In vitro methods for predicting organ transplant rejection.

Abstract

The present invention relates to an in vitro method for predicting organ transplant rejection, preferably acute organ transplant rejection, most preferably acute liver transplant rejection, in a subject in need of transplantation.

Description

[Technical Field]

[0001] The present invention relates to the medical field. In particular, the present invention relates to an in vitro method for predicting organ transplant rejection, preferably acute organ transplant rejection, most preferably acute liver transplant rejection, in a subject in need of transplantation. [Background technology]

[0002] Liver transplants from deceased donors are nowadays a routine procedure for the treatment of end-stage liver failure and, in many cases, the only potential cure. While donation after brain death (DBD) accounts for the majority of organ donors, in recent years there has been growing interest in increasing the donor pool. As a result, organ donation after circulatory arrest (DCD) has been newly introduced and has contributed to an increase in the number of donations in many countries. In Spain, these donations currently account for 32% of donors and were one of the key factors applied to achieve a rate of 46 donors per million inhabitants in 2017.

[0003] Thirty to 40% of transplant patients experience some symptoms of acute rejection or early graft dysfunction in the first few weeks after transplantation. Furthermore, the occurrence of acute rejection symptoms increases the likelihood of chronic graft rejection and loss by 40% in the medium term. Furthermore, up to 4% of these patients lose their organ within the first few weeks, necessitating a new, urgent retransplant, which is not always feasible. All of this puts the patient's life at risk, and 30% of patients have been confirmed to die from causes related to rejection. Furthermore, rejection symptoms are identified at the time of their onset. To date, no tools exist that can predict the onset of these symptoms and their serious impact on the patient's health early. Therefore, there is an unmet medical need to find a reliable method for predicting organ transplant rejection. This method could help clinicians select possible treatments that may be needed in advance, thereby increasing the chances of successful treatment. Summary of the Invention [Problem to be solved by the invention]

[0004] Therefore, the present invention is directed to solving this problem, and provides herein a new method for predicting organ transplant rejection, preferably acute organ transplant rejection, most preferably acute liver transplant rejection. [Means for solving the problem]

[0005] As mentioned above, the present invention relates to a method for predicting organ graft rejection, preferably acute organ graft rejection, most preferably acute liver graft rejection, in a subject in need of transplantation, said method comprising assessing the concentration or expression level of a particular biomarker, wherein identification of a variation or deviation (increase or decrease) in the concentration or expression level relative to a pre-set threshold level determined in a control subject indicates that the subject is likely to undergo organ graft rejection.

[0006] In particular, the inventors assayed the biomarkers shown in Tables 1 and 2. The biomarkers were then quantified as shown in Tables 5 and 6. Interestingly, as shown in Tables 8 and 9, several combinations of at least two of the assayed biomarkers exhibit highly ranked AUC values ​​consistently above 0.7 in association with the prediction of organ transplant rejection.

[0007] There are clear differences between each marker when comparing the non-rejection group and the acute rejection group, as shown in Figures 1, 2, and 3. In a preferred embodiment, the combination of biomarkers fibronectin, hyaluronic acid, MIP1B, mitDNA, MMP7, MMP8, angiogenin, and ANGPT2 was assayed, resulting in a sensitivity and specificity of 99.6% (see Figure 4).

[0008] This means that any individual biomarker from Table 1 and Table 2, or any combination thereof including at least two to all of the biomarkers from Table 1 and Table 2 (i.e., three, four, five, ... up to all of the biomarkers from Table 1 and Table 2), can be used in accordance with the present invention to predict organ graft rejection in a subject in need of a transplant.

