Non-invasive diagnosis of subclinical rejection

Abstract

The present invention described herein relates to a method for diagnosing latent rejection based on the level, amount or concentration of two genes, AKR1C3 and TCL1A, whether they are independent or combined.In the case of combination, the level, amount or concentration of two genes can be further combined with clinical parameters in the form of a composite score.The present invention also provides a method for treating latent rejection in a subject when / when the subject is diagnosed with latent rejection using the method of the present invention, as well as a computer system and a kit of parts for implementing the method for diagnosing latent rejection.

Description

[Technical field]

[0001] The present invention relates to the field of subclinical rejection (SCR) and provides means and methods for diagnosing SCR in a routine manner using non-invasive markers. [Background technology]

[0002] In kidney transplantation, subclinical rejection (SCR), especially subclinical antibody-mediated rejection (sABMR), is a major threat associated with unfavorable allograft outcomes (Filippone & Farber, 2020. Transplantation; Loupy et al., 2015. J Am Soc Nephrol. 26(7):172131; Mehta et al., 2016. Transplantation. 100(8):16108; Rush & Gibson, 2019. Transplantation. 103(6):e139e145; Shishido et al., 2003. J Am Soc Nephrol. 14(4):104652).

[0003] Nevertheless, although the diagnosis of acute and chronic rejection can be suspected clinically from allograft dysfunction and confirmed histologically, by definition, SCR is associated with stable graft function during which graft disease is established. Therefore, its diagnosis cannot rely on traditional renal function measurements such as serum creatinine or glomerular filtration rate. Surveillance biopsies within the first year of follow-up are advocated in routine practice to diagnose, prevent, and ultimately treat early asymptomatic disease (Hoffman et al., 2019. Transplantation. 103(7):14571467; Loupy et al., 2015. J Am Soc Nephrol. 26(7):172131; Moreso et al., 2004. Transplantation. 78(7):10648; Nankivell et al., 2004. Transplantation. 78(2):2429; Rush et al., 1998. J Am Soc Nephrol. 9(11):212934). However, graft biopsy is an intervention that carries risks (Fereira et al., 2004. Transplantation. 77(9):14756), which is not performed in all transplant centers (CouvratDesvergnes et al., 2019. NephrolDialTransplant. 34(4):703711; Mehta et al., 2017. ClinTransplant. 31(5)). Organ biopsy results can also be inaccurate, especially when the biopsy area is not representative of the health of the entire organ.

[0004] When 1-year surveillance biopsies are performed, half show normal or subnormal histology and SCR represents only 25% of cases (Couvrat Desvergnese tal., 2019. Nephrol Dial Transplant. 34(4):703711; Loupye tal., 2015. JAmSoc Nephrol. 26(7):172131; Nankivellet tal., 2004. Transplantation. 78(2):2429).

[0005] Therefore, there is a need for non-invasive biomarkers that do not only detect early SCR but also avoid invasive procedures for the majority of patients without severe histological lesions, without compromising graft function. Regardless of central biopsy practices, such non-invasive biomarkers could potentially be used as screening tools to guide the need for surveillance biopsies and improve patient management (Friedewald & Abecassis, 2019. Am J Transplant. 19(7):21412142).

[0006] Several biomarkers for SCR have been proposed so far, such as blood gene signatures (WO 2015 / 179777; WO 2019 / 217910; Crespo et al., 2017. Transplantation. 101(6):14001409; Friedewald et al., 2019. AmJTransplant. 19(1):98109; Van Loon et al., 2019. EBioMedicine. 46:463472; Zhang et al., 2019. JAmSocNephrol. 30(8):14811494).

[0007] Zhang published a 17-gene signature that could diagnose SCR and acute cellular rejection at 3 months after transplantation with a negative predictive value of 89% and a positive predictive value of 73% (Zhang et al., 2019. J Am Soc Nephrol. 30(8):14811494). Similarly, a 51-gene signature allows identification of SCR at 24 months after transplantation (Friedewald et al., 2019. Am J Transplant. 19(1):98109). However, both of these studies focused only on cellular and border zone rejection. Van Loon reported an 8-gene signature to diagnose only antibody-mediated rejection (Van Loon et al., 2019. EBioMedicine. 46:463472). Finally, a 17-gene signature from the kSort test has also been proposed to diagnose subclinical ABMR (sABMR) at 6 months (Crespo et al., 2017. Transplantation. 101(6):14001409), but was not validated in a large cohort of 1,134 patients (Van Loon et al., 2021. Am J Transplant. 21(2):740750). Thus, none of these signatures are currently in routine use yet.

[0008] Therefore, there remains a need for non-invasive biomarkers capable of detecting SCR in a routine manner.

[0009] Here, we show that two genes, TCL1A and AKR1C3, allow the identification of patients suffering from SCR independently of each other, and that the combination of both genes allows even better discrimination. We further propose a composite score based on the expression of TCL1A and AKR1C3 in combination with three clinical variables (experience of a rejection episode before blood collection, sex of the graft recipient, and intake of immunosuppressants, either cyclosporine A [CsA] or tacrolimus, at the time of blood collection) to identify patients free of SCR one year after transplantation. Summary of the Invention

[0010] The present invention provides a method for diagnosing subclinical renal rejection in a subject in need thereof, comprising: a) determining the level, amount, or concentration of at least one biomarker selected from the group consisting of TCL1A and AKR1C3 in a sample previously collected from the subject; b) comparing the level, amount or concentration of at least one biomarker with the level, amount or concentration of the same at least one biomarker determined in at least one reference subject, where at least one referent is: - Subjects who have not had a kidney transplant, - Kidney transplant recipients who do not have subclinical renal rejection, or - the subject is tested for subclinical renal rejection per se prior to renal transplantation; and c) concluding that the subject is suffering from subclinical renal rejection if the level, amount or concentration of the at least one biomarker is statistically significantly lower than the level, amount or concentration of the same at least one biomarker determined in at least one reference subject.

[0011] In some embodiments, step a) does not include determining the level, amount, or concentration of CD40, CTLA4, ID3, and / or MZB1. In some embodiments, step a) does not include determining the level, amount, or concentration of a biomarker other than TCL1A and / or AKR1C3.

[0012] In some embodiments, step a) comprises determining the level, amount, or concentration of TCL1A in a sample previously taken from the subject. In some embodiments, step a) comprises determining the level, amount, or concentration of AKR1C3 in a sample previously taken from the subject. In some embodiments, step a) comprises determining the level, amount, or concentration of both TCL1A and AKR1C3 in a sample previously taken from the subject.

[0013] In some embodiments, the level, amount, or concentration of at least one biomarker is expressed in terms of an absolute or relative level, amount, or concentration; preferably, it is expressed in terms of a relative level, amount, or concentration normalized to the level, amount, or concentration of one or several reference markers.

[0014] In some embodiments, the method comprises: a) determining a composite score according to the level, amount or concentration of at least one biomarker selected from the group consisting of TCL1A and AKR1C3, preferably both TCL1A and AKR1C3, wherein the composite score is established using formula (1):

number

[0015] In some embodiments, the method comprises: a) - the level, amount or concentration of at least one biomarker selected from the group consisting of TCL1A and AKR1C3, preferably both TCL1A and AKR1C3; and - - Experience of a rejection episode prior to blood collection The gender of the recipient, and · taking immunosuppressants (IS) at the time of blood collection, preferably tacrolimus or cyclosporine A (CsA) at the time of blood collection; determining a composite score by one, two or preferably three clinical parameters selected from: A composite score is established using formula (2):

number

[0016] In this embodiment, the ingestion of an immunosuppressant (IS) at the time of blood collection may be the ingestion of tacrolimus at the time of blood collection, or may be the ingestion of cyclosporine A (CsA) at the time of blood collection.

[0017] In this embodiment, the composite score is: - the level, amount, or concentration of both TCL1A and AKR1C3, and - Three clinical parameters: (i) previous rejection episode before blood collection, (ii) recipient sex, and (iii) intake of cyclosporine A (CsA) at the time of blood collection. can be determined by

[0018] In this embodiment, the subclinical renal rejection is subclinical T cell mediated renal rejection (sTCMR), subclinical antibody mediated renal rejection (sABMR) and / or mixed sTCMR / sABMR.

[0019] In some embodiments, the method comprises: a) - the level, amount or concentration of at least one biomarker selected from the group consisting of TCL1A and AKR1C3, preferably both TCL1A and AKR1C3; and - - Experience of a rejection episode prior to blood collection - the gender of the recipient, Allograft rank, and Number of HLA mismatches between donors and recipients determining a composite score by one, two, three or preferably four clinical parameters selected from: A composite score is established using formula (3):

number

[0020] In this embodiment, the subclinical renal rejection comprises subclinical antibody-mediated renal rejection (sABMR).

[0021] In some embodiments, the at least one reference subject is a reference population that includes two or more reference subjects.

[0022] In some embodiments, the method is computer-implemented.

[0023] The present invention also provides a computer system for diagnosing subclinical renal rejection in a subject in need thereof, comprising: i) at least one processor; ii) when executed by a processor, causes the processor to: receiving an input level, amount, or concentration of at least one biomarker selected from the group consisting of TCL1A and AKR1C3; b. analyzing and converting the input levels, amounts, or concentrations to derive a composite score established using formula (1) as set forth in claim 8; c. generating an output that is a composite score; and d. at least one storage medium storing at least one code readable by a processor for providing a diagnosis of whether the subject is suffering from subclinical renal rejection based on the output; and The present invention also relates to a computer system including the

[0024] In some embodiments, the at least one code readable by the processor, when executed by the processor, causes the processor to: a. Receive an input level, amount, or concentration of at least one biomarker selected from the group consisting of TCL1A and AKR1C3, and (i) experience of a rejection episode prior to blood collection; (ii) the recipient's sex, and (iii) inputting values ​​for one, two or preferably three clinical parameters selected from the intake of immunosuppressants (IS) at the time of blood collection, preferably the intake of tacrolimus or cyclosporine A (CsA) at the time of blood collection; b. analyzing and transforming the input levels, amounts, or concentrations and the input values ​​to derive a composite score established using formula (2) as defined in claim 9; c. generating an output that is a composite score; and d. providing a diagnosis of whether the subject is suffering from subclinical renal rejection based on the output.

[0025] In this embodiment, the ingestion of an immunosuppressant (IS) at the time of blood collection may be the ingestion of tacrolimus at the time of blood collection, or may be the ingestion of cyclosporine A (CsA) at the time of blood collection.

[0026] In this embodiment, the subclinical renal rejection is subclinical T cell mediated renal rejection (sTCMR), subclinical antibody mediated renal rejection (sABMR) and / or mixed sTCMR / sABMR.

[0027] In some embodiments, the at least one code readable by the processor, when executed by the processor, causes the processor to: receiving an input level, amount, or concentration of at least one biomarker selected from the group consisting of TCL1A and AKR1C3; (i) experience of a rejection episode prior to blood collection; (ii) the recipient's sex; (iii) previous transplantation, and (iv) entering values ​​for one, two, three, or preferably four clinical parameters selected from the number of HLA mismatches between the donor and the recipient; b. analyzing and transforming the input levels, amounts, or concentrations and the input values ​​to derive a composite score established using formula (3) as defined in claim 14; c. generating an output that is a composite score; and d. providing a diagnosis of whether the subject is suffering from subclinical renal rejection based on the output.

[0028] In this embodiment, the subclinical renal rejection comprises subclinical antibody-mediated renal rejection (sABMR).

[0029] In some embodiments, a subject is diagnosed as having subclinical renal rejection if the output is substantially higher than the same output obtained in at least one reference subject, where the reference subject is a subject who has not undergone a renal transplant, a renal transplant recipient who does not have subclinical rejection, or a subject who has been tested for subclinical rejection per se prior to renal transplantation.

[0030] The present invention also relates to a computer program comprising processor-readable software code adapted to, when executed by a processor, perform the computer-implemented method of diagnosing subclinical renal rejection disclosed herein.

[0031] The present invention also relates to a non-transitory computer readable storage medium comprising code that, when executed by a computer, causes a processor to perform a computer-implemented method for diagnosing subclinical renal rejection as disclosed herein.

[0032] The present invention also relates to a kit of parts for carrying out the method of diagnosing subclinical renal rejection disclosed herein, comprising means for determining the level, amount or concentration of at least one biomarker selected from the group consisting of TCL1A and AKR1C3, and optionally means for determining the level, amount or concentration of at least one reference marker, and instructions for carrying out the method.

[0033] In some embodiments, the means is selected from the group consisting of a nucleic acid probe, an antibody, and an aptamer.

[0034] definition In the present invention, the following terms have the following meanings:

[0035] "AKR1C3" refers to the gene encoding the aldo-ketoreductase family 1 member C3 protein. The naturally occurring human AKR1C3 gene has the nucleotide sequence set forth in Genbank Accession No. NM_001253908 (version 2 as of May 9, 2021), and the naturally occurring human aldo-ketoreductase family 1 member C3 protein has the amino acid sequence set forth in Genbank Accession No. NP_001240837 (version 1 as of May 9, 2021) or UniProt Accession No. P42330 (version 4 as of October 5, 2010).

[0036] "TCL1A" refers to the gene encoding T cell leukemia / lymphoma protein 1A. Naturally occurring human TCL1A gene has the nucleotide sequence set forth in Genbank Accession No. NM_001098725 (April 18, 2021, version 2), and naturally occurring human T cell leukemia / lymphoma protein 1A has the amino acid sequence set forth in Genbank Accession No. NP_001092195 (April 18, 2021, version 1) or UniProt Accession No. P56279 (July 15, 1998, version 1).

