.e. which is able to give rise to a corresponding mRNA. Thus, a "gene", as used in the present specification, consists of a human genomic nucleic acid encoding a mRNA, whether or not the encoded mRNA codes for a polypeptide.
The nucleic acid sequence, especially the cDNA sequence, of each of the heart-specific marker genes that are described herein is easily available to the one skilled in the art. For example, the one skilled in the art may refer to the gene names listed in Tables 1 to 4, which gene names consist of the unequivocal name of each human gene that is attributed by the HUGO Gene Nomenclature Committee (HGCN). Nucleic acid sequences of the heart-specific marker genes of the invention are thus available upon query at the HGCN database on the basis of the internationally recognized gene name, e.g. at the following Web address : http//www.gene.ucl.ac.uk/cgi-bin/nomenclature/searchgenes.pl.
In all cases, the one skilled in the art may design detection and/or quantification means, including nucleic acid primers or probes, specific for every one of the marker genes of interest described herein, on the basis of the nucleic acid sequences of these marker genes which are available in various sequence databases, including the HGCN database cited above.
Illustratively pair of primers that specifically hybridise with the target nucleic acid gene marker of interest may be designed by any one of the numerous methods known in the art, based on the known partial or complete sequence of the said marker gene.
In certain embodiments, for each of the marker genes of the invention, at least one pair of specific primers, as well as the corresponding detection nucleic acid probe, is already referenced and entirely described in the public "Quantitative PCR primer database", notably at the following Internet address : "http://lpgws.nci.nih.gov/cgi-bin/PrimerViewer".
In all cases, the one skilled in the art may design nucleic acid primers or probes that specifically hybridise with each of the marker genes described herein, starting from their known 3'-end and/or 5'-end nucleic acid sequences. In other embodiments, a specific pair of primers may be designed using the method disclosed in the US Patent n ° US 6,892,141 to Nakae et al., the entire disclosure of which is herein incorporated by reference.
Examples of polynucleotides that are directly usable as primers or probes, or alternatively are usable for designing primers or probes, for the purpose of the invention consist of the polynucleotides having the nucleic acid sequences SEQ ID N ° 1 to 225, each polynucleotide being specific for a given heart-specific marker gene described herein, as it will be detailed elsewhere in the present specification. Table 1 herein discloses references to nucleic acids that may be used as primers or probes that specifically hybridize with each of the heart-specific marker genes according to the invention. In Table 1 , left column contains the abbreviated designation (Internationally recognized gene symbol) of each of the heart-specific marker gene of the invention. Right column contains the reference number (SEQ ID NO) of a nucleic acid that hybridises specifically with the corresponding marker gene expression product (e.g. mRNA or cDNA), as described in the Sequence Listing comprised in the present specification.
Further, Table 2 contains data additional to those of Table 1 relating to the unequivocal identity of each of the heart-specific marker gene according to the invention. In Table 2, the left column named "Symbol" indicates the abbreviated designation (Internationally recognized gene symbol) of each of the marker genes. The column named "Accession Nb" indicates the accession number of the corresponding nucleic acid sequence in various sequence databases, including the EMBL and the GenBank databases. The column named "EntrezGenelD" indicates the accession number of the corresponding nucleic acid sequence in the sequence database from the National Center for Biotechnology Information (NCBI). The column named "Gene Name" indicates the complete internationally recognized name of the corresponding heart- specific marker gene. The column named "RefSeq" indicates the accession number of the corresponding nucleic acid sequence in various sequence databases, including the EMBL and the GenBank databases. Finally, the column named "UniGenelD" indicates the accession number of the corresponding heart-specific marker gene in the NCBI Unigene database of transcribed sequences.
As disclosed in the examples herein, the inventors have performed a wide range differential expression analysis of more than 4000 candidate potentially heart-relevant genes on cardiac tissue samples previously collected from patients with advanced heart failure who underwent a cardiac transplantation or a total artificial heart placement.
The wide range differential expression analysis of more than 4000 candidate genes was performed by :
(i) manufacturing DNA microarrays by immobilization on a solid support of labeled candidate gene-specific probes. Each labeled probe has the ability to specifically hybridize with an expression product of the corresponding candidate gene.;
(ii) (ii-a) extracting the total RNA material from each cardiac tissue sample previously collected from patients affected with an advanced heart failure, and (ii-b) obtaining labeled cDNAs from the total population of mRNAs extracted at step (ii-a). Total RNA extracted from individuals that are not affected with an advanced heart failure is also used, as further controls.
(iii) hybridizing the labeled cDNAs obtained at step (ii) with the DNA microarrays manufactured at step (i);
(iv) measuring the signal intensities that are generated after the formation of hybrid complexes between (i) a plurality of probes immobilized on the DNA microarrays and (ii) a plurality of labeled cDNAs, whereby an expression value for each candidate gene is obtained, and
(v) determining :
(v-1 ) for each of the candidate gene tested, if there exists a relevant relationship between the expression level value of the said candidate gene, as measured at step (iv), and the occurrence of an advanced heart failure, or
(v-2) for sets of two or more of the candidate genes tested, if there exists a relevant relationship between the expression level value of the said candidate genes, as measured at step (iv), and the occurrence of an advanced heart failure.
The determination of a relevant relationship between the expression level value of the said candidate gene, at step (v-2) above, and the occurrence of an advanced heart failure, may be performed by any one of the methods of the suitable statistical analysis that are well known from the one skilled in the art.
For providing more accuracy to the final diagnosis or prognosis method according to the invention, the patients undergoing an advanced heart failure from which originated the cardiac tissue samples were classified into three severity groups, respectively "Stable" (UNOS-2 status with no recent ADHF), "Intermediate" (UNOS-1 B status or UNOS-2 status with recent ADHF) and "Deteriorating" (UNOS-1 A status), thus according to the clinical status grades defined by UNOS, and further according to additional clinical data including the occurrence of hospitalizations for Acute Decompensated Heart Failure during the three months prior to the surgical procedure ("recent ADHF").
For providing still more accuracy to the final diagnosis or prognosis method according to the invention, two spatially distinct transmural samples were obtained from both left ventricle (LV) and right ventricle (RV) immediately after cardiac transplantation, so as to evaluate the statistical relevance of a deregulation of each of the candidate genes for an advanced heart failure caused by a dysfunction of the left ventricle, the right ventricle, or both ventricles, respectively.
For identifying the heart failure-relevant marker genes according to the invention, expression analysis of the candidate genes was performed separately with the left ventricle and the right ventricle cardiac samples.
The determination of a relevant relationship between (A) the expression value of a candidate gene, as measured at step (iv) of the differential expression analysis method above, and (B) the occurrence of, and/or the severity of, an advanced heart failure was performed as follows :
(1 ) selecting those candidate genes that are differentially expressed in Stable patients cardiac samples as compared with Deteriorating patients cardiac samples, separately for LV and RV samples respectively. This step may be performed using the Significance Analysis of Microarrays method (SAM) that is described by Tusher et al. (2001 , Proc Natl Acad Sci USA, Vol. 98 : 51 16-5121 );
(2) performing a mean-centering and a standard deviation-scaling on the differentially expressed candidate genes selected at step (1 ) above, whereby providing a mean expression profile for Stable (Ms) and Deteriorating (Md) patients cardiac samples,
(3) determining a Molecular Severity Score value (MSS) for each of the patient's cardiac sample tested, wherein the MSS value consists of the normalized Euclidean distance, ranging from 0 to 1 , between a given sample and the stable mean profile,
(4) determining the statistical significance of the MSS value obtained at step (3), and
(5) performing a leave-one-out cross validation of the previously obtained MSS values on Stable and Deteriorating samples.
According to the invention, a candidate gene consists of a heart-relevant marker gene if its predictive score value, calculates as a p value, is lower than 0.01 , when using the method generally disclosed above and detailed in the examples herein.
A complete list of the candidate genes that have been finally selected as heart-relevant marker genes according to the invention is disclosed notably in tables 3 and 4 herein.
Table 3 lists the heart-relevant marker genes according to the invention that are predictive for an advanced heart failure in general, as well as for an advanced heart failure due to a dysfunction of the left ventricle. In Table 3, the left column indicates the identity of each of the marker genes by its internationally recognized HGCN Gene symbol. The second column named "Up/Downregulated" indicates if the corresponding marker gene is over-expressed
("UP") or under-expressed ("DOWN") in the course of occurrence of an advanced heart failure.
The third column named "P value" indicates the statistical relevance of the said marker gene, expressed as a P value.
Table 4 lists the heart-relevant marker genes according to the invention that are predictive for an advanced heart failure in general, as well as for an advanced heart failure due to a dysfunction of the right ventricle. In Table 4, the left column indicates the identity of each of the marker genes by its internationally recognized HGCN Gene symbol. The second column named "Up/Downregulated" indicates if the corresponding marker gene is over-expressed ("UP") or under-expressed ("DOWN") in the course of occurrence of an advanced heart failure. The third column named "P value" indicates the statistical relevance of the said marker gene, expressed as a P value.
The results obtained by the inventors have shown that, starting from the marker genes, statistical relevance data determined from left ventricle (LV) and right ventricle (RV), respectively, the whole marker genes separately selected for LV and RV form a collection of marker genes wherein each of the marker genes comprised therein are useful as a marker gene of an advanced heart failure in general.
The general collection of heart-relevant marker genes according to the invention consists of the collection of marker genes that is initially specified above in the present specification, where the general features of the diagnosis or the prognosis method according to the invention are defined.
Thus, in certain embodiments of the method for diagnosis or prognosis according to the invention, step b) consists of quantifying the expression level of one or more marker genes indicative of an advanced heart failure involving a left ventricle dysfunction that are selected from the group consisting of : A2M, ADAMTS5, ADSL, AEBP1, ANXA1, ANXA10, ANXA11, ANXA2, ARPC2, ATP1B3, AZGP1, BSG, BXDC2, BZW2, C15orf41, C1GALT1C1, C1orf63, C1R, C4A, C6orf203, CAB39, CABYR, CBX5, CCNG1, CD63, CHPF, CKM, CMYA3, COL1A1, COL1A2, COL2A1, COL3A1, COL6A3, CTAGE1, CTSB, CTSD, CXX1, DMPK, DNAJB11, DXS9879E, DYNLL1, EDG1, EEF1A1, EEF1B2, EEF2, EFEMP1, EN03, ERP29, FAM13A1, FHL2, FLJ20152, FN1, FXYD1, G6PD, GAPDH, Gcoml, GOT1, GPR83, GPX3, GUK1, HCA112, HLA-A, HLA-DRB4, HRC, HSPA4L, IFITM1, IFITM2, KCNA10, KCNJ8, KCNK10, KCNQ1, KIAA0859, KLF13, LAMA4, LDHA, LENG8, LGMN, LMNA, LMOD3, LOC284393, LOC649550, LRRFIP2, LTBP2, MAPKAPK3, MFSD5, MGP, MIF, MMACHC, MMP2, MRCL3, MTHFD2, MYL5, MYL7, MY015A, MYOM1, NIFIE14, NKX2-5, NPC2, NPPA, NPPB, NRAP, NRG3, NRN1, OR1D5, P4HB, PAM, PCOLCE, PDK4, PDLIM1, PDLIM3, PKM2, PKP2, PLA2G2A, PLN, POPDC2, PPGB, PRAF2, PRDX1, PRKAG3, PSMB1, PSMB10, PTMAP1, PTP4A2, RANBP1, RPL12, RPL18, RPL3, RPL35, RPLPO, RPN2, RPS12, RPS19, RPS2, RPS6, RPSA, RRAS2, RYR2, S100A10, S100A11, SAT, SCNN1A, SERPINB2, SLC1A3, SLC40A1, SLMAP, SMG1, SNX26, SPARC, SPP1, SSR4, STAT6, TIMP1, TLN1, TNFRSF17, TNNI1, TPM2, TRA1, TTC25, TXNIP, VIM, VTN, VWF, WIF1, ZBTB16, ZC3H7A and ZNF9.
In other embodiments of the method for diagnosis or prognosis according to the invention, step b) consists of quantifying the expression level of one or more marker genes indicative of an advanced heart failure involving a right ventricle dysfunction that are selected from the group consisting of : ACAA1, ACADM, ACTC, ADAMTS5, ARPC2, BSG, BZW2, C15orf41, C21orf33, C4A, CAV1, CCR2, CD36, CD63, CDH13, CDH9, CFL1, CFL2, CHPF, CLIC5, CMYA3, COL1A1, COL1A2, COL2A1„COL3A1 , COL4A5, CSDE1, CTAGE1, CTSB, CTSD, DBI, DSTN, EIF4A2, EN03, EPAS1, FABP4, FAM13A1, FGF12, FHL1, FKBP5, FLJ22655, FLNC, FN1, GAPDH, GBAS, Gcoml, GLUL, GPNMB, GPR133, GPR83, GPX3, HADHB, HADHSC, HRC, HTRA1, IFI16, IGFBP5, JAK2, KCTD15, LAMA4, LDHA, LIN10, LM07, LOC220729, MAPKAPK3, ME2, MFSD5, MGST3, MIF, MMACHC, MRCL3, MTHFD2, MYL7, MYOM1, NDUFB4, NEXN, NEXN, NPC2, NPPA, NPPB, NRAP, NRG3, OR1D5, P4HB, PALLD, PAM, PCOLCE2, PDIA3, PDLIM1, PDLIM3, PKIA, PKP2, PLA2G2A, PLN, PLN, POLR2L, PPGB, PPP2CB, PRKAA2, PRKAG2, PRKAG3, RGS5, RRAS2, RYR2, S100A10, S100A11, SAT, SCD, SCNN1D, SDHA, SERPINB2, SLC6A6, SLC9A3R2, SLMAP, SNX26, SSR3, SSR4, TIMP1 , TRA1 , TXNIP, TXNIP, VTN, VWF, YWHAE, ZNF189 and ZNF9. As it is evidenced from the list of the gene markers for the left ventricle and for the right ventricle above, (i) some markers have a deregulated expression only when the advanced heart failure involves a dysfunction of the left ventricle, (ii) some markers have a deregulated expression only when the advanced heart failure involves a dysfunction of the right ventricle, and (iii) the remaining markers are common to both lists, which means that these remaining markers have a deregulated expression when the advanced heart failure indifferently involves a dysfunction of the left ventricle or of the right ventricle.
Thus, in certain other embodiments of the method for diagnosis or prognosis according to the invention, step b) consists of quantifying the expression level of one or more marker genes indicative of an advanced heart failure involving specifically the left ventricle dysfunction that are selected from the group consisting of : A2M, , ADSL, AEBP1 , ANXA1 , ANXA10, ANXA1 1 , ANXA2, , ATP1 B3, AZGP1 , , BXDC2, C1 GALT1 C1 , C1 orf63, C1 R, C6orf203, CAB39, CABYR, CBX5, CCNG1 , CKM, COL6A3, CXX1 , DMPK, DNAJB1 1 , DXS9879E, DYNLL1 , EDG1 , EEF1 A1 , EEF1 B2, EEF2, EFEMP1 , ERP29, FHL2, FLJ20152, FXYD1 , G6PD, GOT1 , GUK1 , HCA1 12, HLA-A, HLA-DRB4, HSPA4L, IFITM1 , IFITM2, KCNA10, KCNJ8, KCNK10, KCNQ1 , KIAA0859, KLF13, LENG8, LGMN, LMNA, LMOD3, LOC284393, LOC649550, LRRFIP2, LTBP2, MGP, MMP2, MYL5, MY015A, NIFIE14, NKX2-5, NRN1 , PCOLCE, PDK4, PKM2, POPDC2, PRAF2, PRDX1 , PSMB1 , PSMB10, PTMAP1 , PTP4A2, RANBP1 , RPL12, RPL18, RPL3, RPL35, RPLPO, RPN2, RPS12, RPS19, RPS2, RPS6, RPSA, SCNN1 A, SLC1 A3, SLC40A1 , SMG1 , SPARC, SPP1 , STAT6, TLN1 , TNFRSF17, TNNI1 , TPM2, TTC25, VIM, WIF1 , ZBTB16 and ZC3H7A.