[0009] Accordingly, a first embodiment of the present invention provides an in vitro method for predicting organ graft rejection in a subject in need of transplantation (hereinafter "the method of the present invention"), comprising: a) detecting in an organ preservation solution or a blood sample obtained from the patient, fibronectin (FIBRONECT), hyaluronic acid (HYALURON), MIP1B, HLADR_FCM, mitDNA, MMP-7, MMP-8, angiogenin, angiopoietin-2, thrombospondin-1, MMP-3, ADAM9, ADAMTS1, ADAMTS13, cathepsin L, bile acids, Casp_1, Casp_3, Casp_8, cathepsin A, cathepsin B, cathepsin D, cathepsin S, CCL20, CCL28, CCL5, CX3CL1, CXCL16, DNA, DPPIV, endoglin, endostatin / collagen XVIII, FFA, FGF Basic, HGF, HMGB1, HSP70, HSP90, ICAM-1, IGFBP-1, IGFBP-2, IGFBP-3, IL10, IL17A, IL18, IL18BP, IL1α, IL1β, IL6, IL8, IP10, kallikrein 6 (KLK6), LDH, leptin, LiveMitoc, LPS, MCP1, MMP-2, MMP-9, nucleosome, PCSK9, pentraxin 3 (PTX3), platelet factor 4 (PF4), prolactin, Prot, PRTN3, myeloblastin, P-selectin, Serpin E1, Serpin and b) assessing the concentration or expression level of at least one biomarker selected from the group consisting of TIMP-1, TIMP-4, uPAr, uric acid, VCAM-1, VEGF, cathepsin X, GDNF, cathepsin Z and / or cathepsin P, or any combination thereof; and b) identifying a deviation or variation (increase or decrease) in the concentration or expression level relative to a pre-set threshold level determined in a control subject, wherein the identification of a deviation or variation (increase or decrease) in the concentration or expression level relative to a pre-set threshold level determined in a control subject is indicative of the subject being likely to undergo organ transplant rejection.

[0010] In a preferred embodiment, the method of the present invention relates to an in vitro method for predicting organ graft rejection in a subject in need of transplantation, comprising: a) assessing the concentration or expression level of at least one biomarker selected from the group consisting of fibronectin, hyaluronic acid, MIP1B, mitDNA, MMP7, MMP8, angiogenin and / or ANGPT2, or any combination thereof, in an organ preservation solution or in a blood sample obtained from the patient; and b) identifying a deviation or variation (increase or decrease) in the concentration or expression level relative to a pre-set threshold level determined in a control subject, which indicates that the subject is likely to reject the organ graft.

[0011] In a preferred embodiment, the method of the present invention relates to an in vitro method for predicting organ graft rejection in a subject in need of transplantation, comprising: a) assessing the concentration or expression level of at least one combination of biomarkers selected from Table 8 or Table 9 in an organ preservation fluid or in a blood sample obtained from the patient; and b) identifying a deviation or variation (increase or decrease) in the concentration or expression level relative to a pre-set threshold level determined in a control subject, which indicates that the subject is likely to undergo organ graft rejection.

[0012] In a preferred embodiment, the method of the present invention relates to an in vitro method for predicting organ graft rejection in a subject in need of transplantation, comprising assessing the concentration or expression level of a combination of the biomarkers fibronectin, hyaluronic acid, MIP1B, mitDNA, MMP7, MMP8, angiogenin and ANGPT2 in organ preservation fluid or in a blood sample obtained from the patient, wherein b) identification of a deviation or variation (increase or decrease) in the concentration or expression level relative to a pre-set threshold level determined in a control subject indicates that the subject is likely to reject the organ graft.

[0013] In a preferred embodiment, the method of the present invention relates to an in vitro method for predicting acute rejection of an organ transplant, most preferably acute rejection of a liver transplant, in a subject in need of a transplant.

[0014] A second embodiment of the present invention is directed to a method for predicting organ graft rejection in a subject in need of transplantation, comprising administering to the subject a therapeutically effective amount of ... Basic, HGF, HMGB1, HSP70, HSP90, ICAM-1, IGFBP-1, IGFBP-2, IGFBP-3, IL10, IL17A, IL18, IL18BP, IL1α, IL1β, IL6, IL8, IP10, kallikrein 6 (KLK6), LDH, leptin, LiveMitoc, LPS, MCP1, MMP-2, MMP-9, nucleosome, PCSK9, pentraxin 3 (PTX3), platelet factor 4 (PF4), prolactin, Prot, PRTN3, myeloblastin, P-selectin, Serpin E1, Serpin the in vitro use of at least one biomarker selected from the group consisting of fibronectin, hyaluronic acid, MIP1B, mitDNA, MMP7, MMP8, angiogenin, and / or ANGPT2, or any combination thereof; or at least one combination of biomarkers selected from Table 8 or Table 9; or a combination of the biomarkers fibronectin, hyaluronic acid, MIP1B, mitDNA, MMP7, MMP8, angiogenin, and ANGPT2.

[0015] In a preferred embodiment, the present invention relates to the in vitro use of the above biomarkers to predict acute rejection of an organ transplant, most preferably acute rejection of a liver transplant, in a subject in need of a transplant.