[0037] "Biological sample" refers to any sample obtained from a subject, preferably a transplant subject, such as a blood sample, serum sample, plasma sample, urine sample, lymph sample, or biopsy.

[0038] "Immunosuppressive therapy" or "immunosuppressive treatment" refers to the administration of one or more immunosuppressive drugs (or immunosuppressants) to a transplant subject. Immunosuppressive drugs that may be used in transplant treatment include all drugs listed in therapeutic subgroup L04 of the Anatomical Therapeutic Chemical Classification System (ATC / DDD Index 2021) developed by the World Health Organization (WHO) for the classification of drugs and other medical products. These are incorporated herein by reference. Further examples include, but are not limited to, the following: purine synthesis inhibitors (azathioprine, mycophenolic acid, mycophenolate mofetil), pyrimidine synthesis inhibitors (e.g., leflunomide, teriflunomide), folate antagonists (e.g., methotrexate), tacrolimus, cyclosporine, pimecrolimus, voclosporin, abetimus, gusperimus, immunomodulatory imid drugs (e.g., lenalidomide, pomalidomide, thalidomide, apremilast), IL1 receptor antagonists (e.g., anakinra), mTOR inhibitors (e.g., sirolimus, everolimus, ridaforolimus, temsirolimus, umirolimus, zotarolimus), anti-complement component 5 antibodies (e.g., eculizumab), anti-TNF antibodies (e.g., adalimumab, afelimomab, certolizumab pegol, golimumab, infliximab, nerelimomab), TNF inhibitors (e.g., etanercept, pegsunercept), anti-interleukin 5 antibodies (e.g., mepolizumab), VEGF inhibitors (e.g., aflibercept), anti-immunoglobulin E antibodies (e.g., omalizumab), anti-interferon antibodies ( For example, faralimomab), anti-interleukin 6 antibodies (for example, clazakizumab, ersilimomab, filgotinib), anti-interleukin 12 and / or interleukin 23 antibodies (for example, lebrikizumab, ustekinumab), anti-interleukin 17A antibodies (for example, secukinumab), interleukin 1 inhibitors (for example, rilonacept), anti-CD3 antibodies (for example, muromonab CD3, otelixizumab, teplizumab, visilizumab), anti-CD4 antibodies (for example, clenoliximab, keliximab, zanolimumab), anti-CD11a antibodies (for example, efalizumab), anti-CD18 antibodies (for example, erlizumab), anti-CD20 antibodies (for example, obinutuzumab,rituximab, ocrelizumab, pascolizumab), anti-CD23 antibodies (e.g., gomiliximab, lumiliximab), anti-CD40 antibodies (e.g., teneliximab, toralizumab), anti-CD62L / L-selectin antibodies (e.g., acelizumab), anti-CD80 antibodies (e.g., galiximab), anti-CD147 antibodies (e.g., gavilimomab), anti-CD154 antibodies (e.g., ruplizumab), anti-BLyS antibodies (e.g., belimumab, blisibimod), anti-CTLA4 antibodies (e.g., abatacept), CTLA4 fusions Proteins (e.g., abatacept, belatacept), anti-CAT antibodies (e.g., bertilimumab, lerdelimumab, metelimu- b), anti-integrin antibodies (e.g., natalizumab, vedolizumab), anti-interleukin 6 receptor antibodies (e.g., tocilizumab), anti-LFA1 antibodies (e.g., odulimomab), anti-interleukin 2 receptor antibodies (e.g., basiliximab, daclizumab, inolimomab), anti-CD5 antibodies (e.g., zolimomab aritoxin, zolimomab aritox), infusion of polyclonal antibodies (e.g., antithymocyte globulin, antilymphocyte globulin), and other monoclonal antibodies, such as atrolizumab, cedelizumab, fontolizumab, maslimomab, morolimumab, pexelizumab, reslizumab, rovelizumab, siplizumab, talizumab, telimomab aritox, vapaliximab, vepalimomab. These drugs can be used in monotherapy or combination therapy.

[0039] "Organ transplantation" refers to the procedure of replacing a diseased organ, organ part, or tissue with a healthy organ or tissue. The transplanted organ or tissue can come from the subject himself (called an "autograft"), from another human donor (called an "allograft"), or from an animal (then called a "xenograft"). The transplanted organ can be artificial or natural, whole (such as kidneys, hearts, and livers) or partial (such as heart valves, skin, and bones).

[0040] "Subclinical (renal) rejection" or "SCR" refers to lesions of the kidney graft defined histologically according to the Banff classification, typically identified by surveillance biopsy during post-transplant follow-up (an intervention that carries risks and is not performed in all transplant centers), but not associated with deterioration of kidney graft function (variably defined as serum creatinine levels not exceeding 10%, 20%, or 25% of baseline value, i.e., serum creatinine levels ranging from about 0.6 to about 1.2 mg / dL for adult males and about 0.5 to 1.1 mg / dL for adult females, although kidney transplant recipients typically have serum creatinine levels ranging from about 1.0 to about 1.9 mg / dL for adult males and about 0.8 to about 1.5 mg / dL for female subjects). It is clinically distinct from acute or chronic rejection, which is characterized by functional renal impairment measured by an elevation of serum creatinine above 10%, 20%, or 25% of the baseline value as defined above. SCR can be classified into two categories: one is a cellular response, primarily by cytotoxic T lymphocytes that are specifically activated against donor antigens and become capable of directly infiltrating, attacking, and damaging the transplanted organ, called "subclinical T cell-mediated rejection" or "sTCMR". The other is a humoral response, humoral, in which the immune system of the organ recipient produces donor-specific antibodies (DSA) against the donor organ, which results in immune attack and damage to the transplanted organ, called "subclinical antibody-mediated rejection" or "sABMR". These two immune mechanisms may also coexist, in which case they are referred to as "mixed sTCMR and sABMR" or "mixed SCR".

[0041] "Subject" refers to any mammal, including, but not limited to, humans, human primates (e.g., chimpanzees, other apes and monkey species, etc.), livestock (e.g., cows, horses, sheep, goats, and pigs, etc.), domestic animals (e.g., rabbits, dogs, and cats), and laboratory animals (e.g., rats, mice, and guinea pigs, etc.). The term does not denote a particular age or sex, unless otherwise specified. In particular, the subject is a human, also referred to as a "patient." In certain embodiments, the subject is a transplanted subject, also referred to as a "graft or transplant tissue recipient" or a "graft or transplanted subject."

[0042] "Transplanted subject" (or "graft or transplant recipient" or "grafted subject") refers to a subject who has received an organ transplant. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0043] The present invention relates to a method for diagnosing subclinical rejection in a subject in need thereof.

[0044] The term "diagnosis" and its conjugations refer to estimating or determining whether a subject has a given disease or condition, or estimating or determining the severity of a given disease or condition (e.g., subclinical rejection). Diagnosis does not require the ability to determine the presence or absence of a particular disease with 100% accuracy, or even that a particular course or outcome is more likely to occur than not. Instead, "diagnosis" refers to an increased probability that a subject has a particular disease or condition, compared to the probability that the subject does not have the particular disease or condition.

[0045] In one embodiment, the subject is a mammal, hi a particular embodiment, the subject is a human.

[0046] In one embodiment, the subject is a transplanted subject. In a particular embodiment, the subject is a kidney transplant recipient. In one embodiment, the kidney transplant recipient may further be grafted with a portion of the pancreas and / or duodenum of the kidney donor.

[0047] In one embodiment, the subject has had a kidney grafted about 1 month, 2 months, 3 months, 6 months, 9 months, or 1 year prior to performing the method of the invention.

[0048] In one embodiment, the subject is undergoing immunosuppressive therapy, ie, the subject is administered one or more immunosuppressant drugs.

[0049] In one embodiment, the subject does not exhibit impaired renal graft function. In one embodiment, the subject has a serum creatinine level of less than 3 mg / dL, less than 2.5 mg / dL, or less than 2 mg / dL. In one embodiment, the subject has a serum creatinine level in the range of about 0.5 to about 2.0 mg / dL.

[0050] In one embodiment, the subject is not suffering from acute rejection.

[0051] In one embodiment, the subject is a renal transplant recipient who is clinically immune tolerant. In one embodiment, the subject is a renal transplant recipient who is clinically immune tolerant. Means and methods for determining whether a renal transplant recipient is clinically immune tolerant are described in the art, in particular in WO 2018 / 015551 or Danger et al., 2017 (Kidney Int. 91(6):14731481).

[0052] In one embodiment, the subject is at risk for silent rejection. Examples of risk factors for silent rejection include, but are not limited to, immunosuppressive therapy, previous acute rejection, chronic allograft nephropathy (CAN), tissue incompatibility, degree of sensitization, donor age, etc.

[0053] In one embodiment, the method comprises providing a sample from a subject.

[0054] The term "sample" generally refers to any sample from a subject that can be tested for the expression level of a biomarker.

[0055] In one embodiment, the sample is a body tissue or fluid sample.

[0056] In one embodiment, the sample is a body tissue sample. A body tissue sample taken from a subject is also called a "biopsy". Examples of body tissues include, but are not limited to, kidney, liver, muscle, heart, lung, pancreas, spleen, thymus, esophagus, stomach, intestine, brain, nerve, testis, prostate, ovary, hair, skin, bone, breast, uterus, bladder, and spinal cord.

[0057] In one embodiment, the sample is a renal tissue sample.

[0058] In one embodiment, the sample is a bodily fluid, examples of which include, but are not limited to, blood, plasma, serum, lymph, peritoneal fluid, cyst fluid, urine, bile, nipple exudate, synovial fluid, bronchoalveolar lavage fluid, sputum, amniotic fluid, peritoneal fluid, cerebrospinal fluid, pleural fluid, pericardial fluid, semen, saliva, sweat, feces, stool, and alveolar macrophages.

[0059] In one embodiment, the sample is a bodily fluid selected from the group comprising or consisting of blood, plasma, and serum.

[0060] In one embodiment, the sample has been previously taken from the subject, i.e. the method of the invention does not include the step of taking a sample from the subject. Thus, according to this embodiment, the method of the invention is a non-invasive method or an "in vitro method".

[0061] In one embodiment, the method comprises determining the level, amount or concentration of at least one biomarker selected from the group comprising or consisting of TCL1A and AKR1C3 in the sample.

[0062] In one embodiment, the method comprises determining the level, amount or concentration of TCL1A in the sample.

[0063] In one embodiment, the method comprises determining the level, amount or concentration of AKR1C3 in the sample.

[0064] In one embodiment, the method comprises determining the level, amount or concentration of TCL1A and AKR1C3 in a sample.

[0065] In one embodiment, the method comprises determining the level, amount or concentration of up to two biomarkers selected from the group consisting of or including TCL1A and AKR1C3 in the sample.Thus, in one embodiment, the method does not comprise determining the level, amount or concentration of biomarkers other than TCL1A and / or AKR1C3 in the sample.In particular, the method does not comprise determining the level, amount or concentration of any of CD40, CTLA4, ID3 and MZB1.

[0066] In one embodiment, the level, amount, or concentration corresponds to the transcription level (ie, expression of mRNA) or translation level (ie, expression of the corresponding protein) of at least one biomarker.

[0067] In one embodiment, the level, amount or concentration of at least one biomarker is determined at the RNA level, i.e., transcription level. Methods for determining the transcription level of biomarkers are well known in the art. Examples of such methods include, but are not limited to, real-time quantitative PCR (qPCR), RT PCR, RT qPCR, hybridization techniques (e.g., using microarrays, NanoString® method, etc.), Northern blot, and combinations thereof, such as, but not limited to, hybridization of the amplicons obtained by RTPCR, sequencing (e.g., RNAseq, also known as next-generation DNA sequencing or "whole transcriptome shotgun sequencing" etc.), etc.

[0068] In one embodiment, the level, amount or concentration of at least one biomarker is determined at the protein level, i.e., translation level.Methods for determining the translation level of biomarkers are well known in the art.Examples of such methods include, but are not limited to, immunohistochemistry, multiplex methods (Luminex), Western blot, enzyme-linked immunosorbent assay (ELISA), sandwich ELISA, flow cytometry, fluorescence-linked immunosorbent assay (FLISA), enzyme immunoassay (EIA), radioimmunoassay (RIA), mass spectrometry (e.g., tandem mass spectrometry [MS / MS]), chromatography-mass spectrometry and combinations thereof), etc.

[0069] In one embodiment, the level, amount, or concentration may be expressed in terms of an absolute or relative level, amount, or concentration.

[0070] When expressed in terms of relative level, amount, or concentration, the level, amount, or concentration is normalized to the level, amount, or concentration of one or several reference markers. A "reference marker" may also be called a "housekeeping marker" and may be a "housekeeping gene" if the level, amount, or concentration of at least one biomarker is determined at the RNA level, or may be a "housekeeping protein" if the level, amount, or concentration of at least one biomarker is determined at the protein level. Thus, the term "housekeeping marker" refers to a gene or protein that is constitutively expressed and is necessary for basic maintenance and essential cell functions. Housekeeping markers are usually not expressed in a cell or tissue-dependent manner, and in most cases are expressed by all cells in a given organism. Housekeeping markers also have a relatively stable or stable expression, and therefore they act as suitable markers for normalizing the level, amount, or concentration of a biomarker of interest. Housekeeping markers and their use in data normalization are well known in the art.

