IN VITRO METHOD FOR PREDICTING ORGAN TRANSPLANT REJECTION

FIELD OF THE INVENTION

The present invention refers to the medical field. Particularly, the present invention refers 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 thereof.

STATE OF THE ART

Deceased donor liver transplantation is nowadays a routine procedure for the treatment of terminal liver failure and often represents the only chance of a cure. Although donation after brain death (DBD) account for the bulk of organ donors, in the recent years there has been a growing interest in increasing the donor pool. As a result, organ donation after circulatory death (DCD) has been reintroduced and contributes to increasing the number of donations in many countries. These donations currently account for 32 % of donors in Spain, and it has been one of the keys applied to reach the number of 46 donors/million inhabitants in 2017.

Between 30 and 40% of transplanted patients will suffer some episode of acute rejection or early graft dysfunction in the first weeks after transplantation. However, the occurrence of acute rejection episodes increases by 40% the probability of suffering chronic rejection and loss of the transplant in the medium term. In addition, up to 4% of these patients lose the organ in the first weeks, leading to the need for a new emergency re-transplantation, which cannot always be carried out. All this puts the life of the patients at risk and also determines that 30% of the patients end up dying from rejection-related causes. In addition, the identification of a rejection episode is done when it is occurring. To date, there is no tool that allows early prediction of the appearance of these episodes and their serious consequences for patient health. So, there is an unmet medical need of finding reliable methods for predicting organ transplant rejection, which may help clinicians to select, in advance, possible treatments that could be needed, thus improving the chances of treatment success.

The present invention is thus focused on solving this problem, and a new method for predicting organ transplant rejection, preferably acute organ transplant rejection, most preferably acute liver transplant rejection, is herein provided. DESCRIPTION OF THE INVENTION

Brief description of the invention

As cited above, the present invention refers to a method for predicting organ transplant rejection, preferably acute organ transplant rejection, most preferably acute liver transplant rejection, in a subject in need thereof, which comprises assessing the concentration or expression level of specific biomarkers, wherein the identification of a variation or deviation (increase or decrease) in the concentration or expression level with respect to a pre-established threshold level determined in control subjects, is an indication that the subject may suffer organ transplant rejection.

Particularly, the inventors assayed the biomarkers shown in Table 1 and Table 2. Then, the biomarkers were quantified such as it is shown in Table 5 and Table 6. Interestingly, such as it is shown in Table 8 and Table 9, several combinations of at least two of the assayed biomarkers are associated with the prediction of organ transplant rejection showing high ranked AUC values, always above 0.7.

Such as it is shown in Figure 1, Figure 2 and Figure 3, there are clear differences between each marker when comparing the non-rejection group and the acute rejection group. In a preferred embodiment, the following combination of biomarkers were assayed: Fibronectin, Hyaluronic acid, MIP1B, mitDNA, MMP7, MMP8, Angiogenin and ANGPT2, giving rise to a Sensitivity and Specificity of 99.6% (see Figure 4).

This means that any individual biomarker of Table 1 and Table 2, or any combination thereof, comprising from at least two biomarkers to all the biomarkers of Table 1 and Table 2, (i.e., three, four, five. . . .until all the biomarkers of Table 1 and Table 2) may be used, according to the present invention to predict organ transplant rejection in a subject in need thereof.

So, the first embodiment of the present invention refers to an in vitro method (hereinafter method of the invention) for predicting organ transplant rejection in a subject in need thereof which comprises: a) Assessing the concentration or expression level of at least a biomarker selected from the group consisting of: Fibronectin (FIBRONECT), Hyaluronic (HYALURON), MIP1B, HLADR FCM, mitDNA, MMP-7, MMP-8, Angiogenin, Angiopoietin-2, Thrombospondin- 1, MMP-3, AD AMP, ADAMTS1, ADAMTS13, Cathepsin L, BILE ACID, Casp l, 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, ILla, ILlbeta, IL6, IL8, IP10, Kallikrein 6 (KLK6), LDH, Leptin, LiveMitoc, LPS, MCP1, MMP-2, MMP-9, Nucleosomas, PCSK9, Pentraxin 3 (PTX3), Platelet Factor 4 (PF4), Prolactin, Prot, PRTN3, Myeloblastin, PSELECTINE, Serpin El, Serpin Fl, 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, in the organ preservation solution or in a blood sample obtained from the patient, and b) wherein the identification of a deviation or variation (increase or decrease) in the concentration or expression level with respect to a pre-established threshold level determined in control subjects, is an indication that the subject may suffer organ transplant rejection.