[0016] A third embodiment of the present invention is directed to fibronectin, hyaluronic acid, MIP1B, HLADR_FCM, mitDNA, MMP-7, MMP-8, angiogenin, angiopoietin-2, thrombospondin-1, MMP-3, ADAM9, ADAMTS1, ADAMTS13, cathepsin L, bile acids, Casp_1, Casp_3, Casp_8, cathepsin A, cathepsin B, cathepsin D, cathepsin S, CCL20, CCL28, CCL5, CX3CL1, CXCL16, DNA, DPPIV, endoglin, endostatin / type XVIII collagen, FFA, FGF. Basic, HGF, HMGB1, HSP70, HSP90, ICAM-1, IGFBP-1, IGFBP-2, IGFBP-3, IL10, IL17A, IL18, IL18BP, IL1α, IL1β, IL6, IL8, IP10, kallikrein 6 (KLK6), LDH, leptin, LiveMitoc, LPS, MCP1, MMP-2, MMP-9, nucleosome, PCSK9, pentraxin 3 (PTX3), platelet factor 4 (PF4), prolactin, Prot, PRTN3, myeloblastin, P-selectin, Serpin E1, Serpin and a kit for carrying out the method of the present invention, comprising reagents or tools for assessing the concentration or expression level of at least one biomarker selected from the group consisting of Fi, SPECKS, THP1_IL1, thrombospondin-1, TIMP-1, TIMP-4, uPAr, uric acid, VCAM-1, VEGF, cathepsin X, GDNF, cathepsin Z and / or cathepsin P, or any combination thereof; or at least one biomarker selected from the group consisting of fibronectin, hyaluronic acid, MIP1B, mitDNA, MMP7, MMP8, angiogenin and / or ANGPT2, or any combination thereof; or at least one combination of biomarkers selected from Table 8 or Table 9; or a combination of the biomarkers fibronectin, hyaluronic acid, MIP1B, mitDNA, MMP7, MMP8, angiogenin and ANGPT2.

[0017] A fourth embodiment of the present invention relates to the use of the kit for predicting organ transplant rejection, preferably for predicting liver transplant rejection.

[0018] Furthermore, the present invention relates to the following:

[0019] An in vitro method for identifying a biomarker signature for predicting organ transplant rejection, preferably an in vitro method for identifying a biomarker signature for predicting liver transplant rejection, comprising: a) assessing the concentration or expression level of at least one of the above-mentioned biomarkers, or any combination thereof, in an organ preservation solution or in a blood sample obtained from a patient; and b) identifying a deviation or variation (increase or decrease) in the concentration or expression level relative to a pre-set threshold level determined in control subjects, indicating that the biomarker signature can be used to predict organ transplant rejection.

[0020] 1. A method for detecting any of the above biomarkers or a combination thereof in an organ preservation solution or in a blood sample obtained from a patient at risk of rejecting an organ transplant, comprising: a) contacting the test sample with primers or antibodies specific for the biomarkers; b) amplifying the biomarker to generate an amplification product in the test sample if primers are used, or amplifying the signal by using a second antibody if a detection antibody is used; c) measuring the expression level by determining the level of amplification product or signal in the test sample; A method comprising:

[0021] The present invention also relates to methods of treating patients identified as being at high risk for organ transplant rejection by using the methods of the present invention, wherein the treatment includes immunosuppressants such as cyclosporine (Neoral™, Gengraf™, Sandimmune™), tacrolimus (Prograf™, FK506), mycophenolate mofetil (CellCept™), prednisone, azathioprine (Imuran™), sirolimus (Rapamune™), daclizumab and basiliximab (Zenapax™ and Simulect™), OKT3™ (monoclonal antibody), thymoglobulin and / or Campath.

[0022] In a preferred embodiment, the method of the present invention is a computer-implemented invention in which a processing unit (hardware) and software are configured to perform the following operations: receiving concentration or expression values ​​of any of the biomarkers described above or a combination thereof; Processing the received concentration or expression values ​​to find substantial variations or deviations; and A variation or deviation in the concentration or expression level is output via a terminal display, where the variation or deviation in the concentration or expression level is indicative of the subject's likelihood of rejecting the organ transplant.

[0023] The invention also relates to a computer program or computer readable medium comprising means for carrying out the method of the invention.

[0024] For purposes of the present invention, the following terms are defined.

[0025] The term "comprising" means including, but not limited to, what follows the word "comprising." Thus, use of the word "comprising" indicates that the listed elements are required or essential, but that other elements are optional and may or may not be present.