[0071] In one embodiment, the method comprises determining a composite score according to the level, amount or concentration of at least one biomarker selected from the group comprising or consisting of the TCL1A and AKR1C3 biomarkers, preferably two of the TCL1A and AKR1C3 biomarkers, as described above.

[0072] In one embodiment, a composite score (hereinafter "SCR score") is established using the following formula (1):

number

[0073] In one embodiment, the regression coefficient β i is established using the following equation (4):

number

[0074] The term "odds ratio" as used herein refers to the strength of association between two events, particularly between a predictor and a given disease or condition, i.e., subclinical rejection. In other words, odds ratio can be defined as the ratio of the odds of a given disease or condition, i.e., subclinical rejection in the presence of a predictor, to the odds of the predictor in the absence of a given disease or condition (i.e., subclinical rejection, or vice versa). If the odds ratio is greater than 1, the two events are positively correlated. Conversely, if the odds ratio is less than 1, the two events are negatively correlated.

[0075] Odds ratios can be determined, for example, by univariate or multivariate logistic regression analysis of each predictor with a diagnosis of a given disease or condition, ie, subclinical rejection, as shown in the Examples section.

[0076] In one embodiment, the odds ratio can be the ratio of odds of a subject suffering from subclinical rejection.

[0077] The SCR score established using formula (1) is generally particularly suitable for diagnosing subclinical rejection, i.e., subclinical T cell-mediated renal rejection (sTCMR), subclinical antibody-mediated renal rejection (sABMR), and / or mixed sTCMR / sABMR, as demonstrated in Example 1 below.

[0078] Additionally or alternatively, the method includes determining an SCR score by: - the level, amount or concentration of at least one biomarker selected from the group comprising or consisting of the TCL1A and AKR1C3 biomarkers, preferably two of the TCL1A and AKR1C3 biomarkers, as described above; - 1, 2, 3 or 4 clinical parameters, preferably 3 or 4 clinical parameters.

[0079] In one embodiment, the clinical parameters are unselected: (i) the age of the kidney recipient subject at the time of testing, and (ii) the age of the kidney recipient subject at the time of transplantation.

[0080] In one embodiment, the clinical parameter is selected from the following: - Experience of a rejection episode prior to blood collection (yes / no); - recipient sex (M / F); - Intake of immunosuppressants (IS) at the time of blood collection (yes / no); - allograft rank, also referred to as previous transplant experience (no previous transplant / one or more previous transplants); and - Number of HLA-A, -B, and / or -DR mismatches (0–3 / >3).

[0081] In one embodiment, the clinical parameters are selected from: (i) experience of a rejection episode prior to blood collection (yes / no), (ii) recipient gender (M / F), and (iii) intake of immunosuppressants (IS) at the time of blood collection (yes / no).

[0082] In one embodiment, the ingestion of an immunosuppressant (IS) is ingestion of tacrolimus or ingestion of cyclosporine A (CsA). In one embodiment, the ingestion of an immunosuppressant (IS) is ingestion of tacrolimus. In one embodiment, the ingestion of an immunosuppressant (IS) is ingestion of cyclosporine A (CsA).

[0083] In one embodiment, the SCR score is established using formula (1) above, where: "β i ” represents the regression coefficient of each predictor i on the level, amount, or concentration of the biomarker and the clinical parameter; "X i ” represents the predictor variables for each predictor i in the level, amount, or concentration of the biomarker and the clinical parameter; "β0" represents the intercept of the equation).

[0084] In one embodiment, the SCR score is established using the following formula (2):

number

[0085] In one embodiment, the intake of immunosuppressant (IS) is intake of tacrolimus, and formula (2) reads as follows:

number

[0086] In one embodiment, the intake of an immunosuppressant (IS) is intake of cyclosporine A (CsA), and formula (2) reads as follows:

number

[0087] In one embodiment, the odds ratio is the odds ratio of subjects suffering from subclinical rejection, odds ratio TCL1A, odds ratio AKR1C3 , odds ratio レシピエントの性別 , and odds ratio タクロリムスの摂取 is less than 1.

[0088] In one embodiment, the odds ratio is the odds ratio of subjects suffering from subclinical rejection, the odds ratio 以前の拒絶反応エピソード and odds ratio CsAの摂取 is greater than 1.

[0089] In an exemplary embodiment, the odds ratio of a subject suffering from subclinical rejection is as defined in Table 3. By way of example, the odds ratio of a subject suffering from subclinical rejection is within the 95% confidence level defined in Table 3.

[0090] Alternatively, the odds ratio may be the ratio of odds that the subject does not suffer from subclinical rejection. According to this embodiment, for subjects suffering from subclinical rejection, the odds ratio defined above is reversed, i.e., the odds ratio TCL1A , odds ratio AKR1C3 , odds ratio レシピエントの性別 , and odds ratio タクロリムスの摂取 is greater than 1, and the odds ratio 以前の拒絶反応エピソード and odds ratio CsAの摂取 is expected to be less than 1.

[0091] The SCR score established using formula (2) is generally particularly suitable for diagnosing subclinical rejection, i.e., subclinical T cell-mediated renal rejection (sTCMR), subclinical antibody-mediated renal rejection (sABMR), and / or mixed sTCMR / sABMR, as demonstrated in Example 1 below.

[0092] In one embodiment, the SCR score is established using the following formula (3):

number

[0093] In one embodiment, the odds ratio is the odds ratio of subjects suffering from subclinical rejection, the odds ratio TCL1A , odds ratio AKR1C3 , and odds ratio レシピエントの性別 is less than 1.

[0094] In one embodiment, the odds ratio is the odds ratio of subjects suffering from subclinical rejection, the odds ratio 以前の拒絶エピソード , odds ratio 同種移植ランク , and odds ratio HLA不一致 is greater than 1.

[0095] Alternatively, the odds ratio may be the ratio of odds that the subject does not suffer from latent rejection. According to this embodiment, the odds ratio defined above for a subject suffering from latent rejection is reversed, i.e., the odds ratio TCL1A , odds ratio AKR1C3 , and odds ratio レシピエントの性別 is greater than 1, and the odds ratio 以前の拒絶反応エピソード , odds ratio 同種移植ランク , and odds ratio HLA不一致 is expected to be less than 1.

[0096] In an exemplary embodiment, the odds ratio for a subject not suffering from subclinical rejection is as defined in FIG. 15A.

[0097] The SCR score established using formula (3) is particularly suitable for diagnosing a particular subtype of subclinical rejection, namely subclinical antibody-mediated renal rejection (sABMR), as demonstrated in Example 2 below.

[0098] In one embodiment, the method includes a step of comparing the level, amount, or concentration of at least one biomarker to the level, amount, or concentration of the same at least one biomarker determined in at least one reference subject.

[0099] In one embodiment, the reference subject is the subject himself prior to kidney transplantation.

[0100] In one embodiment, the reference subject is a substantially healthy subject, preferably a subject who has not undergone a kidney transplant.

[0101] In one embodiment, the reference subject is a renal transplant recipient who is not suffering from subclinical rejection.

[0102] It is also contemplated that a median and / or mean level, amount or concentration of at least one biomarker may be calculated by including multiple samples from several reference subjects.

[0103] Thus, in one embodiment, the method includes a step of comparing the level, amount, or concentration of at least one biomarker to the median and / or mean level, amount, or concentration of the same at least one biomarker determined in a reference population.

[0104] In one embodiment, the reference population comprises or consists of two or more, e.g., 2, 5, 10, 20, 30, 40, 50 or more substantially healthy subjects, preferably two or more subjects who have not undergone a kidney transplant.

[0105] In one embodiment, the reference population comprises or consists of two or more, e.g., 2, 5, 10, 20, 30, 40, 50 or more, kidney transplant subjects who are not suffering from subclinical rejection.

[0106] In one embodiment, the method comprises measuring the level, amount, or concentration of at least one biomarker by: - the level, amount, or concentration of the same at least one biomarker determined in at least one reference subject as defined above, or the median and / or mean level of the level, amount, or concentration of the same at least one biomarker determined in a reference population as defined above; and - the level, amount, or concentration of the same at least one biomarker determined in at least one subject known to have subclinical rejection, or the median and / or mean level of the same at least one biomarker determined in a population of subjects known to have subclinical rejection and comparing the

[0107] Additionally or alternatively, the method includes the step of comparing the composite score to a reference composite score determined in at least one reference subject.

[0108] In one embodiment, the reference subject is the subject himself prior to kidney transplantation.

[0109] In one embodiment, the reference subject is a substantially healthy subject, preferably a subject who has not undergone a kidney transplant.

[0110] In one embodiment, the reference subject is a renal transplant recipient who is not suffering from subclinical rejection.

[0111] It is also contemplated that a median and / or mean reference composite score may be calculated by including multiple samples from multiple reference subjects.

[0112] Thus, in one embodiment, the method comprises comparing the composite score to a median and / or mean reference composite score determined in a reference population.

[0113] In one embodiment, the reference population comprises or consists of two or more, e.g., 2, 5, 10, 20, 30, 40, 50 or more substantially healthy subjects, preferably two or more subjects who have not undergone a kidney transplant.

[0114] In one embodiment, the reference population comprises or consists of two or more, e.g., 2, 5, 10, 20, 30, 40, 50 or more, kidney transplant subjects who are not suffering from subclinical rejection.

[0115] In one embodiment, the method comprises determining a composite score as follows: - a reference composite score determined in at least one reference subject as defined above or a median and / or mean reference composite score determined in a reference population as defined above; and - a composite score determined in at least one subject known to have subclinical rejection or a median and / or mean composite score determined in a population of subjects known to have subclinical rejection and comparing the

[0116] In one embodiment, the method includes concluding that the subject suffers from subclinical rejection based on the comparison in the previous step. Alternatively, the method may include concluding that the subject does not suffer from subclinical rejection based on the comparison in the previous step.

[0117] In one embodiment, the silent rejection is silent T cell mediated rejection (sTCMR) or silent antibody mediated rejection (sABMR). In one embodiment, the silent rejection is silent T cell mediated rejection (sTCMR). In one embodiment, the silent rejection is silent antibody mediated rejection (sABMR). In one embodiment, the silent rejection is mixed sTCMR / sABMR.

[0118] In one embodiment, if the level, amount or concentration of at least one of the biomarkers TCL1A and AKR1C3 is substantially lower than the level, amount or concentration of the same at least one biomarker determined in at least one reference subject defined above, or lower than the median and / or mean level, amount or concentration of the same at least one biomarker determined in a reference population defined above, the subject is concluded to be suffering from subclinical rejection.

[0119] In one embodiment, if the level, amount or concentration of TCL1A is substantially lower than the level, amount or concentration of TCL1A determined in at least one reference subject as defined above, or the median and / or mean level, amount or concentration of TCL1A determined in a reference population as defined above, the subject is concluded to be suffering from subclinical rejection.

[0120] In one embodiment, if the level, amount or concentration of AKR1C3 is substantially lower than the level, amount or concentration of AKR1C3 determined in at least one reference subject defined above, or the median and / or mean level, amount or concentration of AKR1C3 determined in a reference population defined above, the subject is concluded to be suffering from subclinical rejection.

[0121] In one embodiment, if the levels, amounts or concentrations of both the TCL1A and AKR1C3 biomarkers are substantially lower than the levels, amounts or concentrations of both TCL1A and AKR1C3 determined in at least one reference subject as defined above, or the median and / or mean levels, amounts or concentrations of both TCL1A and AKR1C3 determined in a reference population as defined above, the subject is concluded to be suffering from subclinical rejection.

[0122] By "substantially lower" it is meant that the absolute or relative level, amount, or concentration of a given biomarker is statistically significantly decreased compared to the same biomarker in at least one reference subject or reference population, e.g., is decreased by 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80% or more compared to the same biomarker in at least one reference subject or reference population.

[0123] In one embodiment, it is concluded that a subject is not suffering from subclinical rejection if the level, amount, or concentration of at least one of the biomarkers TCL1A and AKR1C3 is substantially equal to or higher than the level, amount, or concentration of the same at least one biomarker determined in at least one subject known to be suffering from subclinical rejection, or the median and / or mean level, amount, or concentration of the same at least one biomarker determined in a population of subjects known to be suffering from subclinical rejection.

[0124] In one embodiment, it is concluded that a subject is not suffering from subclinical rejection if the level, amount, or concentration of TCL1A is substantially equal to or higher than the level, amount, or concentration of TCL1A determined in at least one subject known to be suffering from subclinical rejection, or the median and / or mean level, amount, or concentration of TCL1A determined in a population of subjects known to be suffering from subclinical rejection.

[0125] In one embodiment, it is concluded that the subject is not suffering from subclinical rejection if the level, amount, or concentration of AKR1C3 is substantially equal to or higher than the level, amount, or concentration of AKR1C3 determined in at least one subject known to be suffering from subclinical rejection, or the median and / or mean level, amount, or concentration of AKR1C3 determined in a population of subjects known to be suffering from subclinical rejection.

[0126] In one embodiment, it is concluded that a subject is not suffering from subclinical rejection if the levels, amounts or concentrations of both the TCL1A and AKR1C3 biomarkers are substantially equal to or higher than the levels, amounts or concentrations of both TCL1A and AKR1C3 determined in at least one subject known to be suffering from subclinical rejection, or the median and / or mean levels, amounts or concentrations of both TCL1A and AKR1C3 determined in a population of subjects known to be suffering from subclinical rejection.