In still other embodiments of the method for diagnosis or prognosis according to the invention, step b) consists of quantifying the expression level of one or more marker genes indicative of an advanced heart failure involving specifically the right ventricle dysfunction that are selected from the group consisting of : ACAA1 , ACADM, ACTC, C21 orf33, CAV1 , CCR2, CD36, CDH13, CDH9, CFL1 , CFL2, , CLIC5, COL4A5, CSDE1 , DBI, DSTN, EIF4A2, EPAS1 , FABP4, FGF12, FHL1 , FKBP5, FLJ22655, FLNC, GBAS, GLUL, GPNMB, GPR133, HADHB, HADHSC, HTRA1 , IFI16, IGFBP5, JAK2, KCTD15, LIN10, LM07, LOC220729, ME2, MGST3, NDUFB4, NEXN, NEXN, PALLD, , PCOLCE2, PDIA3, PKIA, PLN, POLR2L, PPGB, PPP2CB, PRKAA2, PRKAG2, RGS5, SCD, SCNN1 D, SDHA, SLC6A6, SLC9A3R2, SSR3, TXNIP, YWHAE and ZNF189.
In view of obtaining a complete diagnosis or a prognosis of an advanced heart failure of an individual while determining if the diagnosed or prognosed heart failure involves (i) the left ventricle, (ii) the right ventricle or (iii) both the eft ventricle and the right ventricle, then the diagnosis or prognosis method according to the invention is preferably performed by quantifying, at step b) of the method, at least one left ventricle-specific marker gene and at least one right ventricle-specific marker gene.
Thus, in yet further embodiments of the method for diagnosis or prognosis according to the invention, step b) consists of quantifying the expression level of two or more marker genes, respectively : (ί) one or more markers predictive of a left ventricle dysfunction selected from the group consisting of : A2M, ADSL, AEBP1, ANXA1 , ANXA10, ANXA11 , ANXA2, , ATP1B3, AZGP1, , BXDC2, C1GALT1C1, C1orf63, C1R, C6orf203, CAB39, CABYR, CBX5, CCNG1, CKM, COL6A3, CXX1, DMPK, DNAJB11, DXS9879E, DYNLL1, EDG1, EEF1A1, EEF1B2, EEF2, EFEMP1, ERP29, FHL2, FLJ20152, FXYD1, G6PD, GOT1, GUK1 , HCA112, HLA-A, HLA- DRB4, HSPA4L, IFITM1, IFITM2, KCNA10, KCNJ8, KCNK10, KCNQ1, KIAA0859, KLF13, LENG8, LGMN, LMNA, LMOD3, LOC284393, LOC649550, LRRFIP2, LTBP2, MGP, MMP2, MYL5, MY015A, NIFIE14, NKX2-5, NRN1, PCOLCE, PDK4, PKM2, POPDC2, PRAF2, PRDX1, PSMB1, PSMB10, PTMAP1, PTP4A2, RANBP1, RPL12, RPL18, RPL3, RPL35, RPLPO, RPN2, RPS12, RPS19, RPS2, RPS6, RPSA, SCNN1A, SLC1A3, SLC40A1, SMG1, SPARC, SPP1, STAT6, TLN1, TNFRSF17, TNNI1, TPM2, TTC25, VIM, WIF1, ZBTB16 and ZC3H7A, and
(ii) one or more markers predictive of a right ventricle dysfunction selected from the group consisting of : ACAA1, ACADM, ACTC, C21orf33, CAV1 , CCR2, CD36, CDH13, CDH9, CFL1, CFL2, , CLIC5, COL4A5, CSDE1, DBI, DSTN, EIF4A2, EPAS1 , FABP4, FGF12, FHL1, FKBP5, FLJ22655, FLNC, GBAS, GLUL, GPNMB, GPR133, HADHB, HADHSC, HTRA1, IFI16, IGFBP5, JAK2, KCTD15, LINK), LM07, LOC220729, ME2, MGST3, NDUFB4, NEXN, NEXN, PALLD, , PCOLCE2, PDIA3, PKIA, PLN, POLR2L, PPGB, PPP2CB, PRKAA2, PRKAG2, RGS5, SCD, SCNN1D, SDHA, SLC6A6, SLC9A3R2, SSR3, TXNIP, YWHAE and ZNF189.
As already described above in the present specification, every one of the heart-relevant marker genes has a high statistical relevance for diagnosing or predicting an advanced heart failure, with a P value which always lower than 0.01 (1x10~2) and up to less than 1x10"11.
When performing the diagnosis or prognosis method according to the invention by quantifying, at step b) of the method, the expression level of only one marker gene or of only a low number of marker genes, e.g. less than 10 marker genes, then a good accuracy of the diagnosis or prognosis of an advanced heart failure may nevertheless be obtained if highly relevant marker(s) are quantified, e.g. marker(s) having a P value lower than 0.0001 (1x10~4) or even lower than 1x10~6.
For selecting marker genes with the desired statistical relevance, the one skilled in the art is fully guided by the marker information that are contained notably in tables 3 and 4 herein.
Thus, in certain embodiments of the diagnosis or prognosis method according to the invention, the one or more left ventricle-specific markers that are quantified at step b) of the method are selected from the group consisting of : MYL7, VTN, LDHA, CD63, A2M, CTSD, BSG, FXYD1, VWF, PDLIM3, C4A, S100A11, C15orf41, PLN, EDG1, RYR2, MYOM1, ANXA11, PAM, ADAMTS5, MIF, HCA112, LAMA4, PLN, AZGP1, BXDC2, DYNLL1, KCNJ8, NPPA, NPPB, EN03, NRG3, LENG8, CAB39, CTSB, LMOD3, COL1A1, CMYA3, BZW2, MMACHC, Gcoml, GUK1, CHPF, COL1A1, PKM2, P4HB, HRC, MYL5, SSR4, MTHFD2, NKX2-5, COL3A1, RRAS2, LGMN, PLA2G2A, PRKAG3, C6orf203, TNNI1, ZBTB16, LTBP2, ZNF9, EEF1A1 and RPS19. In certain other embodiments of the diagnosis or prognosis method according to the invention, the one or more left ventricle-specific marker genes that are quantified at step b) of the method are selected from the group consisting of : PLN, LDHA, ZNF9, MYOM1 , MYL7, PKP2, SLMAP, NRAP, BZW2, VTN, MMACHC, NDUFB4, NPPA, PDLIM3, BSG, ACTC, EPAS1 , MAPKAPK3, NPPB, MFSD5, COL4A5, PLA2G2A, CHPF, MIF, ARPC2, PKIA, CD63, PRKAA2, ADAMTS5, SERPINB2, HADHSC, HRC, PDLIM1 , NEXN, C4A, CLIC5, CFL2, PPGB, DBI, SSR4, TXNIP, SLC6A6, CSDE1 , GPR83, Gcoml , MTHFD2 and CMYA3.
For even more accuracy of the diagnosis or prognosis method according to the invention, the one or more marker genes that are quantified at step b) of the method may be selected from :
(i) one or more marker genes predictive of an advanced heart failure involving a left ventricle dysfunction that are selected from the group consisting of : MYL7, VTN, LDHA, CD63, A2M, CTSD, BSG, FXYD1 , VWF, PDLIM3, C4A, S100A1 1 , C15orf41 , PLN, EDG1 , RYR2, MYOM1 , ANXA1 1 and PAM, and
(ii) one or more marker genes predictive of an advanced heart failure involving a right ventricle dysfunction that are selected from the group consisting of : PLN, LDHA, ZNF9, MYOM1 , MYL7, PKP2, SLMAP, NRAP, BZW2, VTN, MMACHC and NDUFB4.
Generally, the accuracy of the prediction or the diagnosis results obtained at step d) of the in vitro method may increase with an increasing number of heart-relevant marker genes that are quentified at step b).
Depending on the accuracy of the prediction or of the diagnosis results which is sought, when performing the in vitro prediction or diagnosis method according to the invention, the one skilled in the art will adapt step b), so as to use a suitable combination of (1 ) the number of heart-specific marker genes to be quantified and (2) the predictive score (e.g. the P value) of the heart-specific marker genes to be quantified.
As it is shown in Tables 3 and 4, when an advanced heart failure occurs, (a) a part of the heart-specific marker genes are over-expressed, and (b) the remaining part of the heart- specific marker genes are under-expressed, as compared to their respective expression level value found for individuals who are not affected with an advanced heart failure, as well as for healthy individuals.
At step b) of the in vitro prediction or diagnosis method according to the invention, the number of heart-specific marker genes that are quantified may be of 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40,41 , 42, 43, 44, 45, 46, 47, 48, 49, 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, 100, 101 , 102, 103, 104, 105, 106, 107, 108, 109, 1 10, 1 1 1 , 1 12, 1 13, 1 14, 1 15, 1 16, 1 17, 1 18, 1 19, 120, 121 , 122, 123, 124, 125, 126, 127, 128, 129, 130, 131 , 132, 133, 134, 135, 136, 137, 138, 139, 140, 141 , 142, 143, 144, 145, 146, 147, 148, 149, 150, 151 , 152, 153, 154, 155, 156, 157, 158, 159, 160, 161 , 162, 163, 164, 165, 166, 167, 168, 169, 170, 171 , 172, 173, 174, 175, 176, 177, 178, 179, 180, 181 , 182, 183, 184, 185, 186, 187, 188, 189, 190, 191 , 192, 193, 194, 195, 196, 197, 198, 199; 200, 201 , 202, 203, 204, 205, 206, 207, 208, 209, 210, 21 1 , 212, 213, 214, 215, 216, 217, 218, 219, 220, 221 , 222, 223, 224 or 225.
As it will be detailed further in the specification, the comparison step c) is performed using a "control" expression value for each heart-specific marker gene that is tested. The said control expression value consists of the mean expression value for the said heart-specific marker gene that is found in individuals that are not affected with an advanced heart failure to which a deregulation of the said marker gene is associated.
In some preferred embodiments, the comparison step c) consists of comparing the expression value of a plurality of marker genes that are known to be deregulated in patients affected with an advanced heart failure, with the reference expression values for each of the said marker genes that are previously determined in patients that are not affected with an advanced heart failure, including healthy individuals. The collection of expression values for the tested marker genes may also be termed an "expression profile" of these genes. In these preferred embodiments step c) encompasses comparing (i) the expression profile of the gene markers of interest that has been determined from the biological sample of the patient under testing with (ii) at least one reference expression profile of the same marker genes that has been previously determined from patients that are not affected with an advanced heart failure. In certain of these preferred embodiments step c) consists of comparing (i) the expression profile of the gene markers of interest that has been determined from the biological sample of the patient under testing with (ii) every reference expression profile of the same marker genes that has been previously determined from patients that are not affected with an advanced heart failure, which may also include healthy individuals.
In certain embodiments of the method, the expression level value of a heart-specific marker gene can be provided as a relative expression level value. To determine a relative expression level value of a heart-specific marker gene, the level of expression of the said marker gene is previously determined for 10 or more samples of cardiac tissue originating from patients who are not affected with an advanced heart failure, prior to the determination of the expression level for the sample in question. The median expression level of the said heart- specific marker gene, which has been determined in the larger number of samples, is determined and this median expression value is used as a baseline expression level, that may also be termed "control" value for the said heart-specific marker gene. The expression level of the said heart-specific marker gene determined for the test sample (absolute level of expression) is then divided by the median expression value obtained for that marker. This provides a relative expression level.
For a given heart-specific marker gene whose expression level is quantified at step b), a deregulated expression level is determined at step c) if there exists a relevant difference between (i) the expression value obtained at step b) and (ii) the reference control value. The said relevant difference may be any expression level difference that is found statistically significant, irrespective of the statistical method that is used. Generally, a relevant difference is found if, either :
(i) the deregulated expression of the heart-specific marker gene consists of an under- expression, as compared to the control expression value, the deregulated expression value being 0.98 fold or less the control expression value; or
(ii) the deregulated expression of the heart-specific marker gene consists of an over- expression, as compared to the control expression value, the deregulated expression value being 1 .02 fold or more the control expression value.
Indeed, starting from the maker gene information that is contained in the present specification, the one skilled in the art may select any combination of more than one heart- specific marker genes for performing the in vitro diagnosis or prognosis method according to the invention.
Particularly, the one skilled in the art may define a useful combination of two or more heart-specific marker genes using the following general formula (I) :
Cb=M1.x1 + M2.x2 + M3.X3 + ... + Mz.xz,
wherein :
- Cb consists of a specific combination of heart-specific marker genes according to the invention,
- Mi, M2, M3 Mz each denotes a given heart-specific marker gene described in the instant specification and "z" denotes the maximum number of marker genes described herein, with z being presently equal to 225,
- xi, x2, x3 Xz consists of an integer equal to 0 or 1 , which denotes the status of the corresponding heart-specific marker gene in the combination, wherein (i) the said integer being equal to 0 if the corresponding marker gene is absent in the said combination or (ii) the said integer being equal to 1 if the corresponding marker gene is present in the said combination.
As disclosed in the examples herein, the in vitro diagnosis or prognosis method according to the invention is both highly sensitive and highly reproducible.
Illustratively, for diagnosis or prognosis of an advanced heart failure, especially for prognosis of a Stable status or a Deteriorating status, a 95% correct prediction is reached, based on a comparison with the actual clinical classification of the said patients.
Further, diagnosis or prognosis of an advanced heart failure involving a dysfunction of the right ventricle, 100% correct prediction is reached, based on a comparison with the actual clinical classification of the said patients.
Still further, it is shown in the examples herein that for prediction of a Stable status, sensitivity reaches 90% and specificity is of 100%.
Yet further, it is also shown in the examples herein that for prediction of a Deteriorating status, sensitivity reaches 87% and specificity is of 100%. It is also shown in the examples herein that the proportion of patient's samples predicted in concordance with the actual clinical classification is identical for left ventricle samples and right ventricle samples.
It is further shown in the examples herein that patients finally classified in-between the Stable status and the Deteriorating status when performing the in vitro diagnosis or prognosis method according to the invention are those who were actually clinically classified in the Intermediate group.
Also; it is shown in the examples herein that a similar increase of the Molecular Severity Score (MSS) value was obtained for a progression of clinical severity, both for left ventricle samples and for right ventricle samples.
Finally, it has also been shown herein that performing the in vitro diagnosis or prognosis method according to the invention on cardiac tissue samples originating from patients that are under medical treatment, e.g. with adrenergic agonists, phospho-diesterase inhibitors, beta- blockers or angiotensin converting enzyme inhibitors/angiotensin receptor blockers, does not significantly influence the classification obtained at step d) of the method.
General methods for quantifying the expression level of disease-specific marker genes
Any one of the methods known by the one skilled in the art for quantifying a nucleic acid biological marker encompassed herein may be used for performing the in vitro prediction or diagnosis method of the invention. Thus any one of the standard and non-standard (emerging) techniques well known in the art for detecting and quantifying a protein or a nucleic acid in a sample can readily be applied.
Such techniques include detection and quantification of nucleic acid biological markers with nucleic probes or primers.
The expression level of a heart-specific marker gene described herein may be quantified by any one of a wide variety of well known methods for detecting expression of a transcribed nucleic acid. Non-limiting examples of such methods include nucleic acid hybridisation methods, nucleic acid reverse transcription methods, and nucleic acid amplification methods.
In certain embodiments, the expression level of a heart-specific marker gene is assessed by preparing mRNA cDNA (i.e. a transcribed polynucleotide) from cells originating from a cardiac tissue sample of a patient to be tested, and by hybridising the mRNA cDNA with a reference polynucleotide which is a complement of a marker nucleic acid, or a fragment thereof, the said marker nucleic acid being comprised in the expression product of a heart- specific marker gene described herein. cDNA can, optionally, be amplified using any of a variety of polymerase chain reaction methods prior to hybridisation with the reference polynucleotide.
In some preferred embodiments of the in vitro prediction or diagnosis method according to the invention, step b) of expression level quantification of two or more heart-specific marker genes is performed using DNA microarrays. Illustratively, according to this preferred embodiment, a mixture of transcribed polynucleotides obtained from the cardiac tissue sample, or alternatively a mixture of the corresponding cDNAs, is contacted with a substrate having fixed thereto a plurality of polynucleotides, each of these polynucleotides consisting of a polynucleotide complementary to, or homologous with, at least a portion (e.g. at least 7, 10, 15, 20, 25, 30, 40, 50, 100, 500, or more consecutive nucleotide residues) of a disease-specific marker gene. If polynucleotides complementary to or homologous with are differentially detectable on the substrate (e.g. detectable using different chromophores or fluorophores, or fixed to different selected positions), then the levels of expression of a plurality of heart-specific marker genes can be quantified simultaneously using a single substrate (e.g. a "gene chip" microarray of polynucleotides fixed at selected positions). When a method of assessing marker expression is used which involves hybridisation of one nucleic acid with another, it is preferred that the hybridisation be performed under stringent hybridisation conditions.
