WO2016075232A1 - Signature génique associée à une tolérance à une allogreffe rénale - Google Patents

Signature génique associée à une tolérance à une allogreffe rénale Download PDF

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WO2016075232A1
WO2016075232A1 PCT/EP2015/076426 EP2015076426W WO2016075232A1 WO 2016075232 A1 WO2016075232 A1 WO 2016075232A1 EP 2015076426 W EP2015076426 W EP 2015076426W WO 2016075232 A1 WO2016075232 A1 WO 2016075232A1
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subject
expression profile
tolerant
graft
genes
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PCT/EP2015/076426
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Daniel Baron
Gérard RAMSTEIN
Sophie Brouard
Jean-Paul Soulillou
Magali Giral
Rémi HOULGATTE
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Institut National De La Sante Et De La Recherche Medicale
Universite De Nantes
Chu Nantes
Université De Lorraine
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Publication of WO2016075232A1 publication Critical patent/WO2016075232A1/fr

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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

  • the present invention concerns methods and tools for identifying patients tolerant to a kidney graft.
  • Transplantation is the treatment of choice for end-stage renal disease. Recent advances in immunosuppression have improved management of acute rejection and graft survival. However, due to their toxicity, these drugs have numerous deleterious side effects and only a marginal effect on long term rejection. Tolerance is thus increasing regarded as an ideal solution.
  • a biopsy of the grafted kidney allows, through the analysis of the presence or absence of several histological lesion types, for the precise evaluation of said grafted kidney functionality.
  • a biopsy is an invasive examination, which is not without danger for the grafted organ, and is thus usually avoided on grafted subjects that have stable biological parameters values.
  • the variability of the diagnosis, due to the subjectivity of the analysis is a drawback of the histological examination of biopsies.
  • the present invention arises from the unexpected finding by the inventors that a restricted set of 20 gene markers accurately discriminated tolerant grafted patients from stable grafted patients under immunosuppressive therapy with high sensitivity, specificity and reproducibility, both in the five cohorts used in the previous studies mentioned above and in a new independent cohort.
  • the inventors thus identified a specific restricted set of gene markers which allowed the identification of grafted subject for whom a progressive, total or partial withdrawal of immunosuppressive drugs is possible.
  • This set of gene markers has the advantage to be sufficiently small to enable a simple implementation.
  • the diagnosis could be performed from a blood sample, which is completely harmless for the tested grafted subject.
  • the present invention thus concerns a method for the in vitro diagnosis of a graft tolerant or non-tolerant phenotype, comprising:
  • Table 1 Main features of the 20 signature genes of the invention
  • a "graft tolerant phenotype” is defined as a state of tolerance of a subject to his/her graft.
  • a “state of tolerance” means that this subject (referred to as a “graft tolerant subject”) does not reject his/her graft in the absence of an immunosuppressive treatment with a well-functioning graft.
  • a "graft non-tolerant phenotype” refers to the absence in said subject of a state of tolerance, meaning that said subject (referred to as a “graft non-tolerant subject”) would, at the time of the diagnosis, reject its graft if the immunosuppressive treatment was withdrawn.
  • the population of graft tolerant subjects only includes subjects in a state of tolerance to their graft
  • the population of graft non-tolerant subjects thus includes all other subjects and is composed of a variety of different states: patients in acute rejection, patients already suffering from obvious chronic rejection, patients at the early non symptomatic stage of chronic rejection, but also stable patients, which cannot at this time be considered as tolerant but who may later develop a graft tolerant phenotype.
  • the mechanisms of tolerance are complex and still not elucidated, and the cellular and molecular processes of tolerance induction may require a prolonged lapse of time.
  • the population of graft tolerant subjects only includes subjects who have already reached a stable state of tolerance to their graft
  • the population of graft non-tolerant subjects is heterogeneous and includes all other subjects, i.e. both subjects in the process of developing acute or chronic rejection and subjects in the process of developing tolerance.
  • the present invention possesses two major interests:
  • graft non-tolerant i.e. patients that are not diagnosed as graft tolerant
  • tolerance is likely not a stable situation for "entire life” and reinstatement of an immunosuppressive treatment may be needed in some cases to prevent acute or chronic rejection.
  • said subject is a kidney transplanted subject.
  • kidney transplanted subject is a subject that was grafted with a non-syngeneic, including allogenic or even xenogenic, kidney.
  • Said kidney transplanted subject may further have been grafted with another organ of the same donor providing the kidney.
  • said kidney transplanted subject may further have been grafted with the pancreas, and optionally a piece of duodenum, of the kidney donor.
  • Immunosuppressive drugs that may be employed in transplantation procedures include azathioprine, methotrexate, cyclophosphamide, FK-506 (tacrolimus), rapamycin, corticosteroids, and cyclosporins. These drugs may be used in monotherapy or in combination therapies.