[0127] "Substantially equivalent" means that the absolute or relative level, amount, or concentration of a particular biomarker in at least one subject known to have subclinical rejection, or in a population of subjects known to have subclinical rejection, is not statistically significantly different from the level, amount, or concentration of the same biomarker, e.g., within ±10% of the level, amount, or concentration of the same biomarker in at least one subject known to have subclinical rejection, or in a population of subjects known to have subclinical rejection.

[0128] By "substantially elevated" is meant that the absolute or relative level, amount, or concentration of a particular biomarker is increased by a statistically significant amount compared to the same biomarker in at least one subject known to have subclinical rejection, or in a population of subjects known to have subclinical rejection, e.g., increased by 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80% or more compared to the same biomarker in at least one subject known to have subclinical rejection, or in a population of subjects known to have subclinical rejection.

[0129] Additionally or alternatively, if the composite score is substantially higher than a reference composite score determined in at least one reference subject as defined above, or substantially higher than the median and / or mean reference composite score determined in a reference population as defined above, it is concluded that the subject is suffering from subclinical rejection.

[0130] By "substantially increased" is meant that the composite score is statistically significantly increased as compared to the reference composite score of at least one reference subject or reference population, e.g., is increased by 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80% or more as compared to the reference composite score of at least one reference subject or reference population.

[0131] In one embodiment, if the composite score is less than or equal to the composite score determined in at least one subject known to have subclinical rejection, or less than or equal to the median and / or mean composite score determined in a population of subjects known to have subclinical rejection, it is concluded that the subject does not have subclinical rejection.

[0132] By "substantially equal" it is meant that the composite score is not statistically significantly greater than the composite score in at least one subject known to have subclinical rejection, or in a population of subjects known to have subclinical rejection, e.g., within ±10% of the composite score in at least one subject known to have subclinical rejection, or in a population of subjects known to have subclinical rejection.

[0133] By "substantially lower" is meant that the composite score is statistically significantly reduced compared to the composite score in at least one subject known to have subclinical rejection or in a population of subjects known to have subclinical rejection, e.g., a 10%, 20%, 30%, 40%, 50% or more reduction compared to the composite score in at least one subject known to have subclinical rejection or in a population of subjects known to have subclinical rejection.

[0134] The present invention also relates to a method of treating subclinical rejection in a subject in need thereof.

[0135] The term "treatment" and its conjugations refer to administering a treatment regimen to a subject to prevent, inhibit, delay or reverse the progression of a given disease or condition, and / or improve the clinical symptoms of a given disease or condition, and / or prevent the appearance of further clinical symptoms of a given disease or condition, such as subclinical rejection.In particular, subclinical rejection can be harmful to the graft, and if left untreated, can progress to chronic allograft nephropathy (CAN), chronic interstitial fibrosis and tubular atrophy, renal insufficiency, reduced creatinine clearance, chronic rejection, and ultimately reduced graft survival.

[0136] In one embodiment, the method comprises a first step of diagnosing subclinical rejection in a subject using the methods detailed above.

[0137] In one embodiment, the method includes a second step of treating the subject with immunosuppressive therapy if or when the subject is diagnosed with subclinical rejection in the first step.

[0138] In one embodiment, treating a subject diagnosed with subclinical rejection comprises reinitiating a previously completed immunosuppressive therapy.

[0139] In one embodiment, treating a subject diagnosed with subclinical rejection comprises increasing the dosage regimen of a currently administered immunosuppressive therapy.

[0140] In one embodiment, treating a subject diagnosed with subclinical rejection comprises modifying a currently administered immunosuppressive therapy to a more aggressive treatment.

[0141] In one embodiment, treating a subject diagnosed with subclinical rejection comprises administering an additional immunosuppressive therapy in addition to the immunosuppressive therapy currently being administered.

[0142] Examples of suitable immunosuppressive therapies are detailed fully earlier in this specification.

[0143] As an exemplary treatment protocol for immunosuppressive therapy, kidney transplant recipients typically receive induction treatment consisting of two injections of basiliximab in combination with tacrolimus (0.1 mg / kg / day), mycophenolate mofetil (2 g / day) and a corticosteroid (1 mg / kg / day), with the corticosteroid treatment tapered by 10 mg every 5 days until the end of treatment. More aggressive protocols include antithymocyte globulin (e.g., days 0-7) in combination with mycophenolate mofetil (2 g / day) and a corticosteroid (1 mg / kg / day) from day 0, followed by a short period of tacrolimus (0.1 mg / kg / day; e.g., from day 7 until the end of treatment), with the corticosteroid treatment tapered by 10 mg every 5 days until the end of treatment.

[0144] In one embodiment, treating a subject diagnosed with subclinical rejection involves removing or reducing the number of immunoglobulins in the subject, for example, by plasma exchange (PLEX) or administration of an IgG-degrading enzyme (such as imifidase), and optionally further administration of IVIg (intravenous immunoglobulin). This course of treatment may be particularly suitable when the subject has been diagnosed with antibody-mediated rejection (sABMR).

[0145] In one embodiment, treating a subject diagnosed with subclinical rejection includes administering antithymocyte globulin (ATG) and / or T cell depleting antibodies. This course of treatment may be particularly suitable when the subject is diagnosed with antibody-mediated rejection (sABMR).

[0146] In one embodiment, treating a subject diagnosed with subclinical rejection comprises performing surgical splenectomy, splenic embolization, and / or splenic irradiation of the subject's spleen.

[0147] In one embodiment, treating the subject diagnosed with subclinical rejection comprises administering a complement inhibitor.Some examples of complement inhibitors include, but are not limited to, C5 inhibitors (e.g., anti-C5 antibody eculizumab), or C1 esterase inhibitors.This treatment course may be particularly suitable when the subject is diagnosed with antibody-mediated rejection (sABMR).

[0148] For treatments specific to sABMR, see Schinstock et al., 2020 (Transplantation. 104(5):911922), the contents of which are incorporated herein by reference.

[0149] Those skilled in the art will readily appreciate that these courses of treatment are not exclusive and can be combined within the scope of the physician's sound medical judgment.

[0150] The present invention also relates to methods for identifying a subject receiving immunosuppressive therapy as a candidate for cessation or minimization of immunosuppressive therapy.

[0151] In one embodiment, the method comprises a first step of diagnosing subclinical rejection in a subject using the methods detailed above.

[0152] In one embodiment, the method includes a second step of reducing and eventually reducing the immunosuppressive therapy in the subject when the subject has not been diagnosed with or has not been diagnosed with subclinical rejection in the first step, and preferably when the subject has further been determined to be or is determined to be a clinically tolerant kidney transplant recipient. Means and methods for determining whether a kidney transplant recipient is clinically tolerant are described in the art, in particular in WO 2018 / 015551 or Danger et al., 2017 (Kidney Int. 91(6):14731481).

[0153] The present invention also relates to a computer system for diagnosing subclinical rejection in a subject in need thereof.The present invention also relates to a computer-implemented method for diagnosing subclinical rejection in a subject in need thereof.

[0154] As used herein, the term "computer system" refers to any and all devices that can store and process information and / or use the stored information to control the operation or execution of the device itself, regardless of whether such devices are electronic, mechanical, logical, or virtual in nature. The term "computer system" can refer to one computer, but also includes multiple computers working together to perform the functions described as being performed on or by the computer system. Methods implemented using a computer system are referred to as "computer-implemented methods."

[0155] In one embodiment, a computer system according to the present invention comprises: (ii) at least one processor; (iii) at least one computer-readable storage medium storing code readable by the processor.

[0156] As used herein, the term "processor" is meant to include any integrated circuit or other electronic device capable of performing operations on at least one instruction word, such as, for example, executing instructions, code, computer programs, and scripts accessed from a storage medium. However, the term "processor" is not to be construed as being limited to hardware capable of executing software, but refers to a processing device in a general sense, which may include, for example, a computer, a microprocessor, an integrated circuit, or a programmable logic device (PLD). A processor may include one or more graphics processing units (GPUs), whether utilized for computer graphics and image processing or other functions. Furthermore, instructions and / or data enabling the execution of the associated and / or resulting functions may be stored on any processor-readable medium, such as, but not limited to, an integrated circuit, a hard disk, a magnetic tape (such as floppy disks and zip diskettes), an optical disk (such as Blu-ray, CDs, and digital versatile disks), a flash memory (memory cards, and USB flash drives), a random access memory (RAM) (including dynamic RAM and static RAM), a read-only memory (ROM), or a cache. Instructions may be stored in hardware, software, firmware, or any combination thereof, among others.

[0157] Examples of processors include, but are not limited to, a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a general-purpose microprocessor, an application specific integrated circuit (ASIC), a field programmable logic array (FPGA), and other equivalent integrated or discrete logic circuitry.

[0158] In one embodiment, a computer system according to the present invention is linked to a scanner or the like which receives an experimentally determined signal related to the level, amount, or concentration of at least one biomarker including or selected from the group consisting of TCL1A and AKR1C3.

[0159] Alternatively, the level, amount or concentration of at least one biomarker selected from the group including or consisting of TCL1A and AKR1C3 in the sample expression level can be input by other means, optionally together with one, two or preferably three clinical parameters defined above.

[0160] The present invention also relates to a computer program comprising processor-readable software code adapted to perform the computer-implemented methods described herein when executed by a processor.

[0161] In one embodiment, a computer system according to the present invention includes at least one computer program. A computer program may include a set of instructions written to perform specified tasks and executable by a CPU of a digital processing device. The computer readable instructions may be implemented as program modules, such as functions, objects, application programming interfaces (APIs), data structures, etc., that perform particular tasks or implement particular abstract data types. Computer programs may be written in a variety of languages ​​in different versions.

[0162] In one embodiment, the computer program comprises, in part or in whole, one or more web applications, one or more mobile applications, one or more standalone applications, one or more web browser plug-ins, extensions, add-ins, or add-ons, or any combination thereof.

[0163] The present invention also relates to a computer readable storage medium containing code readable by a processor which, when executed by the processor, causes the processor to perform the steps of the computer-implemented methods described herein.

[0164] Examples of computer-readable storage media include, but are not limited to, integrated circuits, hard disks, magnetic tapes (such as floppy disks and zip diskettes), optical disks (Blu-ray, CDs and digital versatile disks), flash memory (memory cards and USB flash drives), random access memory (RAM) (including dynamic RAM and static RAM), read only memory (ROM) or cache.

[0165] In one embodiment, the computer readable storage medium is a non-transitory computer readable storage medium.

[0166] In one embodiment, the code stored on the computer readable storage medium, when executed by a processor of a computer system, causes the processor to: receiving an input level, amount, or concentration of at least one biomarker selected from the group including or consisting of TCL1A and AKR1C3; b. analyzing and transforming the input levels, amounts, or concentrations by consolidating and / or modifying each input level to derive at least one of a probability score, a fitting score, and a classification label; c. generating an output, the output being at least one of a classification label, a fitting score, and a probability score; and d. providing a diagnosis of whether the subject is suffering from subclinical rejection based on the output.

[0167] In one embodiment, at least one of the classification label, the fitting score, and the probability score is a composite score, the "SCR score," according to equation (1) defined above.

[0168] In one embodiment, the code stored on the computer readable storage medium, when executed by a processor of a computer system, causes the processor to: a. receiving an input level, amount or concentration of at least one biomarker selected from the group including or consisting of TCL1A and AKR1C3 and inputting values ​​of one, two, three or four clinical parameters, preferably three or four clinical parameters, as defined above; b. analyzing and transforming the input levels, amounts, or concentrations, and input values ​​by organizing and / or modifying each input to derive at least one of a probability score, a fitting score, and a classification label; c. generating an output, where the output is at least one of a classification label, a fitting score, and a probability score; and d. providing a diagnosis of whether the subject is suffering from subclinical rejection based on the output.

[0169] In one embodiment, at least one of the classification label, the fitting score, and the probability score is a composite score, the "SCR score," according to equation (2) defined above.

[0170] In one embodiment, at least one of the classification label, the fitting score, and the probability score is a composite score, the "SCR score," according to equation (3) defined above.

[0171] The present invention also relates to a kit of parts.

[0172] As used herein, the term "kit of parts" refers to an article of manufacture comprising one or more containers filled with one or more means or reagents for carrying out the methods according to the invention.

[0173] In one embodiment, the kit of parts comprises at least one means for determining the level, amount or concentration of at least one biomarker selected from the group comprising or consisting of TCL1A and AKR1C3 in a sample. Such means may be, for example, a probe for determining the level, amount or concentration of at least one biomarker at the RNA or protein level.

[0174] In one embodiment, the kit of parts comprises at least one means for determining the level, amount or concentration of at least one reference marker as described above. Such means may be, for example, a probe for determining the level, amount or concentration of at least one reference marker at the RNA or protein level.

[0175] In one embodiment, the kit of parts does not include means for determining the level, amount or concentration of any other biomarkers other than TCL1A and AKR1C3, and optionally at least one reference marker. In particular, the kit of parts does not include means for determining the level, amount or concentration of any of CD40, CTLA4, ID3, and MZB1.

[0176] Examples of probes for determining the level, amount, or concentration of at least one biomarker and / or at least one reference marker at the RNA level include, but are not limited to, nucleic acid probes (e.g., TaqMan™ probes, NanoString probes, Scorpions® probes, Molecular Beacons, and LNA® (Locked Nucleic Acid) probes).