In certain embodiments of the in vitro prediction or diagnosis method according to the invention, step b) comprises the steps of :
b1 ) providing two or more sets of nucleic acids, each nucleic acid contained in a set hybridizing specifically with a nucleic acid expression product of a heart-specific marker gene described in the present specification;
b2) reacting the sets of nucleic acids provided at step b1 ) with nucleic acid expression products that are previously extracted from the cardiac tissue sample provided at step a); b3) detecting and quantifying the nucleic acid complexes formed between (i) the sets of nucleic acids provided at step b1 ) and (ii) the nucleic acid expression products that are extracted from the cardiac tissue sample provided at step a);
The nucleic acids that are provided at step b1 ) may also be conventionally termed nucleic acid probes, each nucleic acid probe having the ability to specifically hybridize with an expression product (mRNA or cDNA) from a heart-specific marker gene selected from the group consisting of the heart-specific marker genes described in the present specification.
A "set" of nucleic acids that is provided at step b1 ) consists of one or more nucleic acids
(e.g. nucleic acid probes) that all hybridize with an expression product from the same heart- specific marker gene. In the embodiments wherein a set of nucleic acids comprises two or more nucleic acids, the said nucleic acids may be identical or distinct. In the embodiments wherein a set of nucleic acids comprises two or more distinct nucleic acids, the said distinct nucleic acids preferably hybridize with distinct nucleic acid portions, most preferably non-overlapping portions, of the expression product of the same heart-specific marker gene.
As it will be described in detail below, preferred embodiments of steps b1 ) to b3) are performed with DNA microarrays.
Thus, in most preferred embodiments of step b) of the in vitro prediction or diagnosis method according to the invention, the expression level quantification of the heart-specific marker genes is performed by using suitable DNA microarrays. In such a marker detection/quantification format, the mRNA is immobilised on a solid surface and contacted with a probe, for example by running the isolated mRNA on an agarose gel and transferring the mRNA from the gel to a membrane, such as nitrocellulose. In an alternative format, the probe(s) are immobilized on a solid surface and the mRNA is contacted with the probe(s), for example, in an Affymetrix gene chip array. A skilled artisan can readily adapt known mRNA detection methods for use in detecting the level of mRNA encoded by the markers of the present invention. Specific hybridization technology which may be practiced to generate the expression profiles employed in the subject methods includes the technology described in U.S. Pat. Nos. 5,143,854; 5,288,644; 5,324,633; 5,432,049; 5,470,710; 5,492,806; 5,503,980; 5,510,270; 5,525,464; 5,547,839; 5,580,732; 5,661 ,028; 5,800,992; the disclosures of which are herein incorporated by reference; as well as WO 95/21265; WO 96/31622; WO 97/10365; WO 97/27317; EP 373 203; and EP 785 280. In these methods, an array of "probe" nucleic acids that includes a probe for each of the phenotype determinative genes whose expression is being assayed is contacted with target nucleic acids as described above. Contact is carried out under hybridization conditions, e.g., stringent hybridization conditions as described above, and unbound nucleic acid is then removed. The resultant pattern of hybridized nucleic acid provides information regarding expression for each of the genes that have been probed, where the expression information is in terms of whether or not the gene is expressed and, typically, at what level, where the expression data, i.e., expression profile, is both qualitative and quantitative.
Suitable carriers or solid phase supports for such assays include any material capable of binding the class of molecule to which the marker or probe belongs. Well-known supports or carriers include, but are not limited to, glass, polystyrene, nylon, polypropylene, nylon, polyethylene, dextran, amylases, natural and modified celluloses, polyacrylamides, gabbros, and magnetite.
In order to conduct assays with the above mentioned approaches, the non-immobilised component is added to the solid phase upon which the second component is anchored. After the reaction is complete, uncomplexed components may be removed (e.g., by washing) under conditions such that any complexes formed will remain immobilised upon the solid phase. The detection of marker/probe complexes anchored to the solid phase can be accomplished in a number of methods outlined herein.
In a preferred embodiment, the probe, when it is the unanchored assay component, can be labelled for the purpose of detection and readout of the assay, either directly or indirectly, with detectable labels discussed herein and which are well-known to one skilled in the art.
In another embodiment, determination of the ability of a probe to recognise a marker can be accomplished without labelling either assay component (probe or marker) by utilising a technology such as real-time Biomolecular Interaction Analysis (BIA) (see, e.g., Sjolander, S. and Urbaniczky, C, 1991 , Anal. Chem. 63:2338-2345 and Szabo et al., 1995, Curr. Opin. Struct. Biol. 5:699-705). As used herein, "BIA" or "surface plasmon resonance" is a technology for studying biospecific interactions in real time, without labelling any of the interactants (e.g., BIAcore). Changes in the mass at the binding surface (indicative of a binding event) result in alterations of the refractive index of light near the surface (the optical phenomenon of surface plasmon resonance (SPR)), resulting in a detectable signal which can be used as an indication of real-time reactions between biological molecules. Alternatively, in another embodiment, analogous diagnostic and prognostic assays can be conducted with marker and probe as solutes in a liquid phase. In such an assay, the complexed marker and probe are separated from uncomplexed components by any of a number of standard techniques, including but not limited to: differential centrifugation, chromatography, electrophoresis and immunoprecipitation. In differential centrifugation, marker/probe complexes may be separated from uncomplexed assay components through a series of centrifugal steps, due to the different sedimentation equilibria of complexes based on their different sizes and densities (see, for example, Rivas, G., and Minton, A. P., 1993, Trends Biochem Sci. 18(8):284-7). Standard chromatographic techniques may also be utilized to separate complexed molecules from uncomplexed ones. For example, gel filtration chromatography separates molecules based on size, and through the utilization of an appropriate gel filtration resin in a column format, for example, the relatively larger complex may be separated from the relatively smaller uncomplexed components. Similarly, the relatively different charge properties of the marker/probe complex as compared to the uncomplexed components may be exploited to differentiate the complex from uncomplexed components, for example through the utilization of ion-exchange chromatography resins. Such resins and chromatographic techniques are well known to one skilled in the art (see, e.g., Heegaard, N. H., 1998, J. Mol. Recognit. Winter 1 1 (1 -6):141 -8; Hage, D. S., and Tweed, S. A. J Chromatogr B Biomed Sci Appl 1997 Oct. 10;699(1 -2):499-525). Gel electrophoresis may also be employed to separate complexed assay components from unbound components (see, e.g., Ausubel et al., ed., Current Protocols in Molecular Biology, John Wiley & Sons, New York, 1987-1999). In this technique, protein or nucleic acid complexes are separated based on size or charge, for example. In order to maintain the binding interaction during the electrophoretic process, non- denaturing gel matrix materials and conditions in the absence of reducing agent are typically preferred. SELDI-TOF technique may also be employed on matrix or beads coupled with active surface, or not, or antibody coated surface, or beads.
Appropriate conditions to the particular assay and components thereof will be well known to one skilled in the art.
As already mentioned above, preferred expression quantification methods use isolated RNA. For in vitro methods, any RNA isolation technique that does not select against the isolation of mRNA can be utilised for the purification of RNA from cardiac tissue sample (see, e.g., Ausubel et al., ed., Current Protocols in Molecular Biology, John Wiley & Sons, New York 1987-1999). Additionally, large numbers of tissue samples can readily be processed using techniques well known to those of skill in the art, such as, for example, the single-step RNA isolation process of Chomczynski (1989, U.S. Pat. No. 4,843,155).
An alternative method for determining the level of mRNA marker in a sample involves the process of nucleic acid amplification, e.g., by rtPCR (the experimental embodiment set forth in Mullis, 1987, U.S. Pat. No. 4,683,202), ligase chain reaction (Barany, 1991 , Proc. Natl. Acad. Sci. USA, 88:189-193), self sustained sequence replication (Guatelli et al., 1990, Proc. Natl. Acad. Sci. USA 87:1874-1878), transcriptional amplification system (Kwoh et al., 1989, Proc. Natl. Acad. Sci. USA 86:1 173-1 177), Q-Beta Replicase (Lizardi et al., 1988, Bio/Technology 6:1 197), rolling circle replication (Lizardi et al., U.S. Pat. No. 5,854,033) or any other nucleic acid amplification method, followed by the detection of the amplified molecules using techniques well known to those of skill in the art. These detection schemes are especially useful for the detection of nucleic acid molecules if such molecules are present in very low numbers. As used herein, amplification primers are defined as being a pair of nucleic acid molecules that can anneal to 5' or 3' regions of a gene (plus and minus strands, respectively, or vice-versa) and contain a short region in between. In general, amplification primers are from about 10 to 30 nucleotides in length and flank a region from about 50 to 200 nucleotides in length. Under appropriate conditions and with appropriate reagents, such primers permit the amplification of a nucleic acid molecule comprising the nucleotide sequence flanked by the primers.
As an alternative to making determinations based on the absolute expression level of the marker, determinations may be based on the normalised expression level of the marker. Expression levels are normalised by correcting the absolute expression level of a marker by comparing its expression to the expression of a gene that is not a marker, e.g., a housekeeping gene that is constitutively expressed. Suitable genes for normalisation include housekeeping genes such as the actin gene and the ribosomal 18S gene. This normalisation allows the comparison of the expression level of one or more tissue-specific biological marker of interest in one sample.
The most preferred methods for quantifying a biological marker for the purpose of carrying out the advanced heart failure diagnosis or prediction method of the invention are described hereunder.
Quantifying the expression level of disease-specific marker genes by cDNA microarrays According to this embodiment, a microarray may be constructed based on the disease- specific marker genes that are disclosed throughout the present specification. Preferably, oligonucleotide probes that specifically hybridize with the expression products (mRNA or cDNA) from each of the heart-specific marker genes tested are immobilized on a solid support, most preferably on an ordered arrangement, so as to manufacture the DNA microarray. These marker gene-specific detection probes should be designed and used in conditions such that only nucleic acids having a heart-specific marker gene sequence may hybridize and give a positive result.
Most existing microarrays, such as those provided by Affymetrix (California), may be used with the present invention.
One of skill in the art will appreciate that an enormous number of array designs are suitable. The high density array will typically include a number of probes that specifically hybridize to the sequences of interest. See WO 99/32660 for methods of producing probes for a given gene or genes. In a preferred embodiment, the array will include one or more control probes. Nucleic acid probes immobilized on the microarrav devices
High density array chips include « test probes » that specifically hybridize with mRNAs or cDNAs consisting of the products of expression of the heart-specific biological markers that are described herein.
Test probes may be oligonucleotides that range from about 5 to about 500 or about 5 to about 50 nucleotides, more preferably from about 10 to about 40 nucleotides and most preferably from about 15 to about 40 nucleotides in length. In other particularly preferred embodiments, the probes are about 20 or 25 nucleotides in length. In another preferred embodiment, test probes are double or single strand DNA sequences. DNA sequences may be isolated or cloned from natural sources or amplified from natural sources using natural nucleic acid as templates. These probes have sequences complementary to particular subsequences of the heart-specific markers whose expression they are designed to detect.
In addition to test probes that bind the target nucleic acid(s) of interest, the high density array can contain a number of control probes. The control probes fall into three categories referred to herein as normalization controls; expression level controls; and mismatch controls. Normalization controls are oligonucleotide or other nucleic acid probes that are complementary to labeled reference oligonucleotides or other nucleic acid sequences that are added to the nucleic acid sample. The signals obtained from the normalization controls after hybridization provide a control for variations in hybridization conditions, label intensity, "reading" efficiency and other factors that may cause the signal of a perfect hybridization to vary between arrays. In a preferred embodiment, signals (e.g. fluorescence intensity) read from all other probes in the array are divided by the signal (, fluorescence intensity) from the control probes thereby normalizing the measurements. Virtually any probe may serve as a normalization control. However, it is recognized that hybridization efficiency varies with base composition and probe length. Preferred normalization probes are selected to reflect the average length of the other probes present in the array; however, they can be selected to cover a range of lengths. The normalization control(s) can also be selected to reflect the (average) base composition of the other probes in the array, however in a preferred embodiment, only one or a few probes are used and they are selected such that they hybridize well (i.e., no secondary structure) and do not match any target-specific probes. Expression level controls are probes that hybridize specifically with constitutively expressed genes in the biological sample. Virtually any constitutively expressed gene provides a suitable target for expression level controls. Typical expression level control probes have sequences complementary to subsequences of constitutively expressed "housekeeping genes" including the .beta.-actin gene, the transferrin receptor gene, and the GAPDH gene. Mismatch controls may also be provided for the probes to the target genes, for expression level controls or for normalization controls. Mismatch controls are oligonucleotide probes or other nucleic acid probes identical to their corresponding test or control probes except for the presence of one or more mismatched bases. A mismatched base is a base selected so that it is not complementary to the corresponding base in the target sequence to which the probe would otherwise specifically hybridize. One or more mismatches are selected such that under appropriate hybridization conditions (e.g., stringent conditions) the test or control probe would be expected to hybridize with its target sequence, but the mismatch probe would not hybridize (or would hybridize to a significantly lesser extent). Preferred mismatch probes contain a central mismatch. Thus, for example, where a probe is a twenty- mer, a corresponding mismatch probe may have the identical sequence except for a single base mismatch (e.g., substituting a G, a C or a T for an A) at any of positions 6 through 14 (the central mismatch). Mismatch probes thus provide a control for non-specific binding or cross hybridization to a nucleic acid in the sample other than the target to which the probe is directed. Mismatch probes also indicate whether hybridization is specific or not.
Solid Supports for DNA microarravs
Solid supports containing oligonucleotide probes for differentially expressed genes can be any solid or semisolid support material known to those skilled in the art. Suitable examples include, but are not limited to, membranes, filters, tissue culture dishes, polyvinyl chloride dishes, beads, test strips, silicon or glass based chips and the like. Suitable glass wafers and hybridization methods are widely available. Any solid surface to which oligonucleotides can be bound, either directly or indirectly, either covalently or non-covalently, can be used. In some embodiments, it may be desirable to attach some oligonucleotides covalently and others non- covalently to the same solid support. A preferred solid support is a high density array or DNA chip. These contain a particular oligonucleotide probe in a predetermined location on the array. Each predetermined location may contain more than one molecule of the probe, but each molecule within the predetermined location has an identical sequence. Such predetermined locations are termed features. There may be, for example, from 2, 10, 100, 1000 to 10,000, 100,000 or 400,000 of such features on a single solid support. The solid support or the area within which the probes are attached may be on the order of a square centimeter. Methods of forming high density arrays of oligonucleotides with a minimal number of synthetic steps are known. The oligonucleotide analogue array can be synthesized on a solid substrate by a variety of methods, including, but not limited to, light-directed chemical coupling, and mechanically directed coupling (see U.S. Pat. No. 5,143,854 to Pirrung et al.; U.S. Pat. No. 5,800,992 to Fodor et al.; U.S. Pat. No. 5,837,832 to Chee et al.
In brief, the light-directed combinatorial synthesis of oligonucleotide arrays on a glass surface proceeds using automated phosphoramidite chemistry and chip masking techniques. In one specific implementation, a glass surface is derivatized with a silane reagent containing a functional group, e.g., a hydroxyl or amine group blocked by a photolabile protecting group. Photolysis through a photolithographic mask is used selectively to expose functional groups which are then ready to react with incoming 5' photoprotected nucleoside phosphoramidites. The phosphoramidites react only with those sites which are illuminated (and thus exposed by removal of the photolabile blocking group). Thus, the phosphoramidites only add to those areas selectively exposed from the preceding step. These steps are repeated until the desired array of sequences has been synthesized on the solid surface. Combinatorial synthesis of different oligonucleotide analogues at different locations on the array is determined by the pattern of illumination during synthesis and the order of addition of coupling reagents.
In addition to the foregoing, methods which can be used to generate an array of oligonucleotides on a single substrate are described in WO 93/09668 to Fodor et al. High density nucleic acid arrays can also be fabricated by depositing premade or natural nucleic acids in predetermined positions. Synthesized or natural nucleic acids are deposited on specific locations of a substrate by light directed targeting and oligonucleotide directed targeting. Another embodiment uses a dispenser that moves from region to region to deposit nucleic acids in specific spots.