[0026] The term "consisting of" means "including, and limited to" what precedes the phrase "consisting only of." Thus, the phrase "consisting only of" indicates that the listed elements are required or essential, and that no other elements may be present.

[0027] Generally, the "threshold" or "cutoff value" can be determined experimentally, empirically, or theoretically. The threshold can also be arbitrarily selected based on existing experimental and / or clinical conditions, as would be recognized by a person skilled in the art. The threshold needs to be determined to obtain optimal sensitivity and specificity according to the function and benefit / risk balance (false positive and false negative clinical outcomes) of the test. In one embodiment of the present invention, the threshold is derived from patients who have not experienced organ transplant rejection. Generally, the optimal sensitivity and specificity (and therefore the threshold) can be determined using a receiver operating characteristic (ROC) curve based on experimental data. [Brief explanation of the drawings]

[0028] [Figure 1] Figure 1 shows the difference between each marker when comparing the non-rejection group (normal, n=47) with the acute rejection group (AR, n=20). The dots represent the median value for each marker. [Figure 2]Figure 1 shows a heat map of sample classification using selected markers. A. The heat map illustrates the clustering established when using different algorithmic measures. Darker colors indicate a higher affinity with the non-rejector group, while lighter colors indicate a higher affinity with the rejector group. Two clusters are observed, corresponding to the two existing groups. B. A representation of the distances observed between selected markers when processing the data using two algorithmic approaches. Both groups (non-rejectors and rejectors) are clearly defined; colors closer to black indicate shorter distances, meaning that patients belong to the same group, while lighter colors indicate longer distances, meaning that every patient is in a different group when compared to other patients. A clear clustering was found between all non-rejector patients and rejector patients (NR: non-rejector; P: rejector). [Figure 3] Figure 1 illustrates the rejection and non-rejection groups after processing the results with different algorithms. The ellipses represent the variance of the results for all patients included. There is a significant difference between the two groups at p<0.05. [Figure 4] A. ROC curves for data obtained in rejection and non-rejection groups when markers were analyzed using an algorithm including a combination of fibronectin, hyaluronic acid, MIP1B, mitDNA, MMP7, MMP8, angiogenin, and ANGPT2 biomarkers. The ROC space was calculated at 99.6%. B. Table showing the calculated positive and negative predictive values ​​for both groups (rejection vs. non-rejection) when markers were processed by the algorithm. DETAILED DESCRIPTION OF THE INVENTION

[0029] The present invention is illustrated by the following examples, which are not intended to limit the scope of protection of the invention. [Example]

[0030] Example 1. Materials and Methods Example 1.1. Measurement of cytokines, chemokines, and other soluble molecules by enzyme-linked immunosorbent assay (ELISA) The samples were subjected to ELISA assays by using commercial kits specific for each molecule according to the manufacturer's instructions.

[0031] Example 1.2. Measurement of mitochondrial DNA Mitochondrial DNA was isolated using the DNeasy Blood & Tissue Kit (QIAGEN). Standard human mitochondrial DNA was obtained from the isolated mitochondria. A mitochondrial DNA standard curve was obtained for each assay for absolute quantification of mitochondrial DNA. Quantitative PCR was performed using TB Green™ Premix Ex Taq (Takara Bio Inc., Japan) on an iQ5 Real-Time PCR System (Bio-Rad). The following primers were used: human mitochondrial cytochrome b (F: 5h-CCCCACAAACCCCATTACTAAACCCA-3' (SEQ ID NO: 1) and R: 5'-TTCATCATGCGGAGATGTTGGATGG-3' (SEQ ID NO: 2)).

[0032] Example 1.3. Measurement of membrane protein expression in human umbilical vein endothelial cells (HUVEC) HUVECs were cultured in Gibco™ Medium 2000 supplemented with low serum growth supplement (ThermoFisher Scientific) in the absence of antibiotics. 5 The cells were cultured in 24-well plates and incubated for 16 hours in the presence of organ preservation solution recovered from the excised liver after cold ischemic preservation.

[0033] The membrane expression of these molecules was measured by flow cytometry. Cultured HUVECs were detached from the wells and then stained with specific fluorescent dye antibodies.

[0034] After staining, cells were subjected to flow cytometric analysis on a BD FACSCanto flow cytometer and FACSDiva software (BD Biosciences) by gating on singlets based on forward scatter (FSC-A, FSC-H) and side scatter (SSC-A) parameters. Data were analyzed using FCS Express 5 software (DeNovo Software, Pasadena, CA, USA).