  • Subjects with primary kidney graft generally receive an induction treatment consisting of 2 injections of basiliximab (Simulect ® , a chimeric murine/human monoclonal anti IL2-Ra antibody commercialized by Novartis), in association with tacrolimus (PrografTM, Fujisawa Pharmaceutical, 0.1 mg/kg/day), mycophenolate mofetil (CellceptTM, Syntex Laboratories, Inc, 2 g/day) and corticoids (1 mg/kg/day), the corticoid treatment being progressively decreased of 10 mg every 5 days until end of treatment, 3 months post transplantation.
  • basiliximab Simulect ® , a chimeric murine/human monoclonal anti IL2-Ra antibody commercialized by Novartis
  • tacrolimus PrografTM, Fujisawa Pharmaceutical, 0.1 mg/kg/day
  • mycophenolate mofetil CellceptTM, Syntex Laboratories, Inc, 2 g/day
  • corticoids (1 mg/kg/day
  • Subjects with secondary or tertiary kidney graft, or subjects considered at immunological risk generally receive a short course of anti-thymocyte globulin (ATG) (7 days), in addition from day 0 with mycophenolate mofetil (CellceptTM, Syntex Laboratories, Inc, 2 g/day), and corticosteroids (1 mg/kg/day), then the steroids are progressively tapered of 10 mg every 5 days until end of treatment and finally stopped around 3 months post transplantation.
  • Tacrolimus PrografTM, Fujisawa Pharmaceutical
  • a “biological sample” may be any sample that may be taken from a grafted subject, such as a serum sample, a plasma sample, a urine sample, a blood sample, a lymph sample, or a biopsy. Such a sample must allow for the determination of an expression profile comprising or consisting of the 20 genes defined in the section "Gene markers".
  • Preferred biological samples for the determination of an expression profile include samples such as a blood sample, a lymph sample, or a biopsy.
  • the biological sample is a blood sample, more preferably a peripheral blood sample comprising peripheral blood mononuclear cells (PBMC). Indeed, such a blood sample may be obtained by a completely harmless blood collection from the grafted patient and thus allows for a noninvasive diagnosis of a graft tolerant or non-tolerant phenotype.
  • PBMC peripheral blood mononuclear cells
  • expression profile is meant a group of at least 20 values corresponding to the expression levels of the 20 genes defined in the section " Marker genes” above, optionally with further other values corresponding to the expression levels of other genes.
  • the expression profile consists of a maximum of 200, preferably 100, 75, 50, more preferably 40, 35, 30, 25, even more preferably 20 distinct genes, 20 of which being the 20 genes defined in the section " Marker genes” .
  • the expression profile consists of the 20 genes defined in the section "Marker genes" only, since this expression profile has been demonstrated to be particularly relevant for assessing graft tolerance/non-tolerance.
  • each gene expression level may be measured at the genomic and/or nucleic and/or proteic level.
  • the expression profile is determined by measuring the amount of nucleic acid transcripts of each gene.
  • the expression profile is determined by measuring the amount of each gene corresponding protein.
  • the amount of nucleic acid transcripts can be measured by any technology known by a man skilled in the art.
  • the measure may be carried out directly on an extracted messenger RNA (mRNA) sample, or on retrotranscribed complementary DNA (cDNA) prepared from extracted mRNA by technologies well-known in the art.
  • mRNA messenger RNA
  • cDNA retrotranscribed complementary DNA
  • the amount of nucleic acid transcripts may be measured using any technology known by a man skilled in the art, including nucleic microarrays, quantitative PCR, microfluidic cards, and hybridization with a labelled probe.
  • the expression profile is determined using quantitative PCR.
  • Quantitative, or real-time, PCR is a well-known and easily available technology for those skilled in the art and does not need a precise description.
  • the determination of the expression profile using quantitative PCR may be performed as follows. Briefly, the real-time PCR reactions are carried out using the TaqMan Universal PCR Master Mix (Applied Biosystems). 6 ⁇ cDNA is added to a 9 ⁇ PCR mixture containing 7.5 ⁇ TaqMan Universal PCR Master Mix, 0.75 ⁇ of a 20X mixture of probe and primers and 0.75 ⁇ water.
  • the reaction consisted of one initiating step of 2 min at 50°C, followed by 10 min at 95°C, and 40 cycles of amplification including 15 sec at 95°C and 1 min at 60°C.
  • the reaction and data acquisition can be performed using the ABI PRISM 7900 Sequence Detection System (Applied Biosystems).
  • the number of template transcript molecules in a sample is determined by recording the amplification cycle in the exponential phase (cycle threshold or C T ), at which time the fluorescence signal can be detected above background fluorescence.
  • cycle threshold or C T cycle threshold
  • the starting number of template transcript molecules is inversely related to C T .
  • the expression profile is determined by the use of a nucleic microarray.
  • the expression profile is determined by the use of the nucleic microarray of the invention, as defined in the section "Nucleic microarray” below.
  • the amount of gene corresponding protein can be measured by any technology known by a man skilled in the art, for example by employing antibody-based detection methods such as immunohistochemistry, enzyme-linked immunosorbent assay or western blot analysis, protein microarray, flow cytometry or flow lateral dipstick.
  • antibody-based detection methods such as immunohistochemistry, enzyme-linked immunosorbent assay or western blot analysis, protein microarray, flow cytometry or flow lateral dipstick.