Oligonucleotide probe arrays for expression monitoring can be made and used according to any techniques known in the art (see for example, Lockhart et al., Nat. Biotechnol. 14, 1675-1680 (1996); McGall et al., Proc. Nat. Acad. Sci. USA 93, 13555-13460 (1996). Such probe arrays may contain at least two or more oligonucleotides that are complementary to or hybridize to two or more of the genes described herein. Such arrays may also contain oligonucleotides that are complementary to or hybridize to at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40,41 , 42, 43, 44, 45, 46, 47, 48, 49, 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, 100, 101 , 102, 103, 104, 105, 106, 107, 108, 109, 1 10, 1 1 1 , 1 12, 1 13, 1 14, 1 15, 1 16, 1 17, 1 18, 1 19, 120, 121 , 122, 123, 124, 125, 126, 127, 128, 129, 130, 131 , 132, 133, 134, 135, 136, 137, 138, 139, 140, 141 , 142, 143, 144, 145, 146, 147, 148, 149, 150, 151 , 152, 153, 154, 155, 156, 157, 158, 159, 160, 161 , 162, 163, 164, 165, 166, 167, 168, 169, 170, 171 , 172, 173, 174, 175, 176, 177, 178, 179, 180, 181 , 182, 183, 184, 185, 186, 187, 188, 189, 190, 191 , 192, 193, 194, 195, 196, 197, 198, 199; 200, 201 , 202, 203, 204, 205, 206, 207, 208, 209, 210, 21 1 , 212, 213, 214, 215, 216, 217, 218, 219, 220, 221 , 222, 223, 224 or 225 heart-specific marker genes described therein.
Hybridization
Nucleic acid hybridization simply involves contacting a probe and target nucleic acid under conditions where the probe and its complementary target can form stable hybrid duplexes through complementary base pairing (see WO 99/32660 to Lockhart). The nucleic acids that do not form hybrid duplexes are then washed away leaving the hybridized nucleic acids to be detected, typically through detection of an attached detectable label. It is generally recognized that nucleic acids are denatured by increasing the temperature or decreasing the salt concentration of the buffer containing the nucleic acids. Under low stringency conditions (e.g., low temperature and/or high salt) hybrid duplexes (e.g., DNA-DNA, RNA-RNA or RNA-DNA) will form even where the annealed sequences are not perfectly complementary. Thus, specificity of hybridization is reduced at lower stringency. Conversely, at higher stringency (e.g., higher temperature or lower salt) successful hybridization requires fewer mismatches. One of skill in the art will appreciate that hybridization conditions may be selected to provide any degree of stringency. In a preferred embodiment, hybridization is performed at low stringency, in this case in 6.times.SSPE-T at 37.degree. C. (0.005% Triton x-100) to ensure hybridization and then subsequent washes are performed at higher stringency (e.g., 1 .times. SSPE-T at 37.degree. C.) to eliminate mismatched hybrid duplexes. Successive washes may be performed at increasingly higher stringency (e.g. down to as low as 0.25.times.SSPE-T at 37.degree. C. to 50.degree. C. until a desired level of hybridization specificity is obtained. Stringency can also be increased by addition of agents such as formamide. Hybridization specificity may be evaluated by comparison of hybridization to the test probes with hybridization to the various controls that can be present (e.g., expression level controls, normalization controls, mismatch controls, etc.).
In general, there is a tradeoff between hybridization specificity (stringency) and signal intensity. Thus, in a preferred embodiment, the wash is performed at the highest stringency that produces consistent results and that provides a signal intensity greater than approximately 10% of the background intensity. The hybridized array may be washed at successively higher stringency solutions and read between each wash. Analysis of the data sets thus produced will reveal a wash stringency above which the hybridization pattern is not appreciably altered and which provides adequate signal for the particular oligonucleotide probes of interest.
Signal Detection
The hybridized nucleic acids are typically detected by detecting one or more labels attached to the sample nucleic acids. The labels may be incorporated by any of a number of means well known to those of skill in the art (see WO 99/32660 to Lockhart). Any suitable methods can be used to detect one or more of the markers described herein. For example, gas phase ion spectrometry can be used. This technique includes, e.g., laser desorption/ionization mass spectrometry. In some embodiments, the sample can be prepared prior to gas phase ion spectrometry, e.g., pre-fractionation, two-dimensional gel chromatography, high performance liquid chromatography, etc. to assist detection of markers.
Quantifying the expression level of disease-specific marker genes by nucleic acid amplification
In certain embodiments, the expression level of a heart-specific marker gene, or of a set of heart-specific marker genes, may be quantified with any one of the nucleic acid amplification methods known in the art.
The polymerase chain reaction (PCR) is a highly sensitive-and powerful method for such biological markers quantification.
For performing any one of the nucleic acid amplification method that is appropriate for quantifying a biological marker when performing the in vitro prediction or diagnosis method of the invention, a pair of primers that specifically hybridise with the target mRNA or with the target cDNA is required.
A pair of primers that specifically hybridise with the target nucleic acid biological marker of interest may be designed by any one of the numerous methods known in the art. Illustratively, primers that specifically hybridize with a heart-specific marker gene described herein may be easily designed by the one skilled in the art, on the basis of the nucleic acid sequence of the said heart-specific marker gene, like it is found for example in the HGCN database.
In certain embodiments, for each of the heart-specific markers of the invention, at least one pair of specific primers, as well as the corresponding detection nucleic acid probe, is already referenced and entirely described in the public "Quantitative PCR primer database", notably at the following Internet address : http://lpgws.nci.nih.gov/cgi-bin/PrimerViewer.
In other embodiments, a specific pair of primers may be designed using the method disclosed in the US Patent n ° US 6,892,141 to Nakae et al., the entire disclosure of which is herein incorporated by reference.
Many specific adaptations of the PCR technique are known in the art for both qualitative and quantitative detection purposes. In particular, methods are known to utilise fluorescent dyes for detecting and quantifying amplified PCR products. In situ amplification and detection, also known as homogenous PCR, have also been previously described. See e.g. Higuchi et al., (Kinetics PCR Analysis: Real-time Monitoring of DNA Amplification Reactions, Bio/Technology, Vol 1 1 , pp 1026-1030 (1993)), Ishiguro et al., (Homogeneous quantitative Assay of Hepatitis C Virus RNA by Polymerase Chain Reaction in the Presence of a Fluorescent Intercalate^ Anal. Biochemistry 229, pp 20-213 (1995)), and Wittwer et al., (Continuous Fluorescence Monitoring of Rapid cycle DNA Amplification, Biotechniques, vol.22, pp 130-138 (1997.))
A number of other methods have also been developed to quantify nucleic acids
(Southern, E. M., J. Mol. Biol., 98:503-517, 1975; Sharp, P. A., et al., Methods Enzymol. 65:750-768, 1980; Thomas, P. S., Proc. Nat. Acad. Sci., 77:5201 -5205, 1980). More recently, PCR and RT-PCR methods have been developed which are capable of measuring the amount of a nucleic acid in a sample. One approach, for example, measures PCR product quantity in the log phase of the reaction before the formation of reaction products plateaus (Kellogg, D. E., et al., Anal. Biochem. 189:202-208 (1990); and Pang, S., et al., Nature 343:85-89 (1990)). A gene sequence contained in all samples at relatively constant quantity is typically utilised for sample amplification efficiency normalisation. This approach, however, suffers from several drawbacks. The method requires that each sample have equal input amounts of the nucleic acid and that the amplification efficiency between samples be identical until the time of analysis. Furthermore, it is difficult using the conventional methods of PCR quantitation such as gel electrophoresis or plate capture hybridisation to determine that all samples are in fact analysed during the log phase of the reaction as required by the method.
Another method called quantitative competitive (QC)-PCR, as the name implies, relies on the inclusion of an internal control competitor in each reaction (Becker-Andre, M., Meth. Mol. Cell Biol. 2:189-201 (1991 ); Piatak, M. J., et al., BioTechniques 14:70-81 (1993); and Piatak, M. J., et al., Science 259:1749-1754 (1993)). The efficiency of each reaction is normalised to the internal competitor. A known amount of internal competitor is typically added to each sample. The unknown target PCR product is compared with the known competitor PCR product to obtain relative quantitation. A difficulty with this general approach lies in developing an internal control that amplifies with the same efficiency of the target molecule.
For instance, the nucleic acid amplification method that is used may consist of Real- Time quantitative PCR analysis.
Real-time or quantitative PCR (QPCR) allows quantification of starting amounts of DNA, cDNA, or RNA templates. QPCR is based on the detection of a fluorescent reporter molecule that increases as PCR product accumulates with each cycle of amplification. Fluorescent reporter molecules include dyes that bind double-stranded DNA (i.e. SYBR Green I) or sequence-specific probes (i.e. Molecular Beacons or TaqMan® Probes).
Preferred nucleic acid amplification methods are quantitative PCR amplification methods, including multiplex quantitative PCR method such as the technique disclosed in the published US patent Application n ° US 2005/0089862, to Therianos et al., the entire disclosure of which is herein incorporated by reference.
Illustratively, for quantifying biological markers of the invention, tumor tissue samples are snap-frozen shortly after biopsy collection. Then, total RNA from a "cardiac tissue sample" is isolated and quantified. Then, each sample of the extracted and quantified RNA is reverse- transcribed and the resulting cDNA is amplified by PCR, using a pair of specific primers for each biological marker that is quantified. Control pair of primers are simultaneously used as controls, such as pair of primers that specifically hybridise with TBP cDNA, 18S cDNA and GADPH cDNA, or any other well known "housekeeping" gene.
Illustrative embodiments of quantification of the expression level of the disease-specific marker genes described herein are disclosed in the examples herein.
Kits for predicting or diagnosing the occurrence of a cardiac disease
The invention also relates to a kit for the in vitro prediction or diagnosis of the occurrence of an advanced heart failure in a patient (e.g. in a cardiac tissue sample previously collected from a patient to be tested). The kit comprises a plurality of reagents, each of which is capable of binding specifically with a nucleic acid that is comprised in an expression product (mRNA or cDNA) from a disease-specific marker gene selected from the heart-specific marker genes included in groups (i) to (xi) described herein.
Suitable reagents for binding with a marker nucleic acid (e.g. a mRNA, a spliced mRNA, a cDNA, or the like) include complementary nucleic acids. For example, the nucleic acid reagents may include oligonucleotides (labelled or non-labelled) fixed to a substrate, labelled oligonucleotides not bound with a substrate, pairs of PCR primers, molecular beacon probes, and the like.
Another object of the present invention consists of a kit for the in vitro prediction or diagnosis of the occurrence of an advanced heart failure in a patient, which kit comprises means for quantifying the expression level of two or more heart-specific marker genes that are indicative of the risk of occurrence of, or of the occurrence of, an advanced heart failure disease. The present invention also encompasses various alternative embodiments of the said prediction or diagnosis kit, wherein the said kit comprises combination of marker quantification means, for quantifying the expression level of various combinations of the disease-specific marker genes that are described in the present specification.
Most preferably, a prediction or diagnosis kit according to the invention consists of a
DNA microarray comprising probes hybridizing to the nucleic acid expression products (mRNAs or cDNAs) of the heart-specific gene markers described herein.
This invention also pertains to a collection of nucleic acids that is useful for predicting or diagnosing the occurrence of an advanced heart failure in a patient, wherein the said collection of nucleic acids comprises a combination of at least two distinct nucleic acids, each distinct nucleic acid hybridizing specifically with a heart-specific marker gene described herein.
In certain embodiments of the said collection of nucleic acids, each of the said nucleic acids consists of a nucleic acid probe that specifically hybridises with an expression product of a given heart-specific gene.
In certain other embodiments of the said collection of nucleic acids, each of the said nucleic acid is selected from the group of nucleic acids consisting of SEQ ID Ν to SEQ ID N ° 225 that are described elsewhere in the present specification.
In preferred embodiments, a kit according to the invention comprises (i) a combination or a set of specific nucleic acid probes or (ii) a combination or a set of nucleic acid primers, each kind of probes of primers hybridising specifically with the expression product (mRNA or cDNA) of a heart-specific marker gene selected from the heart-specific marker genes described herein.
In certain embodiments wherein nucleic acid probes are used, including when these probes are immobilised in an arrayed ordering on a solid support.
In other embodiments wherein nucleic acid probes are used, specifically dedicated DNA microarrays may be manufactured by immobilising on a solid support the suitable set of gene- specific probes, the said gene-specific probes being nucleic acid fragments comprising 12 or more consecutive nucleotides of the corresponding gene-specific mRNA or cDNA.
In other embodiments, the said kit comprises a combination or a set of pair of primers comprising at least two kind of pair of primers, each kind of pair of primers being selected from the group consisting of pair of primers hybridising with each of the selected heart-specific marker genes among those disclosed in the present specification.
A primer kit according to the invention may comprise 2 or more kinds of pair or primers, each kind of pair of primers hybridising specifically with one heart-specific marker gene of the invention. For instance, a primer kit according to the invention may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40,41 , 42, 43, 44, 45, 46, 47, 48, 49, 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, 100, 101 , 102, 103, 104, 105, 106, 107, 108, 109, 1 10, 1 1 1 , 1 12, 1 13, 1 14, 1 15, 1 16, 1 17, 1 18, 1 19, 120, 121 , 122, 123, 124, 125, 126, 127, 128, 129, 130, 131 , 132, 133, 134, 135, 136, 137, 138, 139, 140, 141 , 142, 143, 144, 145, 146, 147, 148, 149, 150, 151 , 152, 153, 154, 155, 156, 157, 158, 159, 160, 161 , 162, 163, 164, 165, 166, 167, 168, 169, 170, 171 , 172, 173, 174, 175, 176, 177, 178, 179, 180, 181 , 182, 183, 184, 185, 186, 187, 188, 189, 190, 191 , 192, 193, 194, 195, 196, 197, 198, 199; 200, 201 , 202, 203, 204, 205, 206, 207, 208, 209, 210, 21 1 , 212, 213, 214, 215, 216, 217, 218, 219, 220, 221 , 222, 223, 224 or 225 or more kinds of pairs of primers, each kind of pair of primers hybridising specifically with a single heart-specific marker gene as disclosed herein.
Notably, at least one pair of specific primers, as well as the corresponding detection nucleic acid probe, that hybridise specifically with one disease-specific marker gene of interest, is already referenced and entirely described in the public "Quantitative PCR primer database", notably at the following Internet address : http://lpgws.nci.nih.gov/cgi-bin/PrimerViewer.
Illustrative nucleic acids that are directly usable as primers, or alternatively that are usable for designing primers, consist of the nucleic acids of SEQ ID N ° 1 to SEQ ID N ° 225 that are disclosed herein. Regarding SEQ ID N ° 12, 40, 81 , 1 13, 128, 130, 135, 154, 194, 214 and 216 longer nucleic acid sequences are provided that are usable by the one skilled in the art for designing the appropriate corresponding nucleic acid primers.
Methods for selecting biological markers indicative of an advanced heart failure
This invention also pertains to methods for selecting one or more heart-specific marker genes that are indicative of the occurrence of, or of the risk of occurrence of, an advanced heart failure in a patient.
The said heart-specific marker gene selection method according to the invention preferably comprises the steps of :
a) providing means for quantifying one or more candidate marker genes in a cardiac tissue sample;
b) providing a plurality, or a collection, of cardiac tissue samples originating from patients affected with an advanced heart failure,
c) quantifying the expression level of each of the one or more candidate marker genes, optionally separately in every left ventricle sample and every right ventricle sample contained in the plurality or in the collection of cardiac tissue samples;
d) selecting, in the group of candidate marker genes whose expression level is quantified at step c), those marker genes that are under-expressed or over-expressed in tissue samples provided at step b), whereby a set of marker genes is selected, the said set of marker genes comprising heart-specific marker genes, the under-expression of which, or the over- expression of which, is indicative of the occurrence of, or of the risk of occurrence of, an advanced heart failure in the said patient.
Illustrative embodiments of the selection method above are fully described in the examples herein.
For performing step a) of the selection method above, the marker quantification means encompass means for quantifying marker gene-specific nucleic acids, such as oligonucleotide primers or probes. Illustratively, DNA microarrays may be used at step a) of the selection method above.
Means for specifically quantifying any one of the known potential marker gene, e.g. any gene-specific nucleic acid, may be provided at step a) of the selection method.
Preferably, the plurality or the collection of cardiac tissue samples comprises samples originating from at least 5 distinct individuals affected with an advanced heart failure, and most preferably at least 20, 25 or 30 distinct individuals affected with an advanced heart failure. The statistical relevance of the heart-specific marker genes that are finally selected at the end of the selection method generally increases with the number of distinct individuals tested, and thus with the number of cardiac tissue samples comprised in the plurality of tissue samples or in the collection of tissue samples that is provided at step b).
At step c), quantification of the candidate marker genes on the cardiac tissue samples provided at step b), using the quantification means provided at step a), may be performed according to any one of the quantification methods that are described elsewhere in the present specification.