[0035] Example 2. Results In a prospective clinical study of 80 transplant patients (Tables 3 and 4), we identified 80 markers (Tables 1 and 2) released by donor livers. These markers were expressed more or less differently in patients with acute rejection (Tables 5, 6, and Figure 1). Detection of these markers was performed within the first 48 hours after liver transplantation. Of these markers, those with statistical differences greater than 0.001 (Table 1) were selected for further analysis. Figures 1–4 show differences in marker levels less than 0.001. Next, we combined the marker expression levels with other information to create an algorithm that included combinations of the following biomarkers: fibronectin, hyaluronan, MIP1B, mitDNA, MMP7, MMP8, angiogenin, and ANGPT2 (Figure 4). Furthermore, we calculated all other possible combinations among markers that showed significant differences less than 0.001 (Table 8). These significantly different markers were then combined with the remaining markers (Table 9) to establish combinations ranging from two markers, three markers, etc., to a combination using all markers. The area under the ROC curve was calculated for each marker combination, considering only those combinations that showed an area under the curve greater than 0.7. Using these markers, an algorithm was created and refined until two differentiated groups were obtained (Table 7, Figures 2 and 3). This algorithm allows for the identification of transplant patients likely to experience acute rejection. The algorithm has been validated in several dozen liver transplant patients, demonstrating specificity and sensitivity values ​​of 99.6% (Figure 4). Furthermore, the algorithm has a positive predictive value of 100% and a negative predictive value of 95%.

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Claims

1. 1. An in vitro method for predicting organ graft rejection in a subject in need of transplantation, comprising: a) detecting in an organ preservation solution or a blood sample obtained from the patient, fibronectin, hyaluronic acid, MIP1B, HLADR_FCM, mitDNA, MMP-7, MMP-8, angiogenin, angiopoietin-2, thrombospondin-1, MMP-3, ADAM9, ADAMTS1, ADAMTS13, cathepsin L, bile acids, Casp_1, Casp_3, Casp_8, cathepsin A, cathepsin B, cathepsin D, cathepsin S, CCL20, CCL28, CCL5, CX3CL1, CXCL16, DNA, DPPIV, endoglin, endostatin / type XVIII collagen, FFA, FGF. basic, HGF, HMGB1, HSP70, HSP90, ICAM-1, IGFBP-1, IGFBP-2, IGFBP-3, IL10, IL17A, IL18, IL18BP, IL1α, IL1β, IL6, IL8, IP10, kallikrein 6 (KLK6), LDH, leptin, LiveMitoc, LPS, MCP1, MMP-2, MMP-9, nucleosome, PCSK9, pentraxin 3 (PTX3), platelet factor 4 (PF4), prolactin, Prot, PRTN3, myeloblastin, P-selectin, Serpin E1, Serpin 1. An in vitro method comprising: assessing the concentration or expression level of at least one biomarker selected from the group consisting of TIMP-1, TIMP-4, uPAr, uric acid, VCAM-1, VEGF, cathepsin X, GDNF, cathepsin Z and / or cathepsin P, or any combination thereof; and b) identifying a deviation or variation in said concentration or expression level relative to a pre-set threshold level determined in a control subject, wherein said subject is indicative of a likelihood of rejecting an organ transplant.

2. 2. An in vitro method for predicting organ graft rejection in a subject in need of transplantation according to claim 1, comprising: a) assessing the concentration or expression level of at least one biomarker selected from the group consisting of fibronectin, hyaluronic acid, MIP1B, mitDNA, MMP7, MMP8, angiogenin and / or ANGPT2, or any combination thereof, in an organ preservation solution or in a blood sample obtained from the patient; and b) identifying a deviation or variation in said concentration or expression level relative to a predetermined threshold level determined in control subjects, which indicates that said subject is likely to undergo organ graft rejection.

3. 3. An in vitro method for predicting organ graft rejection in a subject in need of transplantation according to claim 1 or 2, comprising: a) assessing the concentration or expression level of at least one combination of biomarkers selected from Table 8 or Table 9 in an organ preservation solution or in a blood sample obtained from the patient; and b) identifying a deviation or variation in said concentration or expression level relative to a pre-set threshold level determined in control subjects, which is indicative of the subject being likely to undergo organ graft rejection.