[0177] Examples of probes for determining the level, amount, or concentration of at least one biomarker and / or at least one reference marker at the protein level include, but are not limited to, antibodies (e.g., anti-AKR1C3 antibodies and anti-TCL1A antibodies) and aptamers.

[0178] In one embodiment, the probes may be immobilized on a solid support, such as an array.

[0179] In one embodiment, the probe comprises at least one detectable label. Examples of suitable detectable labels include, but are not limited to, FAM (5 or 6 carboxyfluorescein), HEX, CY5, VIC, NED, fluorescein, FITC, IRD700 / 800, CY3, CY3.5, CY5.5, TET (5 tetrachlorofluorescein), TAMRA, JOE, ROX, BODIPY TMR, Oregon Green, Rhodamine Green, Rhodamine Green Red, Texas Red, Yakima Yellow, Alexa Fluor PET, BIOSEARCH BLUE™, MARINA BLUE™, BOTHELL BLUE™, ALEXA FLUOR™, 350 FAM™, SYBR™ Green 1, EvaGreen™, ALEXA FLUOR™ 488 JOE™, 25 VIC™, HEX™, TET™, CAL FLUOR™ Gold 540, YAKIMA YELLOW (registered trademark), ROX (trademark), CAL FLUOR (registered trademark) Red 610, Cy3.5 (trademark), TEXAS RED (registered trademark), ALEXA FLUOR (registered trademark) 568 CRY5 (trademark) QUASAR (trademark) 670, LIGHTCYCLER RED 640 (registered trademark), ALEXA FLUOR (registered trademark) 633 QUASAR (trademark) 705, LIGHTCYCLER RED 705 (registered trademark), ALEXA FLUOR (registered trademark) 680, SYT0 (registered trademark) 9, LC GREEN (registered trademark), LC GREEN (registered trademark) Plus+, and EVAGREEN (trademark).

[0180] Also disclosed herein:

[0181] E1: A method for diagnosing subclinical rejection in a subject in need thereof, a) determining the level, amount, or concentration of at least one biomarker selected from the group consisting of AKR1C3 and TCL1A in a sample previously collected from the subject; b) comparing the level, amount, or concentration of at least one biomarker with the level, amount, or concentration of the same at least one biomarker determined in at least one reference subject; c) concluding that the subject is suffering from subclinical rejection if the level, amount or concentration of the at least one biomarker is statistically significantly lower than the level, amount or concentration of the same at least one biomarker determined in at least one reference subject; The method includes:

[0182] E2: The method according to E1, wherein step a) comprises determining the level, amount or concentration of AKR1C3 in a sample previously taken from the subject.

[0183] E3: The method of E1, wherein step a) comprises determining the level, amount or concentration of TCL1A in a sample previously taken from the subject.

[0184] E4: A method according to any one of E1 to E3, wherein step a) comprises determining the level, amount or concentration of both AKR1C3 and TCL1A in a sample previously taken from the subject.

[0185] E5: The method of any one of E1 to E4, wherein the level, amount, or concentration of at least one biomarker is expressed in terms of an absolute or relative level, amount, or concentration; preferably expressed in terms of a relative level, amount, or concentration normalized to the level, amount, or concentration of one or several reference markers.

[0186] E6: a) - the level, amount or concentration of at least one biomarker selected from the group consisting of AKR1C3 and TCL1A, preferably both AKR1C3 and TCL1A; and - 1, 2 or preferably 3 clinical parameters determining a composite score by b) comparing the composite score with a reference composite score determined in at least one reference subject; c) concluding that the subject suffers from subclinical rejection if the composite score is substantially higher than a reference composite score determined in at least one reference subject; The method according to any one of E1 to E5, comprising:

[0187] E7: The method according to E6, wherein the clinical parameters are selected from (i) experience of a rejection episode prior to blood collection, (ii) gender of the recipient, and (iii) intake of cyclosporine A (CsA) at the time of blood collection.

[0188] E8: The method according to E6 or E7, wherein the composite score is established using the following formula:

number

[0189] E9: The method according to any one of E1 to E8, wherein at least one reference subject is a subject who has not undergone a kidney transplant and / or a kidney transplant recipient who is not suffering from subclinical rejection.

[0190] E10: The method according to any one of E1 to E8, wherein at least one reference subject is the subject himself / herself prior to kidney transplantation.

[0191] E11: The method according to any one of E1 to E10, wherein at least one reference subject is a reference population comprising two or more reference subjects.

[0192] E12: Provided herein is a computer system for diagnosing subclinical rejection in a subject in need thereof, comprising: i) at least one processor; ii) when executed by a processor, causes the processor to: receiving an input level, amount, or concentration of at least one biomarker selected from the group consisting of AKR1C3 and TCL1A; b. analyzing and transforming the input levels, amounts, or concentrations by consolidating and / or modifying each input level to derive at least one of a probability score, a fitting score, and a classification label; c. generating an output, where the output is at least one of a classification label, a fitting score, and a probability score; and d. providing a diagnosis of whether the subject is suffering from subclinical rejection based on the output. At least one storage medium storing at least one processor-readable code for causing the A computer system is disclosed that includes:

[0193] E13: At least one piece of code that is readable by the processor, when executed by the processor, causes the processor to: a. Receiving an input level, amount, or concentration of at least one biomarker selected from the group consisting of AKR1C3 and TCL1A, and inputting values ​​for one, two, or preferably three clinical parameters selected from: (i) experience of a rejection episode prior to blood collection, (ii) gender of the recipient, and (iii) intake of cyclosporine A (CsA) at the time of blood collection: b. analyzing and transforming the input levels, amounts, or concentrations, and input values ​​by organizing and / or modifying each input to derive at least one of a probability score, a fitting score, and a classification label; c. generating an output, where the output is at least one of a classification label, a fitting score, and a probability score; and d. The computer system of E12, wherein the computer system is configured to provide a diagnosis of whether the subject is suffering from subclinical rejection based on the output.

[0194] E14: The computer system of E13, wherein at least one of the classification label, the fitting score, and the probability score is a composite score as described in claim 8.

[0195] E15: A kit of parts for carrying out the method according to any one of E1 to E11, comprising means for determining the level, amount or concentration of at least one biomarker selected from the group consisting of AKR1C3 and TCL1A, and optionally means for determining the level, amount or concentration of at least one reference marker. [Brief description of the drawings]

[0196] [Figure 1] FIG. 1 is a flow diagram showing patient selection criteria for this study. [Diagram 2]Histological diagnosis of 450 biopsies evaluated from patients with stable function by qPCR gene expression. Histological features of kidney biopsies according to the 2015 Banff classification (Loupy et al., 2017. Am J Transplant. 17(1):2841), six histological classifications (normal, iIFTA, borderline, other, humoral and cellular rejection) and two groups (NR and SCR) are color coded in the upper panel. The lower panel shows the scaled -ΔΔCt values ​​from qPCR measurements of the six genes that make up the cSoT, with high and low gene expression represented in yellow and blue. [Figure 3A-3B] Two sets of graphs are shown, showing that cSoT score values ​​at 1-2 years are associated with renal function (MDRD). Figure 3A: In 450 patients, cSoT score is significantly associated with renal function (MDRD formula, in mL / min / 1.73 m2) at 12, 24, 36, and 48 months after transplantation, as shown by r Pearson correlation. P values ​​and the number of analyzed pairs are shown above and inside the bar graphs, respectively. Figure 3B: Dot plots represent function (MDRD) at 12 months after transplantation in correspondence with cSoT score values. [Figure 4A-4B] Figure 4A shows a set of two violin plots depicting the cSoT scores in the NR and SCR groups (Figure 4A) and each of the six histology groups (Figure 4B). Shown are p-values ​​from Student's t-test corrected for multiple testing comparing NR to SCR (Figure 4A), and Kruskal-Wallis p-values ​​with Dunn's post-hoc test comparing normal to other groups. [Figure 5A-5B] A set of two violin plots depicting AKR1C3 expression in the NR and SCR groups (Figure 5A) and each of the six histology groups (Figure 5B). Gene expression represents -ΔΔCt values ​​from qPCR measurements. Shown are p-values ​​from a Student's t-test corrected for multiple testing comparing NR to SCR (Figure 5A), and Kruskal-Wallis p-values ​​with Dunn's post-hoc test comparing the normal group to the other groups. [Figure 6A-6B]A set of two violin plots depicting TCL1A expression in the NR and SCR groups (Figure 6A) and each of the six histology groups (Figure 6B). Gene expression represents -ΔΔCt values ​​from qPCR measurements. Shown are p-values ​​from a Student's t-test corrected for multiple testing comparing NR to SCR (Figure 6A), and Kruskal-Wallis p-values ​​with Dunn's post-hoc test comparing normal to other groups. [Figure 7A-7B] Shown are two sets of violin plots depicting function (MDRD formula, in mL / min / 1.73 m2) 12 months post-transplant in the NR and SCR groups (Figure 7A) and in each of the six histology groups (Figure 7B). Shown are p-values ​​from a Student's t-test corrected for multiple testing comparing NR vs SCR (Figure 7A), and Kruskal-Wallis p-values ​​with Dunn's post-hoc test comparing normal vs other groups. [Figure 8A-8D] A set of four violin plots depicting cSoT score (Figure 8A), AKR1C3 expression (Figure 8B), TCL1A expression (Figure 8C) and function 12 months post-transplant (MDRD formula, in mL / min / 1.73 m2) (Figure 8D) in each of six histology groups from 150 patients with causative biopsy and / or 1-year serum creatine levels above 160 μmol / L. Gene expression represents -ΔΔCt values ​​from qPCR measurements. Kruskal-Wallis p-values ​​with Dunn's post-hoc test comparing normal tissue groups to other histology groups are shown. [Figure 9] Forest plot summarizing the logistic regression model of SRC risk. Values ​​indicate odds ratios, and * and *** represent p-values ​​<0.05 and <0.001, respectively. [Figure 10A-10B]A violin plot showing the composite score (SCR-s) values ​​for NR vs. SCR patients with t-test p-values ​​(Figure 10A) and a set of two graphs showing the ROC curves (thick black curves) showing the specificity and sensitivity of SCR-s, ​​three clinical parameters (logistic regression) (hatched curves), and function 12 months after transplantation (grey curves) (Figure 10B). p-values ​​from the ROC curve comparison using bootstrap tests with the same number of controls and cases as the original sample are shown. [Figures 11A-11C] A set of three graphs showing that the composite score SCR-s can distinguish sABMR and sTCMR patients from NR patients. The violin plots show SCR-s values ​​comparing NR to sABMR and sTCMR patients by Kruskal-Wallis with Dunn's post-hoc test comparing normal to sABMR and sTCMR (FIG. 11A). The corresponding ROC curves comparing normal to sABMR (FIG. 11B) and normal to sTCMR (FIG. 11C) are shown with the AUC. [Figure 12A-12B] A set of two violin plots depicting AKR1C3 expression in NR and SCR groups (FIG. 12A) and each of the six histology groups (FIG. 12B). Gene expression represents -ΔΔCt values ​​from qPCR measurements and the log2 of normalized counts on the NanoString scale. Shown are p-values ​​from a Student's t-test corrected for multiple testing comparing NR to SCR (FIG. 12A), and Kruskal-Wallis p-values ​​with Dunn's post-hoc test comparing normal to other groups. [Figure 13A-13B] A set of two violin plots depicting TCL1A expression in the NR and SCR groups (Figure 13A) and each of the six histology groups (Figure 13B). Gene expression represents -ΔΔCt values ​​from qPCR measurements and the log2 of normalized counts on the NanoString scale. Shown are p-values ​​from a Student's t-test corrected for multiple testing comparing NR to SCR (Figure 13A), and Kruskal-Wallis p-values ​​with Dunn's post-hoc test comparing normal to other groups. [Figure 14A-14D]A set of four violin plots showing that cSoT (Figure 14A), AKR1C3 (Figure 14B) and TCL1A (Figure 14C) expression levels were significantly decreased in blood from sAMR patients compared to other patients, while renal function (MDRD formula, in mL / min / 1.73 m2) was not significantly different between the two groups (Figure 14D). Gene expression represents -ΔΔCt values ​​from qPCR measurements. The p-values ​​of the Mann-Whitney test comparing the two groups are shown. [Figure 15A-15B] A series of graphs showing that four clinical parameters and two genes allow the identification of patients free of sAMR one year after transplantation. The forest plot in Figure 15A summarizes the logistic regression model for sABMR-s. Numbers indicate odds ratios, and *, **, *** represent p-values ​​<0.05, <0.01, <0.001, respectively. The violin plot in Figure 15B shows the sABMR-s values ​​for patients with sAMR compared to other patients by Mann-Whitney p-value in the first cohort, using qPCR (left) or NanoString (right). The dotted lines indicate the optimal thresholds (2.40 and 3.45 for qPCR and NanoString expressions, respectively). [Figures 16A-16C] A set of graphs demonstrating that blood gene expression is independent of the measurement method is shown. Two violin plots represent AKR1C3 (Figure 16A) and TCL1A (Figure 16B) expression in the sABMR group compared to other groups. AKR1C3 and TCL1A expression compared to NanoString values ​​using individual qPCR is shown in Figure 16C. Gene expression is expressed as -ΔΔCt values ​​for qPCR measurements and log2 of normalized counts for NanoString measurements. Mann-Whitney p-values ​​comparing sABMR to other sABMR are shown. [Figures 17A-17D]FIG. 17 is a set of four violin plots showing that immunosuppressive treatment does not change the discriminatory ability of sABMR-s. The violin plots show sABMR-s values ​​of sABMR patients compared to other patients depending on whether the patient is taking tacrolimus (FIG. 17A), corticosteroids (FIG. 17B), antiproliferatives (FIG. 17C), or depletion induction treatment (FIG. 17D). The dotted line indicates the optimal threshold (2.40). The p-values ​​of the Kruskal-Wallis test and Dunn's post-hoc test are shown.