At step d), each candidate marker gene quantified at step c) in the collection of cardiac tissue samples, is compared to the quantification results found for the same marker in tissue samples originating from individuals not affected with an advanced heart disease. Then, only those candidate marker genes that are differentially expressed (i.e. (i) under-expressed, (ii) not expressed, (iii) over-expressed in the said collection of cardiac tissue samples, as compared to the tissue samples originating form non-affected individuals, are positively selected as heart- specific marker genes indicative of the occurrence of an advanced heart failure. At step d), the selection of statistically relevant heart-specific marker genes, by comparing the expression level of a candidate marker gene in one collection of cardiac tissue samples with the expression level of the said candidate marker gene in cardiac tissue samples originating from non-affected individuals, is termed a "One Versus AH" ("OVA") pairwise comparison.
The statistical relevance of each candidate marker gene tested, at step d), may be performed by calculating the P value for the said marker, for example using a univariate t-test. Generally, a marker is selected at step d) of the selection method above, when its P value is lower than 0.01 .
The statistical relevance of the marker selection, at step d) of the method, may be further increased by using other statistical methods, wherein the said other statistical methods may consist of performing a multivariate permutation test, so as to provide 90% confidence that a false marker selection rate is less than 10%.
For further increasing the statistical relevance of the markers initially selected, at step d) of the selection method above, those markers that were initially selected as described above may be submitted to a further cycle of selection, for example by assaying the initially selected markers on further collections of cardiac tissue samples. This further cycle of selection may consist of, for example, performing a further expression analysis of the initially selected markers, for example by technique of quantitative RT-PCR expression analysis or by using DNA microarrays.
According to such a quantitative expression analysis, the quantification measure of expression of each initially selected marker may be normalised against a control value, e.g. the quantification measure of expression of a control gene such as TBP. The results may be expressed as N-fold difference of each marker relative to the value in normal cardiac tissues or to the value in all other cardiac tissues (normal and disease). Statistical relevance of each initially selected marker is then confirmed, for example at confidence levels of more than 95% (P of less than 0.05) using the Mann-Whitney U Test.
The present invention is further illustrated by, without in any way being limited to, the examples below.
Examples
A. Materials and Methods
A.1 . Cardiac samples
Cardiac tissue was obtained from explanted hearts of 44 patients with advanced HF who underwent a cardiac transplantation or a total artificial heart placement at the Nantes University Hospital between 1998 and 2002. Pre-transplant evaluation, including coronary artery angiography, cardiac catheterization, and echocardiography, confirmed the diagnosis, etiology and severity of the disease. At the time of transplantation, patient evaluation included physical examination, laboratory tests, and echocardiography. Macroscopic and histological examination of the explanted hearts confirmed the previously diagnosed etiology for all patients. Extensive individual clinical information can be found in Table 6.
Table 6. Clinical characteristics of advanced HF patients : UNOS: United Network for Organ Sharing; ADHF episode: Acute Decompensated Heart Failure episode; HF: Heart failure; HR: Heart Rate; SAP: Systolic Arterial Pressure; LVEF: Left Ventricle Ejection Fraction; LVEDD: Left Ventricle End-Diastolic Diameter; BNP: Brain Natriuretic Peptide; BUN: blood urea nitrogen; ACEI: Angiotensin Converting Enzyme Inhibitors; ARB: Angiotensin Receptor Blockers; PDE Inhibitors: Phosphodiesterase Inhibitors; DCM: Dilated Cardiomyopathy; CAD: Coronary Artery Disease; CHD: Congenital Heart Disease; RCM: Restrictive Cardiomyopathy; VHD: Valvular Heart Disease; HCM: Hypertrophic Cardiomyopathy. UNOS status corresponds to the medical urgency status as defined. An ADHF episode was defined as recent if it occurred during the 3 months preceding the heart transplantation/total artificial heart placement. Heart failure duration was defined as the delay between onset of heart failure symptoms and heart transplantation/total artificial heart placement. Values for HR, SAP, LVEF, LVEDD, BUN, and serum creatinine corresponded to pre-operative measurements. Values for BNP were obtained within the 2 months before heart transplantation. All patients were treated with loop diuretics (furosemide and/or bumetanide). Only medications related to HF therapy are presented.
Patients were classified into three severity groups based on their clinical status at the time of transplantation. The individual clinical status was defined based on the United Network for Organ Sharing (UNOS) medical urgency status (Renlund et al., J Heart Lung Transplant. 1999; 18: 1065-70) and occurrence of hospitalizations for Acute Decompensated Heart Failure (ADHF) during the three months prior to the surgical procedure ('recent ADHF'). Deteriorating patients were characterized by UNOS-1 A status. Stable patients were defined as UNOS-2 patients with no recent ADHF. The remaining patients were classified as Intermediate (UNOS- 1 B status or UNOS-2 status with recent ADHF).
For each of the 44 explanted hearts, two spatially distinct transmural samples were obtained from both left ventricle (LV) and right ventricle (RV) immediately after cardiac explanation, leading to a total of 176 distinct tissue samples. The samples were taken from non-infarcted zones of the ventricular free walls, snap-frozen in liquid nitrogen, and stored at - 80 °C. Myocardial tissue obtained from cardiac transplantation procedures is considered discarded tissue. Therefore tissue collection was performed without written informed consent.
Microarravs
Microarray preparation and hybridization, and expression data acquisition and processing and data analysis are described in details below.
Data analysis
Unsupervised hierarchical clustering was applied to the entire data set median-centered on genes, using the Pearson correlation as a similarity metric and average linkage clustering. Results were displayed using TreeView (Eisen et al.,. Proc Natl Acad Sci U S A. 1998; 95:
14863-8). Gene clusters were selected using 10 and 0.6 as minimal gene number and minimal correlation respectively. GoMiner was used to identify functional categories that were over- or underrepresented in specific clusters compared to the list of all analyzed genes (Zeeberg et al.,. Genome Biol. 2003; 4: R28).
'Predictors' and 'molecular severity score'
LV- and RV-specific data were separated into distinct datasets and analyzed separately using an identical strategy:
The 'Predictor' was defined as a list of genes differentially expressed between Stable and Deteriorating patient groups. These genes were identified using 'Significance Analysis of Microarrays' (SAM) (Tusher et al.,. Proc Natl Acad Sci U S A. 2001 ; 98: 51 16-21 ). For each analysis we arbitrarily fixed the threshold of statistical significance so that the false-discovery rate < 1 %
LV and RV predictors and an LV-RV predictor were used to calculate a transcriptome- based 'molecular severity score' (MSS) for each sample. The LV-RV predictor was a combination of the genes of the LV and the RV predictor. First, expression profiles were mean- centered and standard deviation-scaled on genes. The mean profile was calculated for Stable (Ms) and for Deteriorating (Md) samples. The molecular severity score (MSS) of a specific sample was defined as the normalized Euclidean distance (ranging from 0 to 1 ) between the sample and the stable mean profile and was calculated as described below:
MSS = where Es = - Ms,]2 and Ed = - Md 2 and X = the expression
Es + Ed i=i i=i
profile of the specific analyzed sample and i = an index of the n genes included in the 'predictors'.
To define the significance level of the obtained MSS, an unpredictable interval in- between the Stable and Deteriorating profiles was calculated. Using 104 random permutations of the expression profiles, we generated a set of 104 MSS. 2.5th and 97.5th percentiles of the random-MSS distribution were defined as the cut-off for the unpredictable interval.
Leave-one-out cross validation was performed on Stable and Deteriorating samples.
One hundred distinct data sets were produced. Each data set was partitioned into a test set consisting of one sample and a learning set consisting of the 99 other samples. The learning set was used to calculate an LV-RV-MSS using the strategy described. The obtained MSS was employed to predict the MSS value of the test sample. This process was repeated so that the MSS value of each sample was predicted using an MSS estimated from all other samples in the data set.
To test the diagnostic power of our classification, we calculated the sensitivity, the specificity, and the positive and negative predictive values of the molecular prediction of Stable and Deteriorating status using the cross-validation results. We also analyzed MSS values obtained from all samples using Receiver Operating Characteristic curves using the jrocfit procedure available at www.jrocfit.org.
To test whether the obtained classification was independent of the method used, we also classified Stable and Deteriorating samples using the Prediction Analysis of Microarrays (PAM) method (Tibshirani et al. Proc Natl Acad Sci U S A. 2002; 99: 6567-72) using a previously published strategy (Kittleson et al.,. Circulation. 2004; 1 10: 3444-51 ; Heidecker et al., Circulation. 2008; 1 18: 238-46.) (see "Materials and Methods" herein).
LV-RV comparison
To test for between-chamber fluctuations of our molecular prediction, we compared MSS values obtained from the LV and RV samples from the same patient. The correlation coefficient was used as a fluctuation index.
Reproducibility
We tested between-sample reproducibility of the MSS values of all biological duplicates. Expression data from biological duplicates were separated to generate 2 comparable data sets. MSS from the duplicate sets were compared using the correlation coefficient. To determine the chamber-specific reproducibility, analyses were performed separately on the LV-specific and RV-specific data sets. Drug-related bias
We tested whether between-group variations in drug treatment could have biased the Predictor discovery. To avoid confounding factors, subgroups of samples from the same chamber and the same severity group were analyzed separately. For each Predictor, mean expression profiles of samples positive and negative for a specific drug treatment were gene-by- gene compared using a student t-test. Genes with p-value < 10~2 were considered as significantly influenced by the tested drug treatment.
Potential biases
We tested whether between-group variations in drug treatment could have biased the predictor discovery. To avoid confounding factors, subgroups of samples from the same chamber and the same severity group were analyzed separately. For each Predictor, expression profiles of samples positive and negative for a specific drug treatment were gene-by- gene compared using a student t-test with p<0.01 . We also tested the predictive power of our predictor in etiology-based and age-based subgroups of patients using the same strategy.
Additional Materials and Methods details are described below.
Study design
Each of the 176 biological samples was compared to a common reference sample consisting of a pool of equal quantities of mRNA from all 176 biological samples. This complex mRNA pool was used as a standard or a common point of measurement that enabled a comparison between the individual mRNA samples.
Control of technical and biological noises inherent to microarray experiments was incorporated in the study design. To account for the technical fluctuation of the expression measurements, eight technical replicate values were obtained for each cardiac sample. To determine the fluctuation due to tissue sampling, two biological replicate samples were obtained from each ventricle. Furthermore, to control for experimenter bias in sample processing, samples were randomly distributed among the three experimenters in charge of all manipulations, at each step of the experiment.
Microarravs
Microarrays were prepared in-house using human-specific 50-mer oligonucleotide probes (MWG Biotech®). The probes were spotted onto epoxy-silane coated glass slides using the Lucidea Array Spotter (Amersham®). The 4217 human genes that were represented on the microarray had been selected for involvement in cardiovascular and/or skeletal muscle normal and pathological functioning. Selection was based on 1 - subtractive hybridization experiments (Rouger K, et al., Am J Physiol Cell Physiol. 2002; 283: C773-C784; Steenman et al.,. Eur J Heart Fail. 2005; 7: 157-65), 2- genome-wide microarray hybridizations (Steenman et al.,. Physiol Genomics. 2003; 12: 97-1 12), 3- literature data. Each probe was spotted in quadruplicate. For more information, see the following Web address:
http://cardioserve.nantes.inserm.fr/ptf-puce/spip.php7article59 RNA isolation, labeling, and hybridization
Total RNA was isolated using TRIZOL® reagent (Life Technologies). mRNA was isolated using the Oligotex mRNA kit (Qiagen). RNA and mRNA quality was assessed using an Agilent 2100 bioanalyzer. Cy3- and Cy5-labeled cDNA was prepared using the CyScribe cDNA Post Labeling Kit (Amersham Pharmacia Biotech). Each individual mRNA sample was Cy3- labeled and mixed with an equal amount of Cy5-labeled reference sample. The mixture was pre-incubated with human Cot-I DNA (Gibco-BRL), yeast tRNA, and polyA RNA, and hybridized onto duplicate microarrays.
Raw data extraction and consolidation
Hybridized arrays were scanned by fluorescence confocal microscopy (Scanarray 4000XL,
GSI-Lumonics). Fluorescence signal measurements were obtained separately for each fluorochrome at 10 μηι/pixel resolution. Hybridization and background signal intensities, and quality control parameters were measured using GenePix Pro 5.0 (Axon®). For raw expression datasets see: http://cardioserve.nantes.inserm.fr/HF profiling.
A Lowess normalization procedure was performed to correct for technical biases. ( Yang et al., Nucleic Acids Res. 2002; 30: e15) The procedure was applied channel-by-channel as described previously. (Workman et al., Genome Biol. 2002; 3: research0048) For each microarray, Cy3- and Cy5-signal intensities were separately normalized to a prototype defined as the median profile of all Cy3- or Cy5-signal intensities. Genes of which all signal intensities were below the background level were filtered out. For each microarray, expression values were calculated as log2(Cy3/Cy5). For each biological sample, expression values were then consolidated as the median of the eight technical replicate values.
Sample classification using Prediction Analysis of Microarrays:
To test whether the obtained classification was independent of the method used, we also classified Stable and Deteriorating samples using the Prediction Analysis of Microarrays (PAM) method (Tibshirani et al., Proc Natl Acad Sci U S A. 2002; 99: 6567-72) using a previously described strategy (Heidecker et al., Circulation. 2008; 1 18: 238-46; Kittleson et al., Circulation. 2004; 1 10: 3444-51 ). One hundred random partitions from each data set (LV and RV) were computed. For each partition, 2/3 of the samples were allocated to a training set and 1 /3 to a test set. In the training set, the threshold to select which genes should be included in the predictor (a list of genes that can distinguish Stable from Deteriorating samples) was arbitrarily set to 2 for all the experiments. This predictor was then applied to the test set. We then calculated the sensitivity, the specificity, and the positive and negative predictive values of PAM for the prediction of Deteriorating status after averaging the results obtained for the 100 different partitions.
B. Results of the examples
Example 1 : Selection and validation of the heart-specific markers
We profiled cardiac gene expression in a cohort of 44 advanced-HF patients using a 4217-oligonucleotide microarray containing genes selected for their involvement in cardiac (patho)physiology. Based on the analysis of clinical information the 44 patients were classified into three HF-severity groups: Deteriorating (n=12), Intermediate (n=19) and Stable (n=13). After raw data extraction and consolidation, 4035 genes were validated for further analysis.
The clinical characteristics of HF severity patient groups are depicted in Table 5 hereunder.
In Table 5 : CAD: Coronary Artery Disease; DCM: Dilated Cardiomyopathy; LVEF: Left Ventricle Ejection Fraction; LVEDD: Left Ventricle End Diastolic Diameter; ACEI: Angiotensin Converting Enzyme Inhibitors; ARB: Angiotensin Receptor Blockers; ADHF: Acute Decompensated Heart Failure. Data are presented as 'mean (SD)' when appropriate. P-value indicates the result of a comparison between the three patient groups using Fisher's exact test or Kruskal-Wallis rank sum test. If p<0.05, groups were compared two-by-two. *: p<0.05 between Deteriorating and Stable; †: p<0.05 between Intermediate and Stable; †: p<0.05 between Deteriorating and Intermediate. An ADHF episode was defined as recent if it occurred during the 3 months before the heart transplantation/total artificial heart placement. HF duration was defined as the delay between onset of HF symptoms and heart transplantation/total artificial heart placement. Values for LVEF, LVEDD, blood urea nitrogen, and serum creatinine corresponded to pre-operative measurements. All patients were treated with loop diuretics (furosemide and/or bumetanide). Only medications related to HF therapy are presented. The clinical profile was determined based on the patients' medical urgency status in the UNOS classification and the occurrence of recent ADHF episodes.
1 .1 . Hierarchical clustering and functional annotation
The 176 cardiac samples and the 4035 selected genes were clustered according to their expression profiles using a hierarchical clustering procedure (Data not shown). Samples were grouped in 2 major clusters mainly based on the expression profile of a 387-gene cluster (white bar). This patient molecular clustering was not correlated with the clinical severity classification. However, within each of the 2 major clusters, Stable and Deteriorating samples were preferentially classified into distinct sub-clusters (p<0.001 within each major cluster, χ2 test).
Gene clusters were selected by automated analysis of the gene classification. Functional annotation revealed enrichment of genes involved in a specific biological process or tissue-type for most of the clusters. Clusters that were too small to obtain a statistically significant annotation using Gominer software (annotations 'natriuretic peptides' and 'cell metabolism') were functionally annotated based on literature analysis. Several of the clusters showed marked differential expression between Stable and Deteriorating samples for LV and/or RV samples. 'Cell metabolism', 'natriuretic peptides', and 'extracellular matrix' gene clusters displayed higher expression for Deteriorating samples than for Stable samples in both LV and RV. 'Cytoskeleton' and 'cell death' gene clusters displayed higher expression for Stable samples than for Deteriorating samples in both LV and RV. Interestingly, the 'mitochondrion' gene cluster displayed higher expression for Stable samples than for Deteriorating samples in RV but not LV.