4. 4. The in vitro method of claim 1, comprising assessing the concentration or expression level of a combination of the biomarkers fibronectin, hyaluronic acid, MIP1B, mitDNA, MMP7, MMP8, angiogenin and ANGPT2 in an organ preservation solution or in a blood sample obtained from the patient, wherein b) identifying a deviation or variation in said concentration or expression level relative to a pre-set threshold level determined in a control subject indicates that said subject is likely to undergo organ transplant rejection.

5. An in vitro method according to any one of claims 1 to 4 for predicting acute rejection of an organ transplant, most preferably acute rejection of a liver transplant, in a subject in need of a transplant.

6. Fibronectin, hyaluronic acid, MIP1B, HLADR_FCM, mitDNA, MMP-7, MMP-8, angiogenin, angiopoietin-2, thrombospondin-1, MMP-3, ADAM9, ADAMTS1, ADAMTS13, cathepsin L, bile acids, Casp_1, Casp_3, Casp_8, cathepsin A, cathepsin B, cathepsin D, cathepsin S, CCL20, CCL28, CCL5, CX3CL1, CXCL16, DNA, DPPIV, endoglin, endostatin / type XVIII collagen, FFA, FGF for predicting organ graft rejection in a subject in need of transplantation. basic, HGF, HMGB1, HSP70, HSP90, ICAM-1, IGFBP-1, IGFBP-2, IGFBP-3, IL10, IL17A, IL18, IL18BP, IL1α, IL1β, IL6, IL8, IP10, kallikrein 6 (KLK6), LDH, leptin, LiveMitoc, LPS, MCP1, MMP-2, MMP-9, nucleosome, PCSK9, pentraxin 3 (PTX3), platelet factor 4 (PF4), prolactin, Prot, PRTN3, myeloblastin, P-selectin, Serpin E1, Serpin In vitro use of at least one biomarker selected from the group consisting of fibronectin, hyaluronic acid, MIP1B, mitDNA, MMP7, MMP8, angiogenin, and / or ANGPT2, or any combination thereof; or at least one combination of biomarkers selected from Table 8 or Table 9; or a combination of the biomarkers fibronectin, hyaluronic acid, MIP1B, mitDNA, MMP7, MMP8, angiogenin, and ANGPT2.

7. 8. The in vitro use according to claim 7 for predicting acute rejection of an organ transplant, most preferably acute rejection of a liver transplant, in a subject in need of a transplant.

8. A kit suitable for carrying out the method according to any one of claims 1 to 5, comprising: fibronectin, hyaluronic acid, MIP1B, HLADR_FCM, mitDNA, MMP-7, MMP-8, angiogenin, angiopoietin-2, thrombospondin-1, MMP-3, ADAM9, ADAMTS1, ADAMTS13, cathepsin L, bile acids, Casp_1, Casp_3, Casp_8, cathepsin A, cathepsin B, cathepsin D, cathepsin S, CCL20, CCL28, CCL5, CX3CL1, CXCL16, DNA, DPPIV, endoglin, endostatin / type XVIII collagen, FFA, FGF basic, HGF, HMGB1, HSP70, HSP90, ICAM-1, IGFBP-1, IGFBP-2, IGFBP-3, IL10, IL17A, IL18, IL18BP, IL1α, IL1β, IL6, IL8, IP10, kallikrein 6 (KLK6), LDH, leptin, LiveMitoc, LPS, MCP1, MMP-2, MMP-9, nucleosome, PCSK9, pentraxin 3 (PTX3), platelet factor 4 (PF4), prolactin, Prot, PRTN3, myeloblastin, P-selectin, Serpin E1, Serpin 10. A kit comprising reagents or tools for assessing the concentration or expression level of at least one biomarker selected from the group consisting of Fi, SPECKS, THP1_IL1, TIMP-1, TIMP-4, uPAr, uric acid, VCAM-1, VEGF, cathepsin X, GDNF, cathepsin Z and / or cathepsin P, or any combination thereof; or at least one biomarker selected from the group consisting of fibronectin, hyaluronic acid, MIP1B, mitDNA, MMP7, MMP8, angiogenin and / or ANGPT2, or any combination thereof; or at least one combination of biomarkers selected from Table 8 or Table 9; or a combination of the biomarkers fibronectin, hyaluronic acid, MIP1B, mitDNA, MMP7, MMP8, angiogenin and ANGPT2.

9. Use of the kit according to claim 8 for predicting organ transplant rejection.

10. 10. Use according to claim 9 of the kit according to claim 8 for predicting liver transplant rejection.

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