[0197] Working Example The present invention is further illustrated by the following examples.

[0198] Example 1 material and method Study population This non-interventional research project involved the follow-up of kidney transplant patients at the University Hospital of Nantes (France), ParisNecker Hospital (France) and University Hospital of Lyon (France). These data were prospectively collected in the multicenter DIVAT database and approved by the French "National Commission on Informatics and Liberty" [CNIL] (DR2025087 N 914184, Feb. 15, 2015) and the French Ministry of Higher Education and Research (file 13.334cohort DIVAT RC12_0452, www.divat.fr).

[0199] For each patient, a blood sample was collected in a PAXgene™ tube (PreAnalytix, Qiagen, Hilden, Germany) at the time of a surveillance biopsy 1 year after transplantation. Samples were stored in the local Biological Resource Center (CRB) of the three participating hospitals and virtually interoperated on a common software (CENTAURE biocollection declared to the Ministry of Research under N PFS08017 since 13 August 2008; www.fondationcentaure.org). Each sample was linked to clinical data in the DIVAT database. Written informed consent was obtained for all patients. The reported clinical research activities are consistent with the principles of the Declaration of Istanbul and in line with the recommended practices of the University Hospital of Nantes.

[0200] A total of 600 single patients and consecutive patients met the inclusion criteria by combined PAXgene™ blood samples and surveillance biopsies at 1 year after transplant. Of these 600 patients, 450 had a mean eGFR (MDRD) of 57.79 ± 14.86 mL / min / 1.73 m after 1 year (serum creatinine level < 160 μmol / L; mean eGFR (MDRD) = 57.79 ± 14.86 mL / min / 1.73 m 2) and showed good function on protocol biopsy and would benefit from a non-invasive biomarker of SCR, but 150 patients underwent an indicated biopsy and had a 1-year serum creatinine value above 160 μmol / L (Figure 1). Clinical characteristics are summarized in Table 1: adults, renal transplant recipients between January 2008 and January 2016, ABO compatible, kidney transplant from a beating heart donor or deceased donor. We did not include patients who underwent multiorgan transplantation. Available data included recipient characteristics (age, sex, history of diabetes, history of cardiovascular disease and malignancy, initial renal disease (with or without recurrence), renal replacement therapy and cytomegalovirus (CMV) serology). Baseline transplant parameters were graft rank, cold ischemia time, number of HLA-AB-DR mismatches, pre-transplant donor-specific antibodies (DSA), induction therapy (depleting vs non-depleting) and delayed graft function (DGF, defined by the need for dialysis within 1 week after surgery). Donor characteristics involved age, sex, donor type (alive or deceased), and last serum creatinine level. Parameters collected in the first year after transplantation were serum creatinine level at 3 and 12 months after transplantation, number of rejection episodes, maintenance treatment at 12 months (cyclosporine A (CsA), tacrolimus, mTORi, MMF / MPA, steroids), and presence of de novo DSA at 12 months after transplantation. Follow-up and data collection were discontinued at the time of relapse to dialysis, death, or retransplantation.

[0201] Table 1: Characteristics of the 450 transplant patients Table 1 shows the clinical characteristics of the 450 patients who met the inclusion criteria, underwent paired protocol biopsy, and had blood RNA samples with normal function (serum creatinine levels <160 μmol / L) at 1 year after transplantation. [Table 1] TIFF2024522109000012.tif87159

[0202] Biopsy evaluation Renal biopsies were interpreted and reviewed by a renal pathologist at our institution according to the 2015 Banff classification (Loupy et al., 2017. Am J Transplant. 17(1):2841). The 450 protocol biopsies were classified into two groups (Figures 1 and 2). (1) SCR group (SCR, n = 45): biopsy with evidence of SCR, showing either antibody (sABMR, n = 33)-mediated rejection or T cell (sTCMR, n = 12)-mediated rejection, and patients were treated accordingly; (2) Non-rejection group (NR, n=405): Rejection biopsies with normal and subnormal histology showing biopsy interstitial fibrosis and tubular atrophy (IFTA) and inflammatory isolated IFTA grade 1 (iIFTA) were pooled and considered as normal biopsies (normal, n=342); biopsies with IFTA with inflammation grades 2 and 3 (iFIAT, n=9), biopsies with borderline changes in which the patient had not undergone treatment (borderline, n=18), and biopsies with other pathologies, such as recurrent glomerulonephritis (n=5) or de novo glomerulonephritis (n=3), BK virus nephropathy (n=2), and CNI toxicity (n=1) (other, n=36).

[0203] RNA isolation Peripheral blood samples were collected in PAXgene™ tubes (PreAnalytix), stored and shipped at 80°C. Total RNA extraction and purification was performed at the CRB of the University Hospital of Nantes using the PAXgene™ Blood miRNA Kit (Qiagen, Hilden, Germany) according to the manufacturer's protocol. Total RNA was quantified using a Nanodrop ND-1000 and 500 ng was used for real-time quantitative PCR (qPCR) and NanoString methods.

[0204] Gene expression of cSoT score For qPCR, cDNA was synthesized using the High Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific, Waltham; MA, USA). TaqMan® Fast Advanced master mix was used with a StepOnePlus™ real-time PCR system (Thermo Fisher Scientific) with a final volume of 10 μL. Gene expression was assessed using commercially available TaqMan® probes (listed in Table 2) and normalized by the geometric mean of the quantification cycle values ​​(Cq) of the B2M and HPRT1 reference genes. Gene expression was run in duplicate, and gene expression with a difference of more than 0.5 cycles or a Cq of more than 35 was measured again and discarded if inconsistent. Commercially available samples of total RNA from peripheral blood leukocytes (Takara Bio Europe SAS, Saint GermainenLaye, France) were used as calibrators to measure 2 -ΔΔCt Gene expression was calculated according to the method.

[0205] Table 2: Genes analyzed using NanoString and TaqMan® information. TtaqMan® probe reference (Thermo Fisher Scientific) and NanoString target sequences are shown for the 13 genes measured. [Table 2]

[0206] Six genes (AKR1C3, CD40, CTLA4, ID3, MZB1, and TCL1A) and six reference genes were measured using NanoString PlexSet™ Technology (NanoString Technologies, Seattle, WA, USA) according to the manufacturer's instructions. The MS4A1 gene (encoding CD20) was also measured in parallel.

[0207] These six genes were selected based on our previous work (WO 2018 / 015551; Danger et al., 2017. Kidney Int. 91(6):14731481), which described a composite score (cSoT) associated with spontaneous clinical immune tolerance in kidney transplant recipients (i.e., stable and acceptable graft function without immunosuppression over multiple years), including the expression levels of these six genes and two clinical parameters (age of the transplanted patient at the time of transplantation and blood collection).

[0208] Capture probes for genes of interest were designed by NanoString support and synthesized by Integrated DNA Technologies (IDT, Coralville, IA). Titration experiments were performed to achieve 99% signal attenuation for three highly expressed genes (ACT, B2M, GAPDH) using unlabeled probes, with a total RNA input of 500 ng chosen to avoid saturating the cartridge signal, as recommended by the provider. Calibration between the two lots of reagents was performed by NanoString support with a common calibrator sample used for qPCR. The ID3 value was discarded because it was below the expression threshold calculated as follows:

number

[0209] Gene expression was normalized using NanoString nSolver™ software 4.0 using the geometric mean of six reference genes (ACT, B2M, GAPDH, HPRT1, TUBA4A, and YWHAZ). Samples with poor quality control and values ​​below the expression threshold were discarded. For replicate samples, the mean of expression values ​​was calculated if correlation was greater than 0.95. Log2 or -ΔΔCt of normalized counts was used for downstream analysis of NanoString and qPCR values, respectively.

[0210] statistical analysis Comparisons of two groups were performed using Student's t test with samples of more than 30, and comparisons of multiple groups were performed using the nonparametric Kruskal–Wallis and Dunn's post hoc tests for continuous variables and χ for categorical variables. 2 The correlation coefficients were calculated using the logistic regression test or Fisher's exact test. Pearson correlation was used to evaluate the relationship between continuous data. Logistic regressions were constructed to evaluate the relationship between histological groups and explanatory variables using stepwise regression and compared using Akaike's information criterion (AIC). The absence of lack of fit was evaluated using an unweighted sum of squares test (Hosmer et al., 1997. StatMed. 16(9):96580) (using package rms). The performance of the models was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), and 95% confidence intervals (CI), and comparison of ROC curves was performed using bootstrap tests (n=1000) using the same number of controls and cases as in the original sample (package pROC) (Robin et al., 2011. BMC Bioinformatics. 12:77). Internal validation was performed by bootstrapping (n=1000) using the caret package (Steyerberg et al., 2001. J Clin Epidemiol. 54(8):77481). Where appropriate, multiple testing was corrected by the Benjamini-Hochberg correction. Analyses were performed using R 4.0.3 and GraphPad Prism v.9 (GraphPad Software, La Jolla, CA, USA).

[0211] result Demographic description of transplant patient cohort The clinical characteristics of 450 kidney transplant patients who met the inclusion criteria, had paired protocol biopsies and blood RNA samples taken 1 year after transplantation, and had serum creatinine levels <160 μmol / L are summarized in Table 1. Patients received standard maintenance immunosuppressive therapy, mainly with calcineurin inhibitors (CNIs: 93.3%; mainly tacrolimus: 85.1%), antiproliferative agents (86.0%, including mycophenolate mofetil (MMF), mycophenolic acid (MPA) or azathioprine), and corticosteroid regimens (74.4%). Seventy-four patients (16.4%) had anti-HLA DSA at the time of transplantation, and 89 (19.78%) showed DSA 1 year after transplantation.

[0212] cSoT scores decline one year after transplant in SCR patients. In 450 patients with good function (serum creatinine level <160 μmol / L) at 1 year after transplantation, we found that cSoT score (described in WO 2018 / 015551 and Dange et al., 2017. Kidney Int. 91(6):14731481) was significantly associated with renal function (MDRD) at 12, 24, 36, and 48 months after transplantation (p<0.01) (Figures 3A and 3B). With this classification, we found that cSoT score was significantly decreased in the blood of SCR patients compared to NR patients (adjusted p=0.013, Figures 4A and 4B), with a ROC AUC of 0.615 (95% CI=[0.5300.700]).

[0213] AKR1C3 and TCL1A are sufficient to diagnose patients unlikely to show SCR based on cSoT score. We first tested the six genes and two clinical parameters that make up the cSoT score, both independently and in association, for their ability to diagnose SCR in 450 patients with normal renal function. We first show that the two clinical parameters (recipient age at transplant and blood draw) were not significantly different in SCR patients compared to NR patients (p=0.932 and 0.936, respectively), and therefore the cSoT score has no effect on the ability to discriminate patients.

[0214] Of the six genes, only AKR1C3 and TCL1A expression was significantly decreased (corrected p-values ​​= 0.016 and < 0.0001, respectively) in the blood of SCR patients compared with NR patients (Figures 5A and 5B for AKR1C3 and Figures 6A and 6B for TCL1A), with the average reduction rate of 45 patients in the SCR group being 45.9% for AKR1C3 and 81.3% for TCL1A.

[0215] When used alone, AKR1C3 and TCL1A allowed discrimination of SCR patients with AUC 0.623 (95% CI = [0.604 to 0.741]) and 0.640 (95% CI = [0.558 to 0.721]), respectively. When associated together, these two genes allowed for an even higher and better discrimination of SCR patients with AUC 0.703 (95% CI = [0.629 to 0.777]).

[0216] We next investigated the histological diagnosis of these 450 patients. Both genes were significantly decreased in sABMR patients compared to patients with normal histology (p=0.0067 and p=0.0145 for AKR1C3 and TCL1A, respectively). A trend towards a decrease in AKR1C3 was also observed in patients with sTCMR compared to patients with normal histology (p=0.073) (Figure 5B). The association of the two genes further strengthens the difference between sABMR and normal histology (p=0.0002). In comparison, renal function at 1 year after transplantation was not different between normal histology and either sABMR or sTCMR (Figures 7A and 7B).

[0217] Finally, no differences were observed for either AKR1C3 or TCL1A between different histological groups in 150 patients with biopsy evaluation for functional and / or causative disease (Figures 8A-D).

[0218] The new combined model makes it possible to identify patients who are free of SCR one year after transplantation. Clinical parameters significantly associated with SCR compared to NR in univariate analysis were determined as experience of a rejection episode before blood collection (p<0.0001), presence of DSA before transplantation and at blood collection (p<0.001 and p=0.0025, respectively), recipient gender (p=0.0057), and intake of corticosteroids and CsA (p=0.012 and p=0.018, respectively) (Table 3). Only experience of a rejection episode before blood collection, recipient gender, and intake of CsA at blood collection were retained as significantly associated with SCR in multivariate analysis (Figure 9 and Table 3).