  • the expression profile may be determined by the use of a protein microarray.
  • antibodies, aptamers, or affibodies microarrays can be used, more particularly antibodies microarrays.
  • the antibodies, aptamers, or affibodies are attached to various supports using various attachment methods, using a contact or non- contact spotter.
  • suitable supports include glass and silicon microscope slides, nitrocellulose, microwells (for instance made of a silicon elastomer).
  • two main technologies can be used: 1 ) direct labelling, single capture assays and 2) dual- antibody sandwich immunoassays.
  • proteins contained in one or more samples are labelled with distinct labels (generally fluorescent or radioisotope labels), hybridized to the microarray, and labelled hybridized proteins are directly detected.
  • label generally fluorescent or radioisotope labels
  • dual-antibody sandwich immunoassays the sample is hybridized to the microarray, and a secondary tagged antibody is added.
  • a third labelled (generally fluorescent or radioisotope label) antibody specific for the tag of the secondary antibody is then used for detection).
  • the determination of the presence of a graft tolerant or graft non-tolerant phenotype is carried out thanks to the comparison of the obtained expression profile with at least one reference expression profile in step (b).
  • a “reference expression profile” is a predetermined expression profile, obtained from a biological sample from a subject with a known particular graft state.
  • the reference expression profile used for comparison with the test sample in step (b) may have been obtained from a biological sample from a graft tolerant subject ("tolerant reference expression profile"), and/or from a biological sample from a graft non- tolerant subject ("non-tolerant reference expression profile").
  • tolerant reference expression profile a biological sample from a graft tolerant subject
  • non-tolerant reference expression profile is an expression profile of a long-term stable grafted subject under classical immunosuppressive therapy.
  • At least one reference expression profile is a tolerant reference expression profile.
  • at least one reference expression profile may be a non- tolerant reference expression profile.
  • the determination of the presence or absence of a graft tolerant phenotype is carried out by comparison with at least one tolerant and at least one non-tolerant reference expression profiles.
  • the diagnosis (or prognostic) may thus be performed using one tolerant reference expression profile and one non-tolerant reference expression profile.
  • said diagnosis is carried out using several tolerant reference expression profiles and several non-tolerant reference expression profiles.
  • the comparison of a tested subject expression profile with said reference expression profiles can be done using the PLS regression (Partial Least Square) which aim is to extract components, which are linear combinations of the explanatory variables (the genes), in order to model the variable response (eg: 0 if STA, 1 if TOL).
  • the PLS regression is particularly relevant to give prediction in the case of small reference samples.
  • the comparison may also be performed using PAM (predictive analysis of microarrays) statistical method.
  • a non supervised PAM 3 classes statistical analysis can thus be performed. Briefly, tolerant reference expression profiles, non-tolerant reference expression profiles, and the expression profile of the tested subject are subjected to a clustering analysis using non supervised PAM 3 classes statistical analysis.
  • a cross validation (CV) probability may be calculated (CV, 0
  • another cross validation probability may be calculated (CV non . t0
  • the diagnosis is then performed based on the CV, 0 i and/or CVnon-toi probabilities.
  • a subject is diagnosed as a tolerant subject if the CV, 0 i probability is of at least 0.5, at least 0.6, at least 0.7, at least 0.75, at least 0.80, at least 0.85, more preferably at least 0.90, at least 0.95, at least 0.97, at least 0.98, at least 0.99, or even 1 .00, and the CV non -toi probability is of at most 0.5, at most 0.4, at most 0.3, at most 0.25, at most 0.20, at most 0.15, at most 0.10, at most 0.05, at most 0.03, at most 0.02, at most 0.01 , or even 0.00. Otherwise, said subject is diagnosed as a graft non- tolerant subject.
  • the expression profile of a graft tolerant phenotype is as follows: the levels of expression of the genes TCL1 A, MZB1 , CD22, BLK, MS4A1 , CD79B, BLNK, FCRL2, IRF4, ID3, AKR1 C3, HINT1 , RFC4, ANXA2R, CD40, FCER2 and CTLA4 are increased and the levels of expression of the genes AKIRIN2, EPS15 and PLBD1 are decreased.
  • the levels of expression of the genes are respectively significantly increased or decreased. Additional parameters useful for the diagnosis
  • said methods may further comprise determining from a biological sample of the subject at least one additional parameter useful for the diagnosis.
  • additional parameter useful for the diagnosis are parameters that cannot be used alone for a diagnosis but that have been described as displaying significantly different values between tolerant grafted subjects and subjects in chronic or acute rejection and may thus also be used to refine and/or confirm the diagnosis according to the above described method according to the invention. They may notably be selected from:
  • PBMC peripheral blood mononuclear cells
  • standard biological parameters specific for said subject grafted organ type means biological parameters that are usually used by clinicians to monitor the stability of grafted subjects status and to detect graft rejection. These standard biological parameters specific for said subject grafted organ type usually comprise serum or plasma concentrations of particular proteins, which vary depending on the grafted organ type. However, these standard biological parameters specific for said subject grafted organ type are, for each organ type, well known of those skilled in the art.
  • standard biological parameters specific for kidney include serum or plasma urea and creatinine concentrations.