1 .2. Prediction of clinical status
Two-class statistical analysis of gene expression profiles of the 24 Deteriorating and 26
Stable samples resulted in the identification of 167 and 126 differentially expressed genes for LV and RV samples respectively. Sixty-six genes were present in both LV and RV predictors. A 225-gene LV-RV predictor was also created by combining the genes of the LV and RV predictors (Figure 1 ). MSS values of patients were calculated based on their individual expression profile for these three predictors.
Figure 1 shows MSS values calculated for the Stable and Deteriorating groups based on the different predictors. In the training set, 95 out of 100 samples were predicted in concordance with the clinical classification, whereas one Stable sample was predicted as Deteriorating and four samples were in the unpredictable interval. These five samples were all LV samples, whereas all RV samples were correctly predicted.
We also used data from all samples to generate Receiver Operating Characteristic curves for each predictor (Figure 2). The LV and RV molecular predictor could accurately identify patients with stable and deteriorating statuses (all area under curve >0.95).
A cross-validation strategy was also employed to account for data over-fitting due to reclassification of the samples used to define the predictors. The overall good classification rate was 94/100, whereas one Stable sample was predicted as Deteriorating and five samples were in the unpredictable interval. The LV molecular predictor identified patients with stable and deteriorating status with a sensitivity of 88% and 92%, and a specificity of 100% and 96%, respectively. The RV molecular predictor identified patients with stable and deteriorating status with a sensitivity of 100% and 96%, and a specificity of 100% and 100%, respectively. The difference in proportion of samples correctly classified for LV (45/50) and RV (49/50) samples was not statistically significant (p=0.20, Fisher exact test). Equivalent prediction power results were obtained when using the PAM prediction method. (Table 7).
We also tested the predictive power of a single-gene predictor based on NPPB gene expression levels (Figure 3). Using this predictor, a high misclassification rate was observed for deteriorating patients in both LV and RV samples.
We investigated whether a significant correlation exists between the Molecular Severity Score and several clinical parameters. Only heart rate correlated significantly with the Molecular Severity Score for both LV and RV data. Left Ventricle End-Diastolic Diameter and Brain Natriuretic Peptide blood level significantly correlated with LV data only, whereas Systolic Arterial Pressure correlated significantly only with RV data. Interestingly, Left Ventricle Ejection Fraction did not correlate with the Molecular Severity Score.
1 .3. Intermediate group analysis
In agreement with the clinical classification, patients of the Intermediate group - who were not used for the construction of the predictors - were on average classified in-between the two other groups using the LV-RV predictor (Figure 4). Progression of clinical severity for the LV samples was associated with a gradual increase of the MSS mean values from 0.32 for the Stable group to 0.51 and 0.68 for the Intermediate and the Deteriorating group respectively (p<0.001 for overall and all pairwise comparisons, one-way analysis of variance on ranks followed by Dunn test). A similar gradual increase of the MSS mean values was also observed for RV samples. In addition, we observed that 50 of the 76 samples (66%) in the Intermediate group exhibited MSS values outside the unpredictable interval and could have been predicted as either Stable or Deteriorating.
1 .4. Drug-related effects
Clinical characteristics of the three patient groups are summarized in Table 1 . The groups were comparable regarding sex, age, HF etiology, and LV ejection fraction. As expected, differences in severity levels were associated with significant inter-group variations regarding treatment with adrenergic agonists, phospho-diesterase inhibitors, beta-blockers, and angiotensin converting enzyme inhibitors/angiotensin receptor blockers. Among the 233 genes in the LV-RV predictor, significant differences in expression related to medication were found for only 3 to 1 1 genes. Removing these genes from the predictors did not modify the number of samples predicted in concordance with the clinical classification (data not shown).
1 .5. Effects of potential biases
The three patient groups were comparable regarding sex, age, HF etiology, and LV ejection fraction (Table 1 ). As expected, differences in severity levels were associated with significant inter-group variations regarding treatment with adrenergic agonists, phospho-diesterase inhibitors, beta-blockers, and angiotensin converting enzyme inhibitors/angiotensin receptor blockers. Significant differences in expression related to these medications were found for only 0-7.0% of the genes included in the LV and RV predictors. Furthermore, significant differences in expression related to age and HF etiology were found for 1 .2 to 2.9% and for 0.6 to 2.3% of the genes respectively. Removing these genes from the predictors did not modify the good classification rates of the samples (data not shown). We also performed distinct prediction analysis for ischemic and non-ischemic patients. The results show that our classification can be accurately applied to both ischemic and non-ischemic patients (Figure 5).
1 .6. Biological Reproducibility We aimed to test whether our classification was reproducible across biological replicates. A significant correlation between MSS values obtained for the duplicate sets was observed (Figure 6), with a better correlation for RV samples than for LV samples.
Figure 6A shows a comparison of patient MSS values obtained from LV and RV data using the LV-RV predictor. A significant correlation between LV and RV MSS values was observed irrespective of the sample severity group. We also aimed to test whether our classification was reproducible across biological replicates. A significant correlation between MSS values obtained for the duplicate sets was observed (Figure 6B), with a better correlation for RV samples than for LV samples.
1 .7. Summary of the results obtained in Example 1
We produced and analyzed the largest set to date of transcriptomal profiles of LV and RV samples from a cohort of 44 HF patients. Ventricular samples were analyzed using a dedicated microarray representing genes selected for their contribution to muscular organ (patho)physiology. Replication at both the biological and the technical level, and control of experimental variations at the different steps of the study allowed detection of even subtle expression changes. We identified a set of genes of which expression changes discriminated between patients with different clinical severity levels and established that clinical deterioration of HF patients was associated with a molecular deterioration expression profile in both LV and RV. Therefore, our study confirms the potential of cardiac gene expression profiling to identify outcome predictors in patients with advanced HF.
1.7. 1. Related findings in previous studies
It has previously been shown that gene expression profiling can discriminate between cardiac patients with different clinical characteristics (Blaxall etal., J Am Coll Cardiol. 2003; 41 : 1096-106; Kaynak et al.,. Circulation. 2003; 107: 2467-74; Liew et al., Nat Rev Genet.
2004; 5: 81 1 -25; Kittleson et al., Circulation. 2004; 1 10: 3444-51 ; Steenman et al., Eur J Heart Fail. 2005; 7: 157-65; Kaab et al.,. J Mol Med. 2004; 82: 308-16). Etiology-related gene expression profiles have been identified in Chagas disease, and hypertrophic, dilated, viral, and ischemic cardiomyopathies (Liew et al., Nat Rev Genet. 2004; 5: 81 1 -25; Kittleson et al., Circulation. 2004; 1 10: 3444-51 ; Wittchen et al.,. J Mol Med. 2007; 85: 257-71 ). In a recent study, , Heidecker et al. identified a transcriptomic signature that could predict clinical outcome of new-onset idiopathic dilated cardiomyopathy patients (Heidecker et al; Circulation. 2008; 1 18: 238-46). Taken together, these findings offer valuable information regarding the molecular basis of HF related to distinct etiologies and they could lead to individualized therapeutic strategies in HF.
Other clinical characteristics such as age and sex have also been shown to have an effect on the transcriptomal profile of HF patients (Boheler et al., Proc Natl Acad Sci U S A. 2003; 100: 2754-9). In our study, the molecular severity markers correctly classified HF patients independent of etiology or age. Because most of our patients were male, we could not validate our classification in female HF patients. We also showed that our results were unchanged when another prediction method was used (Kittleson et al., Circulation. 2004; 110: 3444-51 ; Heidecker et al.,. Circulation. 2008; 118: 238-46). 1.7.2. Potential clinical significance of findings
Prognosis evaluation for advanced HF patients
The results of our study suggest that gene expression profiling has the potential to detect HF patients with highest HF severity with high sensitivity and specificity. Prognosis evaluation is fundamental for the indication of LVAD implantation and heart transplantation in advanced HF patients. Patients depending on intravenous inotropic therapy have the worst prognosis and should benefit from urgent or elective LVAD implantation or urgent transplantation whenever possible. However, risk stratification remains particularly difficult for ambulatory advanced HF patients not depending on intravenous inotropic therapy or prolonged hospitalization, with major impairment of their functional capacities and poor survival. Specific risk scores are not yet available for advanced HF patients but become mandatory in the context of this growing cohort of patients.
Our results showed that the LV and RV predictors lead to a better prediction of clinical status than the NPPB predictor, in particular regarding the prediction of the deteriorating status. It has previously been shown that the NPPB mRNA level in the left ventricle and the BNP peripheral blood level are correlated (Hystad et al., . Acta Physiol Scand. 2001 ; 171 : 395-403). The B-type Natriuretic Peptide (BNP) blood level is widely used as a clinical predictor for HF patients. However the BNP blood level predictive value is still controversial in the specific condition of end-stage HF (Potapov et al., Eur J Cardiothorac Surg. 2005; 27: 899-905; Miller et al., Am J Cardiol. 2005; 96: 837-41 ). A previous report showed that a lower natriuretic peptide blood level, that usually implies a better outcome, may also imply poor outcome in severe HF patients (Miller et al., Am J Cardiol. 2005; 96: 837-41 ). Similarly, our results show a low NPPB mRNA level for patient in the deteriorating status group.
Our results provide a rational to develop prospective clinical research studies using gene expression measurement techniques in advanced HF. While microarrays are a unique tool to screen the largest number possible of potential biomarkers, which was the aim of this study, other techniques such as quantitative RT-PCR will be of greater interest to develop a clinically relevant outcome predictor based on a set of selected biomarkers.
Transcriptomal remodeling of the right ventricle
Our results suggest that molecular prediction using samples taken from RV may be as powerful as molecular prediction using samples taken from LV. Most of the patients with advanced HF have severe LV dysfunction, whereas RV dysfunction intensity is variable among these patients. In addition, transcriptome remodeling of the RV in HF has been evaluated to a lesser extent than for the LV. Our data show that most of the molecular processes disturbed in the LV are also disturbed in the RV. In addition, sensitivity and specificity of prediction of both Stable and Deteriorating statuses using RV samples were at least equivalent to those obtained using LV samples. These results are in agreement with a previous study showing accurate prediction of clinical outcome of new-onset HF patients using a transcriptomic signature obtained from RV endomyocardial biopsies (Heidecker et al., Circulation. 2008; 118: 238-46).
For some specific clinical situations, such as Arrythmogenic RV Dysplasia or Severe
LV infarction with unaffected RV, RV and LV function/morphology may clearly differ. In our study, we could not obtain samples for these very specific groups of patients. Therefore, our results cannot be extended to these patients. However, while these clinical profiles represent a relatively moderate percentage of advanced HF patients, our results can be applied to a majority of patients in advanced HF.
Prediction reproducibility
Measurement reproducibility is another crucial point when developing a predictor of HF severity. Relatively high variability of widely used biomarkers like BNP or N-terminal proBNP blood levels may be a problem for patient management (Bruins et al., Clin Chem. 2004; 50: 2052-8). Our results show that gene expression profiling is reproducible among biological replicates. Reproducibility was higher for RV samples, reinforcing the interest of RV sample utilization to develop a molecular predictor in advanced HF. A hypothesis is that regional tissue heterogeneity may be higher in LV than in RV. One cause may be the presence of infarct scars that preferentially affect the LV. However, ventricular samples analyzed in this study were obtained after careful dissection of the ventricles excluding infarct scars. We also did not observe a higher variability of MSS values obtained for LV samples in patients affected by coronary artery disease compared to other patients. 1.7.3. Potential limitations
Complexity of myocardial remodeling
While transcriptional remodeling is an important mechanism of cardiac remodeling occurring in HF, post-translational modifications are also of crucial importance. Therefore, additional techniques such as Western blot and possibly additional experiments would be necessary to verify a mechanistic role for a single gene/protein, which was not the scope of this study. This study was designed to identify transcriptomic biomarkers that would reveal to be useful for patient classification. We also showed that, at the functional level, most of the identified biomarkers are involved in molecular functions that are important for myocardial remodeling associated with HF.
Effect of medication
Therapeutic interventions, in particular medications, may induce modifications of the cardiac transcriptome (Lowes et al, N Engl J Med. 2002; 346: 1357-65). We tested the hypothesis that the patient classification may be modified by angiotensin converting enzyme inhibitors/angiotensin receptor blockers, beta-blockers, and inotropic drugs. A very low number of genes included in the distinct predictors displayed differential expression associated with different drug intake. Removing these genes from the predictors did not modify the patients' classification. Therefore, medications do not strongly modify the expression level of our predictors. Clinical classification
We compared our molecular predictors to a 2-parameter clinical classification that has not been previously evaluated in advanced HF. Because we used samples taken at the time of cardiac transplantation, it was not possible to compare our predictors to a relevant clinical end- point like mortality or hospitalization for ADHF. We hypothesized that the use of parameters measured at the time of transplantation would better reflect the clinical phenotype at this time and decided to combine two established predictors of HF severity to classify patients. The UNOS medical urgency status has been specifically developed for advanced HF patients listed for cardiac transplantation. The UNOS-1 A status at the time of listing is associated with a 1 month-mortality >30% whereas UNOS-2 patients have a 1 month-mortality <10% (Smits et al., Transplantation. 2003; 76: 1 185-9). The mortality rate on the UNOS waiting list is more than 4 fold higher for UNOS-1 A than for UNOS-2 patients (Deng et al., Curr Opin Cardiol. 2002; 17: 137-44). To better define our group of Stable patients we combined the UNOS medical urgency status with the occurrence of ADHF episodes. Frequent rehospitalizations have been recognized as a strong predictor of HF patient mortality ( Metra et al., Eur J Heart Fail. 2007; 9: 684-94). Other HF severity prediction scores have been developed in advanced HF (Aaronson et al., Circulation. 1997; 95: 2660-7 ; Smits et al., . Transplantation. 2003; 76: 1 185- 9). Comparison of one of these HF severity predictors to the UNOS medical urgency status did not reveal a higher predictive power (Smits et al., Transplantation. 2003; 76: 1 185-9). Other predictors included the measurement of peak oxygen consumption that cannot be recorded in the most severely affected patients (Aaronson et al., Circulation. 1997; 95: 2660-7).
We analyzed expression profiles of patients with advanced HF at the time of cardiac transplantation. Further clinical studies are needed to determine whether gene expression profiling of cardiac tissue provides sensitive prognostic information for advanced ambulatory HF patients using clinical end-points like mortality or hospitalization for HF.