[0219] Table 3: Univariate and multivariate logistic regression analysis of clinical parameters for SCR diagnosis [Table 3]

[0220] Next, we carried out a composite model based on the expression of two genes, AKR1C3 and TCL1A, and these three clinical variables, and tested its discrimination ability in 450 patients with normal graft function. This score (referred to as SCR-s) was constructed using logistic regression, and the experience of rejection episodes and the intake of CsA were positively correlated with the risk of SCR, and the recipient's gender (recipient male vs. female) and the expression of TCL1A and AKR1C3 were negatively correlated with the risk of SCR (likelihood ratio p<0.0001; Figure 9). Alternatively, we can define that the experience of rejection episodes and the experience of the intake of CsA are negatively associated with NR status, while the recipient's gender (recipient male vs. female) and the expression of TCL1A and AKR1C3 are positively associated with NR status.

[0221] In the alternative score, rather than considering CsA intake, this parameter was replaced by intake of tacrolimus, another immunosuppressant that is used much more in clinical practice than cyclosporine A. In SCR-s, ​​intake of tacrolimus was negatively associated with the risk of SCR (or positively associated with NR status). The difference between CsA intake and tacrolimus intake in SCR-s is trivial: if patients take CsA, they typically do not take tacrolimus, and this parameter is positively associated with the risk of SCR; conversely, if patients take tacrolimus, they typically do not take CsA, and this parameter is negatively associated with the risk of SCR.

[0222] This SCR-s was significantly higher in the SCR group compared to the NR group (an average increase of 67.70% among 45 patients in the SCR group) (p<0.0001; Figure 10A), with an AUC of 0.838 (95% CI = [0.779-0.897]), indicating a high discriminatory ability, significantly higher than clinical parameters alone (AUC = 0.797 (95% CI = [0.726-0.867]); p = 0.0126; Figure 10B). Furthermore, SCR-s showed significantly higher values ​​in sABMR or sTCMR compared to NR (p<0.0001) and showed similar AUCs when tested independently in both groups (sABMR and sTCMR were AUC=0.843 (95%CI=[0.773-0.914] and 0.850 (95%CI=[0.761-0.939], respectively; Figure 11A-C). At the optimal threshold (Youden index), specificity and The mean mean and sensitivity were 0.78 and 0.80, respectively, with 317 of 405 NR patients identified as true negatives and 36 of 45 SCR patients identified as true positives (Figure 10A). At such thresholds, the negative predictive value (NPV) was 97.2% and the positive predictive value (PPV) was 29.0%. Finally, internal validation using bootstrap resampling (n = 1000) to correct for model optimality yielded high performance with an AUC of 0.810 (95% CI = [0.73 to 0.89].

[0223] Validating the SCR on an independent platform SCR-s was measured using the enzyme-free probe hybridization-based NanoString method, which uses a different probe than the standard qPCR previously used. We found a highly significant correlation between qPCR and NanoString gene expression (r=0.92 and 0.778 for TCL1A and AKR1C3, respectively; p<0.001) (Table 4).

[0224] Table 4: Blood gene expression is independent of measurement method The correlation plot represents the r Pearson correlation (1 to -1) of gene expression between qPCR and NanoString measurements for 450 patients. [Table 4]

[0225] Using the enzyme-free probe hybridization-based NanoString method, we confirmed the significant downregulation of AKR1C3 and TCL1A in SCR patients (p=0.0045 and 0.013, respectively) compared to NR patients (Figure 12A-B for AKR1C3, Figure 13A-B for TCL1A) and the ability of SCR-s to discriminate SCR patients with an AUC of 0.815 (95% CI=[0.7280.880]).

[0226] Validation of SCR-s in an independent multicenter validation set The validation set included 110 patients, including 11 with SCR. In this cohort, the established SCR-s allowed correct classification of 9 out of 11 SCR patients, resulting in an AUC of 0.884 (95% CI = [0.701 to 0.99]) (Figure 14).

[0227] Using the optimal threshold (Youden index) determined on the training set, the specificity and sensitivity were 0.798 and 0.909, respectively, and the NPV was 98.7%.

[0228] Consideration SCR is only detectable in protocol biopsies from patients with normal allograft function and affects up to 25% of renal biopsies at 1 year post-transplant, with its incidence inversely correlated with time since transplantation (CouvratDesvergnes et al., 2019. NephrolDialTransplant. 34(4):703711; Loupy et al., 2015. JAmSocNephrol. 26(7):172131; Nankivell et al., 2004. Transplantation. 78(2):2429).

[0229] Detection of such lesions is associated with unfavorable outcomes (Filippone & Farber, 2020. Transplantation; Loupy et al., 2015. JAmSocNephrol. 26(7):172131; Mehta et al., 2017. ClinTransplant. 31(5); Nankivell et al., 2004. Transplantation. 78(2):2429; Rush & Gibson, 2019. Transplantation. 103(6):e139e145; Shishido et al., 2003. JAmSocNephrol. 14(4):104652), and early intervention is beneficial for graft outcomes (Kee et al., 2006. Transplantation. 82(1):3642; Parajuli et al., 2019. Transplantation. 103(8):17221729; Rush et al.,1998.JAmSocNephrol.9(11):212934). Thus, detecting such insidious lesions using non-invasive diagnostic tools could improve transplant outcomes, and diagnosing patients without SCR could also help to avoid invasive procedures for patients without severe histological lesions and improve patient management (CouvratDesvergnes et al.,2019.NephrolDialTransplant.34(4):703711; Friedewald&Abecassis,2019.AmJTransplant.19(7):21412142).

[0230] Previously, we reported a six-gene blood signature that allows the detection of patients with clinical immune tolerance (Brouard et al., 2012. Am J Transplant. 12(12):3296307; Danger et al., 2017. Kidney Int. 91(6):14731481; WO 2018 / 015551). Furthermore, this score was found to be decreased in patients who developed anti-HLA antibodies, suggesting that it is associated with loss of immune tolerance.

[0231] Based on these data, we hypothesized that this score would be an ideal signature for indicating a low risk of immune rejection and tested its ability to diagnose SCR in patients with stable graft function early after transplantation. Herein, we showed that this score was not only associated with SCR but also improved it.

[0232] The inventors show that only two genes, AKR1C3 and TCL1A, independently of each other, allow the identification of patients affected by SCR, and that the combination of both genes allows even better discrimination.

[0233] Then, based on the expression of these two genes and three clinical variables (previous experience of rejection episodes, intake of immunosuppressants (especially intake of CsA or intake of tacrolimus), and gender of the recipient), a composite score (SCR-s) was constructed, allowing for high detection of the absence of SCR at 1 year after transplantation.

[0234] Several biomarkers for SCR have been proposed previously, including blood gene signatures (WO 2015 / 179777; WO 2019 / 217910; Crespo et al., 2017. Transplantation. 101(6):14001409; Friedewald et al., 2019. AmJTransplant. 19(1):98109; Van Loon et al., 2019. EbioMedicine. 46:463472; Zhang et al., 2019. JAmSocNephrol. 30(8):14811494). Zhang published that a 17-gene signature can diagnose SCR and acute cellular rejection at 3 months after transplantation with 89% NPV and 73% PPV (Zhang et al., 2019. J Am Soc Nephrol. 30(8):14811494). Similarly, a 51-gene signature allows identification of SCR at 24 months after transplantation (Friedewald et al., 2019. Am J Transplant. 19(1):98109). These signatures did not include AKR1C3 and TCL1A, which can be explained by the fact that in both studies mainly cellular and borderline rejection were analyzed. Van Loon reported an 8-gene signature, but only to diagnose ABMR, that performed comparable to our SCR-s for sABMR (Van Loon et al., 2019. EbioMedicine. 46: 463472). Finally, the 17-gene signature of the kSort study has also been proposed to diagnose 6-month sABMR (Crespo et al., 2017. Transplantation. 101(6): 14001409), but was not validated in a large cohort of 1,134 patients (Van Loon et al., 2021. Am J Transplant. 21(2): 740750).

[0235] We report herein a composite score (SCR-s) including only two genes and three clinical parameters that allows detection of SCR-free patients with normal graft function in one blood sample at one year after transplantation. This non-invasive tool can be used to avoid biopsies of patients who are unlikely to show SCR in a large population of kidney transplant recipients. In fact, this SCR-s reaches 97.2% NPV, which means that a negative test is a true negative with a high probability. As an example, this avoids 317 biopsies in a cohort of 450 patients. Moreover, and in contrast to the conventional solutions detailed above, our SCR-s allows detection of both latent T cell-mediated rejection (sTCMR) and sABMR. Unlike microarray or RNA sequencing-based signatures, this SCR-s can be easily implemented routinely using qPCR, which is widely available in clinical facilities. We also validated the model using both classical qPCR and NanoString platforms to enhance technical robustness and cost-effectiveness.

[0236] We performed an initial validation of SCR-s in an independent multicenter validation set including 110 patients (11 with SCR diagnosed during surveillance biopsy).SCR-s provided correct classification for 9 of the 11 SCR patients.

[0237] Although our model requires further validation in independent patient cohorts, our hypothesis-based test focused on the measurement of only a few genes, thus reducing the "chance" association of parameters that may occur in fishing tests. Furthermore, our analysis is performed in a "real-life" scenario without prior patient selection on a large patient cohort.

[0238] Example 2 With SCR-s in Example 1, the inventors aimed to develop an alternative combined model that would be able to diagnose SCR, whether it be T cell mediated rejection (sTCMR) or subclinical antibody mediated rejection (sABMR), and would be specific only for subclinical antibody mediated rejection (sABMR).

[0239] material and method This is the same as in Example 1.

[0240] result Identification of genes and clinical parameters associated with sABMR Among kidney transplant patients in the study cohort who met the inclusion criteria, we selected 33 with biopsy-proven sABMR (SCR “liquid” in Figure 1 ).

[0241] We found that in the blood of these patients in the sABMR group, cSoT (described in WO 2018 / 015551 and Danger et al., 2017.KidneyInt.91(6):14731481) was significantly decreased compared to patients in the NR group and sTCMR patients (p=0.0102, FIG. 14A), with an AUC of 0.578 (95% CI=[0.4650.691]).

[0242] Of the six genes that make up the cSoT, the expression levels of AKR1C3 and TCL1A were significantly decreased in the blood of these 33 sABMR patients compared to all other patients (p = 0.0034 and 0.0011, respectively) (Figure 14B and Figure 14C). When used alone, AKR1C3 and TCL1A were able to identify sABMR patients with AUCs of 0.652 (95% CI = [0.570-0.734]) and 0.669 (95% CI = [0.578-0.760]), respectively. Combining the two genes successfully identified sABMR patients with an AUC of 0.711 (95% CI = [0.624-0.797]), although renal function was not significantly different between the two groups (p = 0.136) (Figure 14D).

[0243] Eleven clinical parameters, namely, experience of a rejection episode before blood collection (p<0.0001), allograft rank (p<0.0001), use of deleting induction treatment (p=0.00235), recipient sex (p=0.00477), recipient CMV positivity (p=0.0279), corticosteroid intake 12 months after transplant (p=0.0318), and number of HLA-A, -B, and -DR mismatches between donor and recipient strictly >3 (p=0.0362), were significantly associated with sABMR in univariate analysis (p<0.20).

[0244] Construction of a score for detecting sABMR (sABMR-s) From these 11 clinical parameters and the two genes significantly associated with sABMR in univariate analysis, a refined composite score of sABMR (sABMR-s) was constructed using multivariate logistic regression with stepwise selection and bootscape resampling: experience of a rejection episode before blood collection, allograft rank, and HLA mismatch were positively associated with sABMR status, while blood gene expression of TCL1A and AKR1C3 and recipient gender were negatively associated with sABMR status in this sABMR-s (Figure 15A). sABMR-s was significantly lower in the sABMR group than in the groups with other diagnoses (NR group and sTCMR group) (p<0.0001, Figure 15B), and showed high discriminatory ability with AUC0.860 (95%CI=[0.794-0.925]).

[0245] The presence of donor-specific antibodies (DSA) one year after transplantation was significantly associated with sABMR in univariate analysis (p<0.0001), but this parameter did not discriminate sABMR significantly better than the two genes combined (AUC=0.768 (95% CI=[0.686-0.849]), p=0.307).

[0246] Because we constructed the sABMR score based on the high prevalence of de novo DSA (dnDSA)-positive patients without sABMR lesions, independent of DSA measurements (64 patients in our cohort, 60% according to the literature (see, for example, Yamamoto et al., 2016. Transplantation. 100(10):21942202, or Bertrand et al., 2020. Transplantation. 104(8):17261737), DSA experience was not used to construct the sABMR-s. Our sABMR-S had a higher discrimination ability than DSA experience in the first year of transplantation (p=0.00923), and adding DSA experience to sABMR-s did not significantly improve its discrimination performance (AUC=0.877; 95%CI=[0.809-0.944]; p=0.222). Interestingly, sABMR-s remained significantly reduced in sABMR patients compared to DSA-positive patients with a diagnosis other than sABMR (p=0.0011), with an AUC of 0.77 (95%CI=[0.6660.875]).

[0247] Furthermore, among 147 patients who underwent biopsy for medical indications, sABMR-s was also significantly reduced for 23 patients with sABMR and a probing biopsy compared with 124 patients who underwent a probing biopsy for other diagnostic causes (p=0.0023).