  • the serum creatinine concentration is usually comprised between 40 to 80 ⁇ / ⁇ for a woman and 60 to 100 ⁇ / ⁇ for a man, and the serum urea concentration between 4 to 7 mmol/l.
  • GTT gamma glutamyl transpeptidase
  • AST aspartate aminotransferase
  • ALT alanine aminotransferase
  • LDH lactate dehydrogenase
  • bilirubin total or conjugated
  • PBMC peripheral blood mononuclear cells
  • the percentage of CD4 + CD25 + T cells in peripheral blood lymphocytes which may be performed by any technology known in the art, in particular by flow cytometry using labelled antibodies specific for the CD4 and CD25 molecules.
  • the percentage of CD4 + CD25 + T cells in peripheral blood lymphocytes of a tolerant subject is not statistically different from that of a healthy volunteer, whereas it is significantly lower (p ⁇ 0.05) in a non-tolerant grafted subject.
  • the oligoclonal ⁇ families of a non-tolerant grafted subject express increased levels compared to a healthy volunteer of Th1 or Th2 effector molecules, including interleukin 2 (IL-2), interleukin 8 (IL-8), interleukin 10 (IL-10), interleukin 13 (IL- 13), transforming growth factor beta (TGF- ⁇ ), interferon gamma (IFN- ⁇ ) and perforin, whereas oligoclonal ⁇ families of a tolerant grafted subject do not express increased levels of those effector molecules compared to a healthy volunteer.
  • IL-2 interleukin 2
  • IL-8 interleukin 8
  • IL-10 interleukin 10
  • IL- 13 interleukin 13
  • TGF- ⁇ transforming growth factor beta
  • IFN- ⁇ interferon gamma
  • the analysis of PBMC immune repertoire consists advantageously in the qualitative and quantitative analysis of the T cell repertoire, such as the T cell repertoire oligoclonality and the level of TCR transcripts or genes.
  • the T cell repertoire oligoclonality may be determined by any technology enabling to quantify the alteration of a subject T cell repertoire diversity compared to a control repertoire.
  • said alteration of a subject T cell repertoire diversity compared to a control repertoire is determined by quantifying the alteration of T cell receptors (TCR) complementary determining region 3 (CDR3) size distributions.
  • TCR T cell receptors
  • CDR3 complementary determining region 3
  • the level of TCR expression at the genomic, transcriptional or protein level is preferably determined independently for each ⁇ family by any technology known in the art.
  • the level of TCR transcripts of a particular ⁇ family may be determined by calculating the ratio between these ⁇ transcripts and the transcripts of a control housekeeping gene, such as the HPRT gene.
  • a significant percentage of ⁇ families display an increase in their transcript numbers compared to a normal healthy subject.
  • a graft tolerant subject displays a T cell repertoire with a significantly higher oligoclonality than a normal healthy subject.
  • Such additional parameters may be used to confirm the diagnosis obtained using the expression profile of the invention.
  • certain values of the standard biological parameters may confirm a graft non- tolerant diagnosis: if the serum concentration of urea is superior to 7 mmol/l or the serum concentration of creatinine is superior to 80 ⁇ / ⁇ for a female subject or 100 ⁇ / ⁇ for a male subject, then the tested subject is diagnosed as not tolerant to his/her graft.
  • Another object of the invention concerns a nucleic acid microarray comprising nucleic acids specific for the 20 following genes: TCL1 A, MZB1 , CD22, BLK, MS4A1 ,
  • CD79B CD79B, BLNK, FCRL2, IRF4, ID3, AKR1 C3, HINT1 , RFC4, ANXA2R, CD40, FCER2,
  • CTLA4 CTLA4, AKIRIN2, EPS15 and PLBD1 .
  • a "nucleic microarray” consists of different nucleic acid probes that are attached to a substrate, which can be a microchip, a glass slide or a microsphere-sized bead.
  • a microchip may be constituted of polymers, plastics, resins, polysaccharides, silica or silica-based materials, carbon, metals, inorganic glasses, or nitrocellulose.
  • Probes can be nucleic acids such as cDNAs ("cDNA microarray”) or oligonucleotides (“oligonucleotide microarray”), and the oligonucleotides may be about 25 to about 60 base pairs or less in length.
  • a target nucleic sample is labelled, contacted with the microarray in hybridization conditions, leading to the formation of complexes between target nucleic acids that are complementary to probe sequences attached to the microarray surface. The presence of labelled hybridized complexes is then detected.
  • microarray hybridization technology is available to the man skilled in the art, such as those described in patents or patent applications US 5,143,854; US 5,288,644; US 5,324,633; US 5,432,049; US 5,470,710; US 5,492,806; US 5,503,980; US 5,510,270; US 5,525,464; US 5,547,839; US 5,580,732; US 5,661 ,028; US 5,800,992; WO 95/21265; WO 96/31622; WO 97/10365; WO 97/27317; EP 373 203; and EP 785 280.
  • the nucleic microarray is an oligonucleotide microarray comprising, or consisting of, 20 oligonucleotides specific for the 20 genes defined in the section "Marker genes" above.