Table 1 Table 1 Table 1
Gene Symbol SEQ ID N° Gene Symbol SEQ ID N° Gene Symbol SEQ ID N°
ACAA1 1 CBX5 30 EDG1 59
AC A DM 2 CCNG1 31 EEF1 A1 60
ACTC 3 CD63 32 EEF1 B2 61
A2M 4 CDH13 33 EEF2 62
ADAMTS5 5 CDH9 34 EFEMP1 63
ADSL 6 CFL1 35 EN03 64
AEBP1 7 CFL2 36 EPAS1 65
ANXA1 8 CHPF 37 ERP29 66
ANXA10 9 CLIC5 38 FABP4 67
ANXA1 1 10 CKM 39 FAM13A1 68
ANXA2 1 1 CMYA3 40 FGF12 69
ARPC2 12 COL1 A1 41 FHL1 70
ATP1 B3 13 COL1 A2 42 FKBP5 71
AZGP1 14 COL2A1 43 FLJ22655 72
BSG 15 COL3A1 44 FLNC 73
BXDC2 16 COL4A5 45 FHL2 74
BZW2 17 CSDE1 46 FLJ20152 75
C15orf41 18 COL6A3 47 FN1 76
C21 orf33 19 CTAGE1 48 FXYD1 77
C1 GALT1 C1 20 CTSB 49 G6PD 78
C1 orf63 21 CTSD 50 GAPDH 79
C1 R 22 DBI 51 GBAS 80
C4A 23 DSTN 52 Gcoml 81
CAV1 24 EIF4A2 53 GLUL 82
C6orf203 25 CXX1 54 GPNMB 83
CCR2 26 DMPK 55 GPR133 84
CD36 27 DNAJB1 1 56 GOT1 85
CAB39 28 DXS9879E 57 GPR83 86
CABYR 29 DYNLL1 58 GPX3 87 Table 1 Table 1 Table 1
Gene Symbol SEQ ID N° Gene Symbol SEQ ID N° Gene Symbol SEQ ID N°
GUK1 88 LMOD3 1 17 OR1 D5 146
HADHB 89 LOC284393 1 18 P4HB 147
HADHSC 90 LOC649550 1 19 PALLD 148
HCA1 12 91 LRRFIP2 120 PAM 149
HLA-A 92 LTBP2 121 PCOLCE 150
HLA-DRB4 93 MAPKAPK3 122 PCOLCE2 151
HRC 94 ME2 123 PDIA3 152
HTRA 95 MFSD5 124 PDK4 153
HSPA4L 96 MGST3 125 PDLIM1 154
IFI16 97 MGP 126 PDLIM3 155
IGFBP5 98 MIF 127 PKIA 156
JAK2 99 MMACHC 128 PKM2 157
KCTD15 100 MMP2 129 PKP2 158
IFITM1 101 MRCL3 130 PLA2G2A 159
IFITM2 102 MTHFD2 131 PLN 160
KCNA10 103 MYL5 132 POLR2L 161
KCNJ8 104 MYL7 133 POPDC2 162
KCNK10 105 MY015A 134 PPGB 163
KCNQ1 106 MYOM1 135 PPP2CB 164
KIAA0859 107 NDUFB4 136 PRAF2 165
KLF13 108 NEXN 137 PRKAA2 166
LAMA4 109 NIFIE14 138 PRKAG2 167
LDHA 1 10 NKX2-5 139 PRDX1 168
LIN10 1 1 1 NPC2 140 PRKAG3 169
LM07 1 12 NPPA 141 PSMB1 170
LOC220729 1 13 NPPB 142 PSMB10 171
LENG8 1 14 NRAP 143 PTMAP1 172
LGMN 1 15 NRG3 144 PTP4A2 173
LMNA 1 16 NRN1 145 RANBP1 174 Table 1 Table 1
Gene Symbol SEQ ID N° Gene Symbol SEQ ID N°
RGS5 175 SSR3 204
RPL12 176 SPARC 205
RPL18 177 SPP1 206
RPL3 178 SSR4 207
RPL35 179 STAT6 208
RPLPO 180 TIMP1 209
RPN2 181 TLN1 210
RPS12 182 TNFRSF17 21 1
RPS19 183 TNNI1 212
RPS2 184 TPM2 213
RPS6 185 TRA1 214
RPSA 186 TTC25 215
RRAS2 187 TXNIP 216
RYR2 188 VIM 217
S100A10 189 VTN 218
S100A1 1 190 VWF 219
SAT 191 YWHAE 220
SCD 192 ZNF189 221
SCNN1 D 193 WIF1 222
SDHA 194 ZBTB16 223
SCNN1 A 195 ZC3H7A 224
SLC6A6 196 ZNF9 225
SLC9A3R2 197
SERPINB2 198
SLC1 A3 199
SLC40A1 200
SLMAP 201
SMG1 202
SNX26 203
Figure imgf000050_0001
Figure imgf000051_0001
Figure imgf000052_0001
Figure imgf000053_0001
Figure imgf000054_0001
Figure imgf000055_0001
Figure imgf000056_0001
Figure imgf000057_0001
Figure imgf000058_0001
Figure imgf000059_0001
Figure imgf000060_0001
Figure imgf000061_0001
Figure imgf000062_0001
Table 3
Left ventricle predictor
Gene symbol Up/Downregulated P value
MYL7 DOWN 3.5415E-1 1
VTN UP 2.8072E-10
LDHA UP 5.5017E-10
CD63 UP 1 .7726E-08
A2M UP 1 .8073E-08
CTSD UP 2.2130E-08
BSG UP 3.0897E-08
FXYD1 UP 6,6721 E-08
VWF UP 8.4085E-08
PDLIM3 UP 1 .1597E-07
C4A UP 2.5059E-07
S100A1 1 UP 3.4184E-07
C15orf41 UP 3.9594E-07
PLN DOWN 4.9983E-07
EDG1 UP 5.4689E-07
RYR2 DOWN 5.6404E-07
MYOM1 DOWN 6.2878E-07
ANXA1 1 UP 8.3290E-07
PAM UP 8.3677E-07
ADAMTS5 DOWN 1 .0051 E-06
MIF UP 1 .0912E-06
HCA1 12 UP 1 .3098E-06
LAMA4 UP 1 .3816E-06
PLN DOWN 1 .4043E-06
AZGP1 UP 1 ,4391 E-06
BXDC2 UP 1 .9253E-06
DYNLL1 UP 2.5326E-06
KCNJ8 UP 2,8361 E-06
NPPA UP 2.8920E-06
NPPB UP 3.4045E-06
EN03 UP 3.6942E-06
NRG3 UP 3.7204E-06
LENG8 UP 3.8887E-06
CAB39 UP 5.4633E-06
CTSB UP 5.6164E-06
LMOD3 DOWN 8.1282E-06
COL1 A1 UP 8.4977E-06
CMYA3 DOWN 8.8940E-06
BZW2 DOWN 9.4745E-06
MMACHC UP 1 .0292E-05 Table 3
Left ventricle predictor
Gene symbol Up/Downregulated P value
Gcoml DOWN 1 ,2111 E-05
GUK1 UP 1.4172E-05
CHPF UP 1.5275E-05
COL1A1 UP 1.6770E-05
PKM2 UP 1.7206E-05
P4HB UP 1.7917E-05
HRC UP 2.3029E-05
MYL5 DOWN 2.3336E-05
SSR4 UP 2.3503E-05
MTHFD2 UP 2.8223E-05
NKX2-5 UP 2,9471 E-05
COL3A1 UP 3.1137E-05
RRAS2 DOWN 4.8509E-05
LGMN UP 5.8393E-05
PLA2G2A UP 6.2692E-05
PRKAG3 UP 7.4708E-05
C6orf203 UP 7.6537E-05
TNNI1 UP 8.3660E-05
ZBTB16 DOWN 8.6554E-05
LTBP2 UP 8.8514E-05
ZNF9 DOWN 8.9947E-05
EEF1A1 UP 9.0593E-05
RPS19 UP 9.5099E-05
LMNA UP 1.0502E-04
NPC2 UP 1.0863E-04
TIMP1 UP 1.0955E-04
RPS2 UP 1.1291E-04
FHL2 UP 1.1360E-04
TPM2 DOWN 1.1506E-04
PPGB UP 1.1768E-04
TRA1 UP 1 ,2209E-04
VIM UP 1.2569E-04
EEF2 UP 1 ,3429E-04
TLN1 UP 1 ,4222E-04
MFSD5 UP 1.4534E-04
DNAJB11 UP 1.4989E-04
PSMB10 UP 1.5467E-04
TXNIP DOWN 1.5482E-04
OR1D5 DOWN 1.5969E-04
NRAP DOWN 1.6146E-04
SERPINB2 UP 1.6603E-04
CCNG1 UP 1.7277E-04 Table 3
Left ventricle predictor
Gene symbol Up/Downregulated P value
SAT UP 1 .7616E-04
CBX5 UP 1 .8457E-04
C1 R UP 1 .8899E-04
ARPC2 UP 2.0548E-04
SLMAP DOWN 2.2209E-04
WIF1 UP 2.2477E-04
KCNK10 UP 2,3531 E-04
RPN2 UP 2.3883E-04
CTAGE1 DOWN 2.4445E-04
TNFRSF17 UP 2.4636E-04
KCNQ1 UP 2.5818E-04
MAPKAPK3 DOWN 2.6570E-04
ZC3H7A UP 2.6927E-04
SMG1 UP 2,8316E-04
RPSA UP 3.3546E-04
KLF13 UP 3.7144E-04
ADSL UP 3,8615E-04
PDLIM1 UP 3.8966E-04
FN1 UP 3.9252E-04
DMPK UP 3.9445E-04
ANXA10 DOWN 4,0081 E-04
CXX1 UP 4.3929E-04
COL1 A1 UP 4.5259E-04
PKP2 DOWN 4.8469E-04
MRCL3 DOWN 5.0995E-04
DXS9879E UP 5.1993E-04
RPS12 UP 5.3689E-04
RANBP1 UP 5,6691 E-04
RPL3 UP 5.8426E-04
MRCL3 DOWN 6.0542E-04
C1 GALT1 C1 UP 6,4621 E-04
COL1 A2 UP 6.5882E-04
ATP1 B3 UP 6.7244E-04
PCOLCE UP 6.9175E-04
STAT6 UP 7,4181 E-04
GAPDH UP 7.8560E-04
ANXA2 UP 7.9256E-04
IFITM1 UP 8.1418E-04
G6PD UP 8.3859E-04
MY015A UP 8.4292E-04
PTP4A2 UP 8,6531 E-04
FAM13A1 UP 8,9251 E-04 Table 3
Left ventricle predictor
Gene symbol Up/Downregulated P value
ERP29 UP 9.3196E-04
GOT1 UP 1 .0013E-03
SPP1 UP 1 .0673E-03
SLC40A1 UP 1 .0859E-03
CKM DOWN 1 .1622E-03
AEBP1 UP 1 .1913E-03
EFEMP1 UP 1 ,2361 E-03
SLC1 A3 UP 1 .2380E-03
HLA-DRB4 UP 1 .2509E-03
RPL12 UP 1 .2804E-03
IFITM2 UP 1 .2813E-03
KIAA0859 DOWN 1 .2918E-03
RPL35 UP 1 .4260E-03
PDK4 UP 1 .4516E-03
SCNN1 A UP 1 .4880E-03
MMP2 UP 1 .5999E-03
COL6A3 UP 1 .7190E-03
SPARC UP 1 .7800E-03
HSPA4L UP 1 .8377E-03
KCNA10 UP 1 .8946E-03
GPX3 UP 1 .9264E-03
NIFIE14 UP 1 .9863E-03
COL2A1 UP 2.0265E-03
LRRFIP2 UP 2.0549E-03
PRAF2 UP 2.1 133E-03
POPDC2 UP 2.1739E-03
NRN1 UP 2.1789E-03
GPR83 UP 2.1878E-03
LOC284393 UP 2.2325E-03
CABYR UP 2.3175E-03
S100A10 UP 2,3415E-03
TTC25 UP 2,3821 E-03
SNX26 UP 2.4637E-03
FLJ20152 UP 2.4796E-03
PSMB1 UP 2.5469E-03
PTMAP1 UP 2.6134E-03
RPS6 UP 2.6753E-03
C1 orf63 UP 2.7522E-03
EEF1 B2 UP 2,8351 E-03
PRDX1 UP 3.1224E-03
HLA-A UP 3.2404E-03
LOC649550 UP 3.2749E-03 Table 3
Left ventricle predictor
Gene symbol Up/Downregulated P value
ANXA1 UP 4J592E-03
RPL18 UP 4.7698E-03
RPLPO UP 4.9537E-03
MGP UP 6.0325E-03
Table 4
Right ventricle predictor
Gene symbol Up/Downregulated P value
PLN DOWN 1 .0384E-1 1
LDHA UP 2,1701 E-1 1
ZNF9 DOWN 7.0326E-10
MYOM1 DOWN 1 .3203E-09
MYL7 DOWN 8.5803E-09
PKP2 DOWN 1 ,2524E-08
SLMAP DOWN 2.9767E-08
NRAP DOWN 3.9312E-08
BZW2 DOWN 2.0557E-07
VTN UP 2.1485E-07
MMACHC UP 3.9805E-07
NDUFB4 DOWN 5.5769E-07
NPPA UP 2.1246E-06
PDLIM3 UP 2.2062E-06
BSG UP 3.9525E-06
ACTC DOWN 5.2767E-06
EPAS1 DOWN 5.7073E-06
MAPKAPK3 DOWN 6.0242E-06
NPPB UP 9,551 1 E-06
MFSD5 UP 9.6489E-06
COL4A5 UP 9.9392E-06
PLA2G2A UP 1 .0044E-05
CHPF UP 1 .1629E-05
MIF UP 1 .1775E-05
ARPC2 UP 1 ,4894E-05
PKIA DOWN 1 .5484E-05
CD63 UP 1 .8074E-05
PRKAA2 DOWN 2.2326E-05
ADAMTS5 DOWN 2,2331 E-05
SERPINB2 UP 2.5177E-05
HADHSC DOWN 2.5629E-05
HRC UP 2.6094E-05
PDLIM1 UP 2.6456E-05
NEXN DOWN 2.9806E-05
C4A UP 3.5056E-05
CLIC5 DOWN 3.9049E-05
CFL2 DOWN 4,0481 E-05
PPGB UP 4.1 105E-05
DBI DOWN 5.8933E-05 Table 4
Right ventricle predictor
Gene symbol Up/Downregulated P value
SSR4 UP 6.2373E-05
TXNIP DOWN 6.7543E-05
SLC6A6 UP 7.3253E-05
CSDE1 DOWN 8.9740E-05
GPR83 UP 8.9754E-05
Gcoml DOWN 9.7137E-05
MTHFD2 UP 9,7861 E-05
CMYA3 DOWN 9.9388E-05
PLN DOWN 1 .0186E-04
IGFBP5 DOWN 1 .0529E-04
PDIA3 UP 1 .1019E-04
PRKAG2 DOWN 1 .1246E-04
SSR3 DOWN 1 .1303E-04
GPR133 UP 1 .1483E-04
COL1 A1 UP 1 .1672E-04
HADHB DOWN 1 .1796E-04
COL1 A1 UP 1 ,2443E-04
AC A DM DOWN 1 ,2749E-04
HTRA1 UP 1 .4174E-04
EN03 UP 1 .4215E-04
PCOLCE2 UP 1 ,4320E-04
LINK) DOWN 1 .6560E-04
SAT UP 1 .8313E-04
RYR2 DOWN 1 .8993E-04
PRKAG3 UP 1 .9161 E-04
GAPDH UP 2.1681 E-04
COL3A1 UP 2.1760E-04
CFL1 DOWN 2.2862E-04
PPP2CB DOWN 2,5851 E-04
P4HB UP 2.6488E-04
YWHAE DOWN 2,7021 E-04
COL1 A1 UP 2.8730E-04
OR1 D5 DOWN 2.9432E-04
KCTD15 UP 2.9667E-04
CDH13 DOWN 3.0925E-04
IFI1 6 UP 3.0999E-04
CTSD UP 3.2993E-04
FABP4 UP 3.3622E-04
SLC9A3R2 DOWN 3.5932E-04
NPC2 UP 3.9364E-04
MGST3 DOWN 4,0231 E-04 Table 4
Right ventricle predictor
Gene symbol Up/Downregulated P value
C21 orf33 DOWN 4.1060E-04
ZNF189 DOWN 4.3101 E-04
VWF UP 4.5453E-04
CAV1 DOWN 4.6738E-04
SDHA DOWN 5.1509E-04
SNX26 UP 5.3526E-04
LM07 DOWN 5.6584E-04
CTSB UP 5.6885E-04
CTAGE1 DOWN 6.0492E-04
TXNIP DOWN 6.2468E-04
LAMA4 UP 6.2673E-04
S100A10 UP 6.4439E-04
ACAA1 UP 6,5851 E-04
EIF4A2 DOWN 6.7300E-04
LOC220729 DOWN 6.7786E-04
COL2A1 UP 7.1319E-04
MRCL3 DOWN 7.5950E-04
JAK2 DOWN 7.9485E-04
FHL1 DOWN 8.2557E-04
SCD UP 8.6556E-04
SCNN1 D DOWN 8.6642E-04
CDH9 UP 8.7023E-04
NRG3 UP 8.7857E-04
S100A1 1 UP 9.1566E-04
GPX3 UP 9.1862E-04
RRAS2 DOWN 9.9188E-04
CD36 DOWN 1 .0448E-03
GBAS DOWN 1 .0470E-03
FLJ22655 UP 1 .0755E-03
ME2 DOWN 1 .0849E-03
PAM UP 1 .0853E-03
FAM13A1 UP 1 .1227E-03
POLR2L DOWN 1 .1908E-03
PALLD DOWN 1 .1957E-03
RGS5 DOWN 1 .1961 E-03
TIMP1 UP 1 .2135E-03
NEXN DOWN 1 .3001 E-03
COL1 A2 UP 1 .4375E-03
FGF12 DOWN 1 .4589E-03
C15orf41 UP 1 ,4872E-03
FHL1 DOWN 1 .8785E-03 Table 4
Right ventricle predictor
Gene symbol Up/Downregulated P value
FKBP5 DOWN 1 .9739E-03
TRA1 UP 2.0183E-03
GPNMB DOWN 2.1762E-03
FLNC DOWN 2.3104E-03
DSTN DOWN 2.7632E-03
CCR2 DOWN 3.4489E-03
FN1 UP 3.4694E-03
GLUL DOWN 3.5574E-03
Table 5
Stable Deteriorating Intermediate
n=13 n=12 n=19 p-value
Male / Female 12/1 10/2 16/3 0.747
Age, years 50 (15) 49 (9) 48 (12) 0.559
Initial cardiac disease, CAD / DCM / other 5/6/2 4/7/1 8/7/4 0.840
HF duration, months 32 (29) 24 (33) 29 (32) 0.459
Heart rate, min"1 69 (13) 100 (16) 76 (16) <0.001 *t
Systolic arterial pressure, mmHg 102 (17) 97 (9) 103 (12) 0.509
LVEF, % 24 (11) 22 (7) 24 (7) 0.829
LVEDD, mm 73 (13) 66 (7) 65 (10) 0.254
Blood urea nitrogen, mmol/l 9.1 (5.6) 9.8 (4.2) 9.0 (4.1) 0.884
Serum Creatinine, μιτιοΙ/Ι 107 (26) 107 (22) 101 (36) 0.642
Medications, % of patients
ACEI / ARB 100 58 84 0.024 *
Beta-Blockers 69 0 26 <0.001 *†
Adrenergic agonists 0 100 42 <0.001 *†t
Phosphodiesterase Inhibitors 0 67 0 <0.001 *t
Aldosterone blockers 77 58 53 0.420
Statin 46 33 32 0.724
Digoxin / Digitoxin 46 25 26 0.502
UNOS medical urgency status 2 1A 1Bor2
Number of recent ADHF episodes 0 1.8 (0.4) 1.7 (0.8)
TABLE 6
patient sex age severity physical RV laboratory tests
echocardiography medications
status medical history examination
UNOS status recent RV dilation MPAP BNP
ACEI PDE inhibitors
ADHF episodes initial BUN serum creatinin adrenergic digitoxin