In a preferred embodiment, the method of the invention refers to an in vitro method for predicting organ transplant rejection in a subject in need thereof which comprises: a) assessing the concentration or expression level of at least a biomarker selected from the group consisting of: Fibronectin, Hyaluronic acid, MIP1B, mitDNA, MMP7, MMP8, Angiogenin and/or ANGPT2 , or any combination thereof, in the organ preservation solution or in a blood sample obtained from the patient, and b) wherein the identification of a deviation or variation (increase or decrease) in the concentration or expression level with respect to a pre-established threshold level determined in control subjects, is an indication that the subject may suffer organ transplant rejection.

In a preferred embodiment, the method of the invention refers to an in vitro method for predicting organ transplant rejection in a subject in need thereof which comprises: a) assessing the concentration or expression level of at least a combination of biomarkers selected from Table 8 or Table 9, in the organ preservation solution or in a blood sample obtained from the patient, and b) wherein the identification of a deviation or variation (increase or decrease) in the concentration or expression level with respect to a pre-established threshold level determined in control subjects, is an indication that the subject may suffer organ transplant rejection.

In a preferred embodiment, the method of the invention refers to an in vitro method for predicting organ transplant rejection in a subject in need thereof which comprises assessing the concentration or expression level of the following combination of biomarkers: Fibronectin, Hyaluronic acid, MIP1B, mitDNA, MMP7, MMP8, Angiogenin and ANGPT2, in the organ preservation solution or in a blood sample obtained from the patient, and b) wherein the identification of a deviation or variation (increase or decrease) in the concentration or expression level with respect to a pre-established threshold level determined in control subjects, is an indication that the subject may suffer organ transplant rejection.

In a preferred embodiment, the method of the invention refers to an in vitro method for predicting acute organ transplant rejection, most preferably acute liver transplant rejection, in a subject in need thereof.

The second embodiment of the present invention refers to the in vitro use of at least a biomarker selected from the group consisting of Fibronectin, Hyaluronic, MIP1B, HLADR FCM, mitDNA, MMP-7, MMP-8, Angiogenin, Angiopoietin-2, Thrombospondin- 1, MMP-3, ADAM9, ADAMTS1, ADAMTS13, Cathepsin L, BILE ACID, Casp l, 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, IL 10, IL 17 A, IL 18, IL18BP, ILla, ILlbeta, IL6, IL8, IP10, Kallikrein 6 (KLK6), LDH, Leptin, LiveMitoc, LPS, MCP1, MMP-2, MMP-9, Nucleosomas, PCSK9, Pentraxin 3 (PTX3), Platelet Factor 4 (PF4), Prolactin, Prot, PRTN3, Myeloblastin, PSELECTINE, Serpin El, Serpin Fl, 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 of at least a biomarker selected from the group consisting of Fibronectin, Hyaluronic acid, MIP1B, mitDNA, MMP7, MMP8, Angiogenin and/or ANGPT2, or any combination thereof; or of at least a combination of biomarker selected from Table 8 or Table 9; or of the following combination of biomarkers: Fibronectin, Hyaluronic acid, MIP1B, mitDNA, MMP7, MMP8, Angiogenin and ANGPT2, for predicting organ transplant rejection in a subject in need thereof.

In a preferred embodiment, the present invention refers to the in vitro use of the abovedescribed biomarkers for predicting acute organ transplant rejection, most preferably acute liver transplant rejection, in a subject in need thereof.

The third embodiment of the present invention refers to a kit for performing the method of the invention which comprises reagents or tools for assessing the concentration or expression level of at least a biomarker selected from the group consisting of Fibronectin, Hyaluronic, MIP1B, HLADR FCM, mitDNA, MMP-7, MMP-8, Angiogenin, Angiopoietin-2, Thrombospondin- 1, MMP-3, ADAM9, ADAMTS1, ADAMTS13, Cathepsin L, BILE ACID, Casp l, 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, IL 10, IL 17 A, IL 18, IL18BP, ILla, ILlbeta, IL6, IL8, IP10, Kallikrein 6 (KLK6), LDH, Leptin, LiveMitoc, LPS, MCP1, MMP-2, MMP-9, Nucleosomas, PCSK9, Pentraxin 3 (PTX3), Platelet Factor 4 (PF4), Prolactin, Prot, PRTN3, Myeloblastin, PSELECTINE, Serpin El, Serpin Fl, 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 of at least a biomarker selected from the group consisting of: Fibronectin, Hyaluronic acid, MIP1B, mitDNA, MMP7, MMP8, Angiogenin and/or ANGPT2, or any combination thereof; or of at least a combination of biomarker selected from Table 8 or Table 9; or of the following combination of biomarkers: Fibronectin, Hyaluronic acid, MIP1B, mitDNA, MMP7, MMP8, Angiogenin and ANGPT2.