  • the oligonucleotides are about 50 bases in length.
  • Suitable microarray oligonucleotides specific for the 20 genes defined in the section "Marker genes” above may be designed, based on the genomic sequences of these genes (defined in Table 1 above), using any method of microarray oligonucleotide design known in the art.
  • any available software developed for the design of microarray oligonucleotides may be used, such as, for instance, the OligoArray software (available at http://berry.engin.umich.edu/Oligoarray/), the GoArrays software (available at http://www.isima.fr/bioinfo/goarrays/), the Array Designer software (available at http://www.premierbiosoft.com/dnamicroarray/index.html), the Primer3 software (available at http://frodo.wi.mit.edu/primer3/primer3_code.html), or the Promide software (available at http://oligos.molgen.mpg.de/).
  • the OligoArray software available at http://berry.engin.umich.edu/Oligoarray/
  • the GoArrays software available at http://www.isima.fr/bioinfo/goarrays/
  • the Array Designer software available at http://www.premierbiosoft.com/dnamicro
  • the present invention also relates to a kit for the in vitro diagnosis of a graft tolerant phenotype, comprising at least one reagent for the determination of an expression profile comprising the 20 following genes: TCL1 A, MZB1 , CD22, BLK, MS4A1 , CD79B, BLNK, FCRL2, IRF4, ID3, AKR1 C3, HINT1 , RFC4, ANXA2R, CD40, FCER2, CTLA4, AKIRIN2, EPS15 and PLBD1 .
  • a reagent for the determination of an expression profile is meant a reagent which specifically allows for the determination of said expression profile, i.e. a reagent specifically intended for the specific determination of the expression level of the genes comprised in the expression profile.
  • This definition excludes generic reagents useful for the determination of the expression level of any gene, such as taq polymerase or an amplification buffer, although such reagents may also be included in a kit according to the invention.
  • a kit for the in vitro diagnosis of a graft tolerant or graft non-tolerant phenotype may further comprise instructions for determination of the presence or absence of a graft tolerant phenotype.
  • kit for the in vitro diagnosis of a graft tolerant phenotype may also further comprise at least one reagent for the determining of at least one additional parameter useful for the diagnosis such as standard biological parameters specific for said subject grafted organ type, phenotypic analyses of PBMC (notably the percentage of CD4 + CD25 + T cells in peripheral blood lymphocytes and the cytokine expression profile of T cells), and quantitative and/or qualitative analysis of PBMC immune repertoire (such as the T cell repertoire oligoclonality and the level of TCR transcripts).
  • PBMC notably the percentage of CD4 + CD25 + T cells in peripheral blood lymphocytes and the cytokine expression profile of T cells
  • quantitative and/or qualitative analysis of PBMC immune repertoire such as the T cell repertoire oligoclonality and the level of TCR transcripts.
  • the reagent(s) for the determination of an expression profile comprising, or consisting of, the 20 genes defined in the section "Marker genes" above, preferably include specific amplification primers and/or probes for the specific quantitative amplification of transcripts of the genes TCL1 A, MZB1 , CD22, BLK, MS4A1 , CD79B, BLNK, FCRL2, IRF4, ID3, AKR1 C3, HINT1 , RFC4, ANXA2R, CD40, FCER2, CTLA4, AKIRIN2, EPS15 and PLBD1 , and/or a nucleic microarray for the detection of the genes TCL1 A, MZB1 , CD22, BLK, MS4A1 , CD79B, BLNK, FCRL2, IRF4, ID3, AKR1 C3, HINT1 , RFC4, ANXA2R, CD40, FCER
  • the instructions for the determination of the presence or absence of a graft tolerant phenotype preferably include at least one reference expression profile.
  • at least one reference expression profile is a graft tolerant expression profile.
  • at least one reference expression profile may be a graft non-tolerant expression profile.
  • the present invention also concerns a method of treatment of a grafted subject, comprising:
  • step (ii) adapting the immunosuppressive treatment in function of the result of step (i).
  • Said adaptation of the immunosuppressive treatment may consist in:
  • the present invention also concerns a method for monitoring the suitability of an immunosuppressive treatment or absence of treatment in a kidney transplanted subject, comprising the steps of:
  • step c) optionally based on the comparison in step b), beginning, continuing or discontinuing an immunosuppressive therapy in said subject.
  • the method further comprises starting an immunosuppressive therapy weaning, in particular if said subject was under immunosuppressive therapy, or continuing the absence of immunosuppressive therapy, in particular if said subject was not under immunosuppressive therapy.
  • the method further comprises starting an immunosuppressive therapy, in particular if said subject was not under immunosuppressive therapy, or continuing, modifying or increasing an immunosuppressive therapy, in particular if said subject was under immunosuppressive therapy.
  • the present invention further concerns an immunosuppressive therapy, as defined in the section "Immunosuppressive therapy" above for use for the treatment of a kidney transplanted subject identified as a graft non-tolerant subject, comprising identifying the subject as a graft non-tolerant subject by:
  • step b) based on the comparison in step b), identifying the subject as a graft non- tolerant subject.