[0248] At the optimal threshold (Youden index) maximizing specificity and sensitivity, corresponding to a value of 2.40, sABMR-s showed specificity and sensitivity of 0.840 and 0.758, respectively. At this threshold, sABMR-s had a negative predictive value (NPV) of 97.7% and a positive predictive value (PPV) of 27.7%, with 342 of 408 patients with a diagnosis other than sABMR identified as true negative (83.8%) and 25 of 33 patients with sABMR identified as true positive (75.8%). Finally, internal validation using bootstrap resampling (n=1000) to correct for model optimality yielded high performance with an AUC of 0.830 (95% CI=[0.74-0.92].

[0249] Validation of sABMR-s on an independent technology platform sABMR-s was constructed using standard qPCR methods for AKR1C3 and TCL1A measurements. To enable use on a large scale, we validated the technology using an enzyme-free probe hybridization-based NanoString platform with probes different from those used for qPCR. We confirmed significant downregulation of AKR1C3 and TCL1A in sABMR compared to other groups (p=0.013 and 0.0004, respectively) (Figure 16A-B), with a significant high correlation between qPCR and NanoString gene expression (r=0.901 and 0.757, p<0.0001 for TCL1A and AKR1C3, respectively) (Figure 16C). Because qPCR and NanoString measurements showed different dynamic ranges, the sABMR-s parameters were used to adjust the coefficients in the NanoString data. The discrimination ability of sABMR-s using NanoString data reached a similar discrimination ability as qPCR, with an AUC of 0.859 (95% CI = [0.793 to 0.925]) (p < 0.0001; Figure 15B).

[0250] Immunosuppression did not alter the discriminatory ability of sABMR-s. sABMR-s was slightly reduced in patients without sABMR lesions who were treated with either corticosteroids or antithymocyte globulin (ATG)-depleting induction therapy compared with patients without sABMR lesions who did not receive corticosteroids or received non-depleting treatment or no induction therapy (p=0.0002 and <0.0001, respectively). In the subset of patients treated with tacrolimus (Figure 17A), corticosteroids (Figure 17B), antiproliferatives (Figure 17C) or depletion induction treatments (Figure 17D), the AUC values ​​comparing sABMR to other treatments were 0.864 (95%CI = [0.802-0.926]), 0.839 (95%CI = [0.767-0.911]), 0.855 (95%CI = [0.790-0.921]), and 0.811 (95%CI = [0.726-0.896]), respectively. Thus, sABMR-s was still able to discriminate between patients with and without sABMR lesions, regardless of treatment.

Claims

A method for assisting in determining the presence or absence of subclinical renal rejection in a subject, comprising: a) determining the level, amount, or concentration of at least one biomarker selected from the group consisting of TCL1A and AKR1C3 in a sample previously taken from the subject; b) comparing the level, amount, or concentration of the at least one biomarker with the level, amount, or concentration of the same at least one biomarker determined in at least one reference subject, wherein the at least one reference subject is: - a subject who has not received a kidney transplant, - a kidney transplant recipient who does not have subclinical renal rejection, or - the subject itself before kidney transplantation who has been tested for subclinical renal rejection ; and if, as a result of step b), the level, amount, or concentration of the at least one biomarker is statistically significantly lower than the level, amount, or concentration of the same at least one biomarker determined in the at least one reference subject, the result indicates that the subject has subclinical renal rejection. Method. Claim 2 The method according to claim 1, wherein step a) does not include determining the level, amount, or concentration of CD40, CTLA4, ID3, and / or MZB1. Claim 3 The method according to claim 1, wherein step a) does not include determining the level, amount, or concentration of biomarkers other than TCL1A and / or AKR1C3. Claim 4 The method according to any one of claims 1 to 3, wherein step a) includes determining the level, amount, or concentration of TCL1A in the sample previously taken from the subject. Claim 5 The method according to claim 1, wherein step a) includes determining the level, amount, or concentration of AKR1C3 in the sample previously taken from the subject. Claim 6 The method according to claim 1, wherein step a) includes determining the levels, amounts, or concentrations of both TCL1A and AKR1C3 in the sample previously taken from the subject. Claim 7 The method according to claim 1, wherein the level, amount, or concentration of the at least one biomarker is expressed in terms of absolute or relative level, amount, or concentration. Claim 8 a) Determining a composite score based on the level, amount, or concentration of said at least one biomarker selected from the group consisting of TCL1A and AKR1C3, wherein said composite score is established using formula (1): 【Number 1】 (wherein: “β ｉ ” represents the regression coefficient of each of the levels, amounts, or concentrations of the at least one biomarker; "X" ｉ represents a predictor of the respective level, amount, or concentration of said at least one biomarker; 「β ０ 」 represents the intercept of the equation) step; b) Comparing said composite score with a reference composite score determined in said at least one reference subject and comprising If, as a result of step b), said composite score is substantially higher than said reference composite score determined in said at least one reference subject, the result indicates that the subject is suffering from subclinical renal rejection, according to the method of claim 1. **Claim 9** a) - the level, amount, or concentration of said at least one biomarker selected from the group consisting of TCL1A and AKR1C3; and - one, two, or three clinical parameters selected from: - experience of a rejection episode prior to blood sampling, - gender of the recipient, and - intake of immunosuppressive agent (IS) at the time of blood sampling Determining a composite score, wherein said composite score is established using formula (2): 【Number 2】 (wherein: 「β ＴＣＬ１Ａ 」, 「β ＡＫＲ１Ｃ３ 」, 「β 以前の拒絶反応エピソード 」, 「β ＩＳの摂取 」, and 「β レシピエントの性別 」 represent the regression coefficients of each predictor in the level, amount, or concentration of the biomarker and the clinical parameter; "Previous rejection episode" represents a predictor variable defining experience of a rejection episode prior to blood sampling, where 0 = "no previous rejection episode", 1 = "one or more previous rejection episodes"; "IS intake" represents a predictor variable defining intake of immunosuppressive agent (IS) at the time of blood sampling, where 0 = "no intake of CsA" or "intake of tacrolimus", 1 = "intake of CsA" or "no intake of tacrolimus"; "Recipient gender" represents a predictor variable defining the gender of the transplant recipient, where 0 = "female", 1 = "male"; "Expr(TCL1A)" and "Expr(AKR1C3)" represent predictor variables defining the level, amount, or concentration of TCL1A and AKR1C3, respectively; 「β ０ 」 represents the intercept of the equation) step; b) Comparing said composite score with a reference composite score determined in said at least one reference subject and comprising If, as a result of step b), said composite score is substantially higher than said reference composite score determined in said at least one reference subject, the result indicates that the subject is suffering from subclinical renal rejection, according to the method of claim 1. **Claim 10** The method according to claim 9, wherein the intake of the immunosuppressant (IS) at the time of blood collection is the intake of tacrolimus at the time of blood collection.

11. The method according to claim 9, wherein the intake of the immunosuppressant (IS) at the time of blood collection is the intake of cyclosporine A (CsA) at the time of blood collection.

12. The composite score is: - The level, amount, or concentration of both TCL1A and AKR1C3, and - The following three clinical parameters: (i) experience of rejection episodes before blood collection, (ii) gender of the recipient, and (iii) intake of cyclosporine A (CsA) at the time of blood collection The method according to claim 9, which is determined by.

13. The method according to claim 9, wherein the subclinical renal rejection is subclinical T cell-mediated renal rejection (sTCMR), subclinical antibody-mediated renal rejection (sABMR) and / or sTCMR / sABMR mixed type.

14. a) - The level, amount, or concentration of at least one biomarker selected from the group consisting of TCL1A and AKR1C3; and - One, two, three, or four clinical parameters selected from the following: · Experience of rejection episodes before blood collection, · Gender of the recipient, · Allograft rank, and · Number of HLA mismatches between the donor and the recipient A step of determining a composite score by, The composite score is established using formula (3): 【Number 3】 (wherein: 「β ＴＣＬ１Ａ 」, 「β ＡＫＲ１Ｃ３ 」, 「β 以前の拒絶反応エピソード 」, 「β 同種移植ランク 」, 「β ＨＬＡ不一致 」, and 「β レシピエントの性別 」 represent the regression coefficients of each predictor in the levels, amounts, or concentrations of biomarkers and clinical parameters; "Previous rejection episode" represents a predictor variable that defines the experience of rejection episodes before blood collection, 0 = "no previous rejection episode", 1 = "one or more previous rejection episodes"; "Allograft rank" represents a predictor variable that defines the occurrence of previous transplants, 0 = "no previous transplant", 1 = "one or more previous transplants"; "HLA mismatch" represents a predictor variable that defines the occurrence of HLA mismatches between the donor and the recipient, 0 = "3 or fewer HLA-A, -B, and / or -DR mismatches", 1 = "strictly more than 3 HLA-A, -B, and / or -DR mismatches"; "Gender of the recipient" represents a predictor variable that defines the gender of the transplant recipient, 0 = "female", 1 = "male"; "Expr(TCL1A)" and "Expr(AKR1C3)" represent predictor variables that define the level, amount, or concentration of TCL1A and AKR1C3, respectively; 「β ０ 」 represents the y-intercept of the equation) step; b) comparing the composite score with a reference composite score determined in at least one reference subject; comprising: wherein, if the composite score is substantially higher than the reference composite score determined in the at least one reference subject as a result of step b), the result indicates that the subject is suffering from subclinical renal rejection, according to the method of claim 1. **Claim 15** The method according to claim 14, wherein the subclinical renal rejection consists of subclinical antibody-mediated renal rejection (sABMR). **Claim 16** The method according to claim 1, wherein the at least one reference subject is a reference population comprising two or more reference subjects. **Claim 17** The method according to claim 1, which is computer-implemented. **Claim 18** A computer system for assisting in determining the presence or absence of subclinical renal rejection in a subject, comprising: i) at least one processor; ii) at least one storage medium readable by the processor and storing at least one code which, when executed by the processor, causes the processor to a. receive an input level, amount, or concentration of the at least one biomarker selected from the group consisting of TCL1A and AKR1C3; b. analyze and transform the input level, amount, or concentration to derive a composite score established using formula (1) according to claim 8; c. generate an output which is the composite score; and d. provide a result as to whether the subject is suffering from subclinical renal rejection based on the output. A computer system comprising. **Claim 19** The at least one code readable by the processor, when executed by the processor, causes the processor to a. receive an input level, amount, or concentration of the at least one biomarker selected from the group consisting of TCL1A and AKR1C3, and input values of one, two, or three clinical parameters selected from (i) experience of rejection episodes before blood sampling, (ii) gender of the recipient, and (iii) intake of immunosuppressive agent (IS) at the time of blood sampling; b. analyze and transform the input level, amount, or concentration and the input values to derive a composite score established using formula (2) according to claim 9; c. generate an output which is the composite score; and ​ ​ d. Based on the output, providing a result as to whether the subject is suffering from subclinical renal rejection The computer system according to claim 18, which causes the above to be executed **Claim 20** The computer system according to claim 19, wherein the intake of the immunosuppressant (IS) at the time of blood collection is the intake of tacrolimus at the time of blood collection. **Claim 21** The computer system according to claim 19, wherein the intake of the immunosuppressant (IS) at the time of blood collection is the intake of cyclosporine A (CsA) at the time of blood collection. **Claim 22** The computer system according to claim 18, wherein the subclinical renal rejection is subclinical T cell-mediated renal rejection (sTCMR), subclinical antibody-mediated renal rejection (sABMR), and / or a mixed type of sTCMR / sABMR. **Claim 23** When the at least one code readable by the processor is executed by the processor, the processor is caused to a. Receive an input level, amount, or concentration of the at least one biomarker selected from the group consisting of TCL1A and AKR1C3, (i) Experience of rejection episodes before blood collection, (ii) Gender of the recipient, (iii) Previous transplantation, and (iv) Number of HLA mismatches between the donor and the recipient Entering the values of one, two, three, or four clinical parameters selected from, b. Analyzing and converting the input level, amount, or concentration, and the input values to derive a composite score established using the formula (3) according to claim 14. c. Generating an output that is the composite score, and d. Based on the output, providing a result as to whether the subject is suffering from subclinical renal rejection The computer system according to claim 18, which causes the above to be executed **Claim 24** The computer system according to claim 23, wherein the subclinical renal rejection consists of subclinical antibody-mediated renal rejection (sABMR). **Claim 25** When the output is substantially higher than the same output obtained in at least one reference subject, the subject is determined to be suffering from subclinical renal rejection, and the reference subject is a subject who has not received a kidney transplant, a kidney transplant recipient who is not suffering from subclinical rejection, or the subject itself before kidney transplantation who has been examined for subclinical rejection. The computer system according to any one of claims 18 to 24. **Claim 26** A computer program comprising software code readable by a processor adapted to execute the computer-implemented method according to claim 17 when executed by the processor. **Claim 27** A non-transitory computer-readable storage medium comprising code for causing a computer to cause a processor to execute the computer-implemented method according to claim 17 when executed by the computer. **Claim 28** A kit of parts for carrying out the method according to any one of claims 1 to 17, comprising means for determining the level, amount or concentration of at least one biomarker selected from the group consisting of TCL1A and AKR1C3. **Claim 29** The kit of parts according to claim 28, further comprising means for determining the level, amount or concentration of at least one reference marker and instructions for use for carrying out the method. **Claim 30** The kit of parts according to claim 28, wherein the means is selected from the group consisting of nucleic acid probes, antibodies and aptamers.

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