HR SAP LVEF LVEDD ARB aldosteron statin
cardiac disease duration of agonist digoxin blockers blockers
HF
years number months min -1 mmHg % mm mmHg ng/1 mmol/1 μπιοΐ/ΐ
Stable group
01 M 46 2 0 DCM 34 85 120 23 64 4.1 75
02 M 60 2 0 CAD 68 60 92 20 67 11.5 143
03 M 63 2 0 CAD 42 58 99 17 75 24.3 129
04 M 50 2 0 CAD 3 58 100 20 68 4.5 87
05 M 50 2 0 DCM 6 63 100 25 96 7.1 102
06 M 64 2 0 DCM 10 53 113 25 69 10.1 147
07 F 24 2 0 CHD 8 83 91 50 50 666.2 5.1 72
08 M 41 2 0 DCM 48 90 110 7 93 7.6 89
09 M 54 2 0 DCM 59 77 77 30 73 5.9 125
10 M 19 2 0 CHD 6 80 96 40 na 4.6 73
11 M 64 2 0 CAD 6 52 80 20 80 58.8 12.0 115
12 M 59 2 0 DCM 91 81 115 13 70 332.6 14.6 120
13 M 60 2 0 CAD 33 61 138 20 66 7.2 118
Intermediate group
14 M 60 2 2 DCM 26 60 90 19 58 1016.0 11.2 134
15 M 44 IB 1 CAD 3 101 113 30 66 na na
16 M 59 2 2 DCM 3 78 97 25 69 174.9 10.3 90
17 F 38 2 1 CHD 60 66 101 30 70 794.2 8.3 98
18 M 56 IB 2 DCM 18 82 99 14 70 7.5 147
19 M 54 2 1 CAD 31 119 116 20 66 1261.4 16.5 157
20 M 54 2 4 CAD 63 51 108 35 53 21.1 184
21 M 50 2 1 CAD 3 76 106 16 76 7.2 92
22 M 57 IB 2 DCM 4 90 83 21 87 544-9 7.9 82
23 M 57 IB 2 DCM 16 82 115 13 69 9.6 124
24 M 44 2 1 RCM 71 60 114 18 49 8.2 56
25 M 42 IB 1 CAD 2 75 100 30 60 961.3 6.3 74
26 M 65 IB 1 CAD 16 79 80 20 79 4.7 94
27 F 24 IB 1 DCM 1 60 120 25 52 3.9 58
28 M 46 IB 1 CAD 4 89 105 25 63 1153.8 6.5 107
29 M 56 2 2 CAD 60 61 92 25 72 10.4 105
30 M 46 IB 3 DCM 117 70 122 25 69 195.2 9.6 96
31 F 16 2 2 CHD 46 76 98 40 48 6.2 46
32 M 48 IB 2 HCM 5 75 93 20 61 599.6 6.5 79
TABLE 6 (Continued)
patient sex age severity physical echocardiograph RV laboratory tests
status medical history examination medicatio y
Deteriorating group
33 M 56 1A 1 CAD 1 97 111 24 54 8.2 110 + - +
34 M 34 1 A 2 DCM 9 122 86 18 64 12.1 105 +
35 M 53 1A 2 DCM 113 80 95 25 68 517.5 10.2 133 + - +
36 M 60 1A 2 DCM 20 75 95 20 67 5.4 84 + - +
37 M 58 1A 1 CAD 4 95 87 12 66 18.8 110 +
38 F 41 1A 2 VHD 8 102 97 35 59 5.5 69 +
39 M 63 1A 2 DCM 4 78 111 28 70 9.4 131 +
40 F 45 1 A 2 CAD 2 120 93 22 52 5.4 83 +
41 M 48 1A 2 DCM 56 110 115 28 71 13.8 145 + - +
42 M 39 1 A 2 DCM 20 100 95 25 70 7.3 108 + - +
43 M 39 1A 2 DCM 50 120 90 10 71 14.4 113 + - +
44 M 52 1A 2 CAD 2 98 94 15 75 7.4 99 + - +
Table 7: Sample classification using Prediction Analysis for Microarrays Se Spe PPV NPV
Left ventricle predictor 099 091 091 0.99
Right ventricle predictor 0.95 0.95 0.94 0.96

Claims

1. An in vitro method for diagnosing an advanced heart failure, or for prognosis of the outcome of an advanced heart failure, in an individual, wherein the said method comprises the steps of : a) providing a cardiac tissue sample previously collected from the said individual;
b) quantifying, in the said cardiac tissue sample, the expression level of one or more marker genes that are indicative of the risk of occurrence of, or of the occurrence of, an advanced heart failure, wherein the said one or more marker genes are selected from the group consisting of :
ACAA1, ACADM, ACTC, A2M, ADAMTS5, ADSL, AEBP1, ANXA1, ANXA10, ANXA11, ANXA2, ARPC2, ATP1B3, AZGP1, BSG, BXDC2, BZW2, C15orf41 , C21orf33, C1GALT1C1, C1orf63, C1 R, C4A, CAV1 , C6orf203, CCR2, CD36, CAB39, CABYR, CBX5, CCNG1 , CD63, CDH13, CDH9, CFL1 , CFL2, CHPF, CLIC5, CKM, CMYA3, COL1A1, COL1A2, COL2A1, COL3A1, COL4A5, CSDE1, COL6A3, CTAGE1, CTSB, CTSD, DBI, DSTN, EIF4A2, CXX1, DMPK, DNAJB11, DXS9879E, DYNLL1, EDG1, EEF1A1, EEF1B2, EEF2, EFEMP1, EN03, EPAS1, ERP29, FABP4, FAM13A1, FGF12, FHL1, FKBP5, FLJ22655, FLNC, FHL2, FLJ20152, FN1, FXYD1, G6PD, GAPDH, GBAS, Gcoml, GLUL, GPNMB, GPR133, GOT1, GPR83, GPX3, GUK1, HADHB, HADHSC, HCA112, HLA-A, HLA-DRB4, HRC, HTRA1, HSPA4L, IFI16, IGFBP5, JAK2, KCTD15, IFITM1, IFITM2, KCNA10, KCNJ8, KCNK10, KCNQ1, KIAA0859, KLF13, LAMA4, LDHA, LIN10, LM07, LOC220729, LENG8, LGMN, LMNA, LMOD3, LOC284393, LOC649550, LRRFIP2, LTBP2, MAPKAPK3, ME2, MFSD5, MGST3, MGP, MIF, MMACHC, MMP2, MRCL3, MTHFD2, MYL5, MYL7, MY015A, MYOM1, NDUFB4, NEXN, NIFIE14, NKX2-5, NPC2, NPPA, NPPB, NRAP, NRG3, NRN1, OR1D5, P4HB, PALLD, PAM, PCOLCE, PCOLCE2, PDIA3, PDK4, PDLIM1, PDLIM3, PKIA, PKM2, PKP2, PLA2G2A, PLN, POLR2L, POPDC2, PPGB, PPP2CB, PRKAA2, PRKAG2, PRAF2, PRDX1, PRKAG3, PSMB1, PSMB10, PTMAP1, PTP4A2, RANBP1, RPL12, RPL18, RPL3, RPL35, RPLPO, RPN2, RPS12, RPS19, RPS2, RPS6, RPSA, RRAS2, RYR2, S100A10, S100A11, SAT, SCD, SCNN1D, SDHA, SCNN1A, SLC6A6, SLC9A3R2, SERPINB2, SLC1A3, SLC40A1, SLMAP, SMG1, SNX26, SSR3, SPARC, SPP1, SSR4, STAT6, TIMP1, TLN1, TNFRSF17, TNNI1 , TPM2, TRA1, TTC25, TXNIP, VIM, VTN, VWF, YWHAE, ZNF189, WIF1 , ZBTB16, ZC3H7A, and ZNF9
whereby an expression value is obtained for each quantified marker gene;
c) comparing (i) the expression value obtained at step b) for each quantified marker gene with (ii) a control expression value for the said marker gene, whereby a deregulated expression level of each marker gene may be determined; and
d) predicting or diagnosing the occurrence of an advanced heart failure in the said patient if one or more of the said marker genes comprised has a deregulated expression level.
2. The method according to claim 1 , wherein step b) consists of quantifying the expression level of two or more markers, respectively :
(i) one or more markers predictive of a left ventricle dysfunction selected from the group consisting of : A2M, , ADSL, AEBP1 , ANXA1 , ANXA10, ANXA1 1 , ANXA2, , ATP1 B3, AZGP1 , , BXDC2, C1 GALT1 C1 , C1 orf63, C1 R, C6orf203, CAB39, CABYR, CBX5, CCNG1 , CKM, COL6A3, CXX1 , DMPK, DNAJB1 1 , DXS9879E, DYNLL1 , EDG1 , EEF1 A1 , EEF1 B2, EEF2, EFEMP1 , ERP29, FHL2, FLJ20152, FXYD1 , G6PD, GOT1 , GUK1 , HCA1 12, HLA-A, HLA-DRB4, HSPA4L, IFITM1 , IFITM2, KCNA10, KCNJ8, KCNK10, KCNQ1 , KIAA0859, KLF13, LENG8, LGMN, LMNA, LMOD3, LOC284393, LOC649550, LRRFIP2, LTBP2, MGP, MMP2, MYL5, MY015A, NIFIE14, NKX2-5, NRN1 , PCOLCE, PDK4, PKM2, POPDC2, PRAF2, PRDX1 , PSMB1 , PSMB10, PTMAP1 , PTP4A2, RANBP1 , RPL12, RPL18, RPL3, RPL35, RPLPO, RPN2, RPS12, RPS19, RPS2, RPS6, RPSA, SCNN1 A, SLC1 A3, SLC40A1 , SMG1 , SPARC, SPP1 , STAT6, TLN1 , TNFRSF17, TNNI1 , TPM2, TTC25, VIM, WIF1 , ZBTB16 and ZC3H7A, and
(ii) one or more markers predictive of a right ventricle dysfunction selected from the group consisting of : ACAA1 , ACADM, ACTC, C21 orf33, CAV1 , CCR2, CD36, CDH13, CDH9, CFL1 , CFL2, , CLIC5, COL4A5, CSDE1 , DBI, DSTN, EIF4A2, EPAS1 , FABP4, FGF12, FHL1 , FKBP5, FLJ22655, FLNC, GBAS, GLUL, GPNMB, GPR133, HADHB, HADHSC, HTRA1 , IFI16, IGFBP5, JAK2, KCTD15, LIN10, LM07, LOC220729, ME2, MGST3, NDUFB4, NEXN, NEXN, PALLD, , PCOLCE2, PDIA3, PKIA, PLN, POLR2L, PPGB, PPP2CB, PRKAA2, PRKAG2, RGS5, SCD, SCNN1 D, SDHA, SLC6A6, SLC9A3R2, SSR3, TXNIP, YWHAE and ZNF189.
3. The method according to claim 1 , wherein step b) comprises the steps of :
b1 ) providing two or more sets of nucleic acids, each nucleic acid contained in a set hybridizing specifically with a nucleic acid expression product of a heart-specific marker gene described in the present specification;
b2) reacting the sets of nucleic acids provided at step b1 ) with nucleic acid expression products that are previously extracted from the cardiac tissue sample provided at step a); b3) detecting and quantifying the nucleic acid complexes formed between (i) the sets of nucleic acids provided at step b1 ) and (ii) the nucleic acid expression products that are extracted from the cardiac tissue sample provided at step a);
4. A kit for the in vitro diagnosis of an advanced heart failure, or for prognosis of the outcome of an advanced heart failure, in an individual, which kit comprises means for quantifying the expression level of one or more marker genes that are indicative of an advanced heart failure, which marker genes are selected from the group consisting of :
5. A method for adapting a pharmaceutical treatment in a patient affected with an advanced heart failure comprising the steps of :
a) performing, on at least one cardiac tissue sample collected from the said patient, the in vitro diagnosis or prognosis method according to claim 1 ;
b) adapting the pharmaceutical treatment of the said patient.
PCT/EP2009/063297 2009-10-12 2009-10-12 A method for the diagnosis or prognosis of an advanced heart failure Ceased WO2011044927A1 (en)

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US20140031411A1 (en) * 2010-12-01 2014-01-30 Max-Delbruck-Centrum Fur Molekulare Medizin Berlin-Buch Gpnmb/osteoactivin as a biomarker and drug target in cardiac diseases
US10359433B2 (en) 2012-06-19 2019-07-23 Biouniversa S.R.L. BAG3 as biochemical serum and tissue marker
EP2861754A1 (en) * 2012-06-19 2015-04-22 Biouniversa S.r.l. Bag3 as biochemical serum and tissue marker
RU2554056C1 (en) * 2013-09-03 2015-06-20 Федеральное государственное бюджетное учреждение "Федеральный Центр сердца, крови и Эндокринологии имени В.А. Алмазова" Министерства здравоохранения Российской Федерации Set of synthetic oligonucleotides for identifying nucleotide sequence of coding part of nkx2,5, cfc1, gata4 genes and recognising mutations associated with orphan single-gene pathology underlying familiar congenital heart disease
WO2015168145A1 (en) * 2014-04-28 2015-11-05 Penn Marc S Sdf-1 delivery for treating advanced ischemic cardiomyopathy
US11744478B2 (en) 2015-07-30 2023-09-05 Medtronic, Inc. Absolute intrathoracic impedance based scheme to stratify patients for risk of a heart failure event
WO2017026733A1 (en) * 2015-08-12 2017-02-16 중앙대학교 산학협력단 Composition for diagnosing, preventing, or treating vascular smooth muscle cell proliferative diseases using fgf12
US11219664B2 (en) 2015-08-12 2022-01-11 Chung-Ang University Industry Academic Cooperation Foundation Composition for diagnosing, preventing, or treating vascular smooth muscle cell proliferative diseases using FGF12
WO2017099838A1 (en) * 2015-12-11 2017-06-15 The United States Of America, As Represented By The Secretary, Department Of Health And Human Services Gene therapy for combined methylmalonic acidemia/aciduria and hyperhomocysteinemia/homocystinuria, cobalamin c type, and deficiency of mmachc
US11510998B2 (en) 2015-12-11 2022-11-29 The United States Of America, As Represented By The Secretary, Department Of Health And Human Services Gene therapy for combined methylmalonic acidemia/aciduria and hyperhomocysteinemia/homocystinuria, cobalamin C type, and deficiency of MMACHC
EP4458988A3 (en) * 2017-07-05 2025-02-19 The Regents of the University of California Assay for pre-operative prediction of organ function recovery
EP4153995A4 (en) * 2020-05-19 2024-10-23 Falcon Bioscience, LLC DETECTION AND TREATMENT OF HEALTH PROBLEMS CHARACTERIZED BY LACK OF PERFUSION
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CN116327939A (en) * 2022-10-28 2023-06-27 中国人民解放军北部战区总医院 Medical application of AMPK gamma 2 protein in preventing or treating heart failure after myocardial infarction
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