  • the present invention also concerns a method for treating a kidney transplanted subject with immunosuppressive therapy comprising the steps of: a) determining from a biological sample from the grafted subject an expression profile comprising the 20 following genes: TCL1 A, MZB1 , CD22, BLK, MS4A1 , CD79B, BLNK, FCRL2, IRF4, ID3, AKR1 C3, HINT1 , RFC4, ANXA2R, CD40, FCER2, CTLA4, AKIRIN2, EPS15 and PLBD1 ,
  • step b) treating the subject with immunosuppressive therapy as defined in the section "Immunosuppressive therapy" above if the comparison in step b) indicates that the subject has a graft non-tolerant phenotype.
  • the immunosuppressive therapy denotes an immunosuppressive composition
  • an immunosuppressive composition comprising at least one immunosuppressive drug, such as azathioprine, methotrexate, cyclophosphamide, FK-506 (tacrolimus), rapamycin, a corticosteroid, a cyclosporine, basiliximab, mycophenolate mofetil or antithymocyte globulin; or an immunosuppressive combination comprising at least two immunosuppressive drugs, such as at least two immunosuppressive drugs selected from the group consisting in azathioprine, methotrexate, cyclophosphamide, FK-506 (tacrolimus), rapamycin, a corticosteroid, a cyclosporine, basiliximab, mycophenolate mofetil and antithymocyte globulin.
  • an immunosuppressive combination comprising at least two immunosuppressive drugs, such as at least two immunosup
  • Another object of the invention concerns a method for identifying a kidney transplanted subject under immunosuppressive therapy as a candidate for immunosuppressive therapy weaning, comprising the steps of:
  • step b) identifying the subject as eligible to immunosuppressive therapy weaning if the comparison in step b) indicates that the subject has a graft tolerant phenotype.
  • immunosuppressive therapy weaning is meant herein the progressive reduction, and optionally eventually the suppression of an immunosuppressive therapy.
  • This example describes the identification of the expression profile of the graft tolerant phenotype and the confirmation of its potent use as a diagnosis marker of graft tolerant or non-tolerant subjects.
  • MIS minimal immunotherapy
  • the inventors performed two types of meta-analyses. The first captures in each individual dataset the clusters of differential genes between the two groups and identifies the overlap as a consensus gene set. The second relies on the integration of the different datasets as a single corpus of data and identifies, after an analysis similar to the one performed on the individual datasets, the clusters of differentially expressed genes. Reprocessing, integration and analysis
  • Integration of heterogeneous datasets is especially a problem of data scales and distributions and variation due to probe effects is larger than the variation due to arrays. For that reason, for each dataset, to ensure that all genes lie within the same dynamic range, the inventors applied a per gene standardization to ensure that all genes lie within the same dynamic range (same mean, same variance). This location (mean)- scale (variance) adjustment of the genes is one of the generally advisable methods performing well to remove experiment effects and it is assumed that this transformation, while trivially making data more comparable, do not remove any biological signal of interest.
  • This transformation while trivially making data more comparable, do not remove any biological signal of interest.
  • Pearson correlations between gene profiles are not impacted by this linear scaling, this natural transformation is also commonly used for classification of gene expression data.
  • g iiS /W,, s -
  • g iiS denotes the standardized expression measurements of gene / ' in sample S
  • M iiS is the (log 2 transformed) expression level of gene / ' in sample S before being standardized
  • M I:(S TA ) is the mean expression of gene / ' across STA samples
  • SD I:(S TA ) is the standard deviation of gene / ' computed across STA samples.
  • Hierarchical clustering was performed to investigate relations between gene expression profiles and samples with the Cluster program (Eisen et al. (1998) Proc Natl Acad Sci U S A 95: 14863-14868).
  • the clustering method employed was an average linkage with the uncentered correlation as a similarity metric. It was applied to the individual datasets (log 2 median centered data) and the consensus set (standardized data). Results were displayed (heatmaps and dendrograms) using the TreeView program.
  • Cluster program was used to partition the datasets.
  • the maximum number of iterations to reach stability was set to 1000 and the number of nodes was fixed to 10.
  • the inventors To gain biological insight into the clusters, the inventors also used the gene set analysis (GSA) approach (Subramanian et al. (2005) Proc Natl Acad Sci U S A 102: 15545-15550) to identify among a large collection of gene sets (MSigDB) those with a similar gene composition. Analyses were performed on the version 4 of the database using the collections C2 (curated gene sets) and C5 (GO gene sets). Finally, to highlight cell-specific gene subsets, the inventors performed a virtual microdissection analysis (VMDA) (Alizadeh et al.
  • VMDA virtual microdissection analysis
  • the resulting matrix M(i,j) was a Boolean matrix that indicated that a gene i (one of the 8224 genes) is a neighbor of j (one of the 595 genes).
  • This matrix was represented by a graph and a hierarchical clustering was used as a layout algorithm to highlight the structure of this graph, to filter and to reduce the number of visible elements, and to provide a condensed representation of strongly connected components (clusters).
  • the intra-cluster connectivity corresponds to the proportion of edges inside the cluster.
  • Connectivity corresponds to the number of distinct edges (or paths) that exist between each pair of genes. Accordingly, pairs of connected genes were iteratively joined to form dense nodes equivalent to clusters. Density of connected genes, defined as intra-cluster density, was then used as the cluster fitness measure and ranged from 0 [isolated genes] to 1 [fully connected genes]. Clusters with more than 2 genes and a density higher than a preset threshold of 0.5 were retained resulting in 284 good clusters gathering 1462 genes. Results were displayed as a network using Cytoscape (Shannon et al. (2003) Genome Res 13: 2498-2504). Each vertex of the graph corresponds to a cluster and its size is proportional to the number of genes it contains (3 to 101 genes).
  • Edges represent inter-cluster densities greater or equal to 0.2.
  • the graph was manually split into 6 meta-clusters which were interpreted using the plug-in BiNGO (Maere et al. (2005) Bioinformatics 21 : 3448-3449) to assess over-representation of GO categories in the biological network.
  • Rank According to gene patterns supported by rank-based differences between two biological situations (Feng et al. (2009) BMC Genomics 10: 41 1 ), the inventors also tried to discover coordinated gene expression comparable to the one observed in tolerance. To this end, samples from each GEO study were preprocessed using rank- based normalization (Tsodikov et al. (2002) Bioinformatics 18: 251 -260). For each dataset, all pairs of samples c and d (i.e. all possible combinations) were considered. Let one call p c and p d their respective profile formed by the expression values of a set G of genes.
  • G the reference pattern of 251 genes identified by student's t- test as the most differentially expressed (p ⁇ 0.005) between the TOL and STA groups of patients.
  • G comprises two subsets A ('positive') and B ('negative') related to the 168 genes over-expressed and the 83 genes under-expressed in the TOL group respectively.
  • the area under the ROC curve (AUC) was then used to measure for each computed vector V, how well genes were related to their corresponding binary labels (A and B). For each dataset, the pair (c,d) with the best AUC value was retained among which those having a value greater than 0.80 were selected. This threshold is statistically highly significant and corresponds to a q-value of 10 ⁇ 11 to observe such an AUC value when the labels are given at random.
  • the resulting 215 pairs (c,d) were used to create the expression matrix on the set G of genes. For each pair (c,d), data were log 2 transformed and median centered. Results were displayed by a heat-map using Treeview, except for 7 genes with more than 20% missing values and which were removed from the visualization. In addition, a text mining approach was applied on the 215 datasets to identify, in titles and summaries, significant bias (Fisher's exact test) of key key-word frequencies compared to the rest of the datasets.
  • Gene Selection In microarray analysis, gene selection is a crucial step for increasing the performances of classifiers.
  • the inventors used the T-test to rank genes according to their p-value and keep the top ranked genes.
  • Choice of the classifier As there is not a unique emerging classification method, the selection of a classifier is essential for prediction accuracy.
  • SVM classifier Support Vector machine (SVM), also known as maximum margin classifier, is a supervised machine learning technique. SVM was retained for the analysis of the classification performances. The inventors thus defined the positive samples as those belonging to the TOL class and the negative as those belonging to the STA class of patients. Basically, SVM maps input data points to construct maximal-marginal hyperplanes in higher dimensional space to classify data with the two class labels TOL and STA. The hyperplane is constructed using only the support vectors (i.e., data that lie on the margin) simultaneously minimizing the empirical classification error and maximizing the geometric margin. In addition, to solve the problems of data imbalance (none equilibrated effectives in the considered classes), the inventors used an ensemble of under-sampled SVMs based on data resampling and a majority vote for decisions on the 100 folds.
  • SVM Support Vector machine
  • Performance evaluation The inventors imposed the independence between the learning procedure and the test through cross-validation. They first reduced all samples to the expression values relative to the gene selection. They then iteratively partitioned the dataset into two parts: samples from five studies were used as a whole for learning the SVM model; samples from the sixth study were predicted using the classifier. For each study, the inventors then obtained classification accuracy and other common performance metrics: sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) (as disclosed in Table 3).
  • Table 3 Confusion matrix (2x2 contingency table) and common performance metrics calculated from it
  • the mean accuracy determined the fitness of this gene selection.
  • the performances of classification obtained on test datasets were also compared to the ones obtained on 1000 equivalent datasets (same size, same sample composition) made of TOL and STA samples randomly picked from the different studies. Independent confirmation of the results by real time PCR using a new collection of samples
  • the 30 stable recipients were randomly selected from a large cohort of 131 transplant recipients (Garrigue et al. (2014) Transplantation 97: 168-175) who had received a first and unique kidney transplant from a deceased donor and displayed a stable graft function (creatinemia ⁇ 150 ⁇ / ⁇ , proteinuria ⁇ 1 g/24 h and GFR>40 ml/min) under standard immunosuppression (tacrolimus or cyclosporine A for maintenance therapy) for at least 5 years.
  • 19 healthy volunteers HV
  • presenting normal blood formula and no infectious or other concomitant pathology for at least 6 months before the study were also used as controls.
  • RNA preparation and Reverse-transcription Total RNA was prepared from frozen PBMC samples using the Trizol method (Invitrogen, Cergy Pontoise, France) according to the manufacturer's instructions. RNA was quantified using a NanoDrop microvolume ND-1000 spectrophotometer (Thermo Scientific, Courtaboeuf, France). The integrity of the RNA samples was assessed using an Agilent 2100 Bioanalyzer (Agilent Technologies, Massy, France) with the Eukaryote Total RNA Nano assay. The RNA samples were stored at -80°C until needed.
  • cDNA complementary DNA
  • PCR Real Time Polymerase Chain Reaction
  • a k-means clustering identified 10 clusters (K1 to K10) in each study with clear functional annotations.
  • a total of 19 clusters out of the 50 were found significant between TOL and STA with at least one of the two tests (Student and/or Fisher, p ⁇ 0.01 ).
  • the 50 clusters had relevant but limited similarities. Accordingly, a focus on the 19 clusters indicated that most of the differential genes do not replicate in another studies. Only 0.74% (14 genes: APOM, ARHGAP17, AURKB, IGBP1 , IL10RA, IL15RA, IL1 RL1 , INSM1 , IRF4, MAOA, MICB, SMAD3, TK1 , YPEL2) was in fact commonly identified across the five studies. Two of them (TK1 , IRF4) were present either in the footprint of 49 genes from Brouard or the list of the 30 top genes from Newell. These 14 genes did not accurately discriminate TOL from STA samples (Table 5).
  • Meta-analysis by integration of the datasets identified a statistically and functionally relevant gene signature of tolerance:
  • K1 224 genes linked to proliferation
  • K2 183 genes
  • K10 188 genes
  • B and T lymphocyte activation and differentiation
  • GSA Gene set analysis
  • Virtual microdissection analysis (VMDA) revealed the clear participation of B, CD4 T lymphocytes and monocytes in tolerance:
  • the TOL signature (K1 , K2 and K10) was compared to clusters from various tissue and blood cell samples.
  • TOL and HV were closely related as no (0 gene, p ⁇ 0.001) or minor difference (3 genes, p ⁇ 0.05) could be detected in meta-analysis ( Figure 2) and RT- PCR set ( Figure 3).
  • This similarity between TOL and HV was reinforced by the observation that only 68 genes out of the 1846 analyzed were differential (p ⁇ 0.001).
  • 18 of the markers (90%) also displayed differential expression between STA and HV, both in the meta-analysis ⁇ p ⁇ 0.001) and in the RT-PCR ⁇ p ⁇ 0.05) sets ( Figures 2 and 3). Altogether, these data show that TOL and HV display roughly the same "healthy" profile.
  • the present inventors defined a gene signature thanks to the meta-analysis of blood transcriptome studies comparing TOL group with the more related group of STA patients. This group of patients was chosen because they represent the most appropriate cohort to look at tolerance markers to identify the patients who may benefit from an IS weaning protocol in the future.
  • the inventors To assess the reliability of the signature, the inventors first performed a full cross- validation procedure. This analysis yielded good predictions and enabled to validate a selection of the top-20 markers, mostly centred on B cells, as accurately discriminating tolerant from stable recipients. In a second step, these 20 markers were experimentally revalidated in an independent cohort of new TOL samples, from which 6 corresponded to new cases. These results showed that the initial findings of the inventors were not dependant on the technology used or the analyzed set of samples. Both analyses yielded good prediction performances (more than 90%). Hence, the biomarkers of the invention could be reliably used to detect tolerance and stratify kidney recipients in clinics. First they may help for a better follow-up of the tolerant patients.

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Abstract

La présente invention concerne un procédé pour le diagnostic in vitro d'un phénotype tolérant ou non tolérant vis-à-vis d'une greffe, comprenant la détermination à partir d'un échantillon biologique de sujet greffé d'un profil d'expression comprenant les 20 gènes suivants : TCL1A, MZB1, CD22, BLK, MS4A1, CD79B, BLNK, FCRL2, IRF4, ID3, AKR1C3, HINT1, RFC4, ANXA2R, CD40, FCER2, CTLA4, AKIRIN2, EPS15 et PLBD1 ; la comparaison du profil d'expression obtenu avec au moins un profil d'expression de référence, et la détermination du phénotype tolérant vis-à-vis de la greffe ou non tolérant vis-à-vis de la greffe à partir de ladite comparaison.
PCT/EP2015/076426 2014-11-12 2015-11-12 Signature génique associée à une tolérance à une allogreffe rénale WO2016075232A1 (fr)

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Publication number Priority date Publication date Assignee Title
WO2018015551A1 (fr) * 2016-07-22 2018-01-25 INSERM (Institut National de la Santé et de la Recherche Médicale) Procédés permettant de distinguer un sujet tolérant
JP2019527062A (ja) * 2016-07-22 2019-09-26 インセルム（インスティチュート ナショナル デ ラ サンテ エ デ ラ リシェルシェ メディカル） 寛容な対象を識別するための方法
US11479817B2 (en) 2016-07-22 2022-10-25 INSERM (Institut National de la Santé et de la Recherche Médicale Methods for discriminating a tolerant subject

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