The fourth embodiment of the present invention refers to the use of the kit for predicting organ transplant rejection, preferably for predicting liver transplant rejection.

Additionally, the present invention refers to:

An in vitro method for identifying biomarker signatures for predicting organ transplant rejection, preferably for predicting liver transplant rejection, which comprises: a) Assessing the concentration or expression level of at least one of the above cited biomarkers, or any combination thereof, in the organ preservation solution or in a blood sample obtained from the patient, and b) wherein the identification of a deviation or variation (increase or decrease) in the concentration or expression level with respect to a pre-established threshold level determined in control subjects, is an indication that the biomarker signature can be used for predicting organ transplant rejection.

A method for detecting any of the above cited biomarkers, or combinations thereof, in the organ preservation solution or in a blood sample obtained from the patient at risk of suffering organ transplant rejection, the method, comprising: a) contacting the test sample with a primer or antibody specific to the biomarkers. b) amplifying the biomarker in the case of using a primer to produce an amplification product in the test sample or amplify the signal by using a second antibody in the case of using and antibody for detection; and c) measuring the expression level by determining the level of the amplification product or signal in the test sample.

The present invention also refers to a method for treating a patient who has been identified as probably suffering from organ transplant rejection by using the method of the invention, wherein the treatment comprises immunosuppressants such as: Cyclosporines (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.

In a preferred embodiment, the method of the invention is a computer-implemented invention, wherein a processing unit (hardware) and a software are configured to:

• Receive the concentration or expression values of any of the above cited biomarkers or combinations thereof,

• Process the concentration or expression values received for finding substantial variations or deviations, and

• Provide an output through a terminal display of the variation or deviation of the concentration or expression level, wherein the variation or deviation of the concentration or expression level indicates that the subject may suffer from organ transplant rejection.

The present invention also refers to a computer program or a computer-readable media containing means for carrying out a method of the invention.

For the purpose of the present invention the following terms are defined:

• The term "comprising" means including, but not limited to, whatever follows the word "comprising". Thus, use of the term "comprising" indicates that the listed elements are required or mandatory, but that other elements are optional and may or may not be present.

• The term "consisting of’ means including, and limited to, whatever follows the phrase “consisting of’. Thus, the phrase "consisting of’ indicates that the listed elements are required or mandatory, and that no other elements may be present. • Typically, a "threshold value" or "cut-off value" can be determined experimentally, empirically, or theoretically. A threshold value can also be arbitrarily selected based upon the existing experimental and/or clinical conditions, as would be recognized by a person of ordinary skilled in the art. The threshold value must be determined in order to obtain the optimal sensitivity and specificity according to the function of the test and the benefit/risk balance (clinical consequences of false positive and false negative). In one embodiment of the present invention, the threshold value is derived from patients not suffering from organ transplant rejection. Typically, the optimal sensitivity and specificity (and so the threshold value) can be determined using a Receiver Operating Characteristic (ROC) curve based on experimental data.

Description of the figures

Figure 1. Differences between each marker when comparing the non-rejection group (normal; n =47) and the acute rejection group (AR; n = 20). Dots represent the median value for each of the markers.

Figure 2. Heat map of sample classification when using selected markers. A. The heat map represents the clustering established when using different algorithmic measurements. When the color is darker, it indicates a closer affinity to the non-rejection group, while if the color is lighter, it represents a stronger affinity to the rejection group. Two clusters are observed corresponding to the two existing groups. B. Representation of the observed distances between the selected markers when processing the data using 2 algorithmic methodologies. Both groups (Non-rejecters and Rejecters) are well-defined, where colors closer to black indicate shorter distances, meaning patients belonging to the same group, while lighter colors represent greater distances or patients from different groups after compared every patient with the others. A clear clustering was found among all patients who are non-rejecters and patients who are rejecters. (NR: non-rejecters; P: rejecters).

Figure 3. Graphical representation of the rejection vs non-rejection groups after processing the results with different algorithms. The ellipses represent the dispersion of results for all patients included. There are significant differences between the two groups with a p < 0.05.

Figure 4. A. ROC curve from the data obtained in the rejection and non-rejection groups when the markers are analyzed by using an algorithm comprising the following combination of biomarkers: Fibronectin, Hyaluronic acid, MIP1B, mitDNA, MMP7, MMP8, Angiogenin and ANGPT2. The ROC space was calculated in 99.6%. B. Table showing the calculation of the positive and negative predictive values of both groups (rejection vs non-rejection) when the markers are processed by the algorithm.

Detailed description of the invention

The present invention is illustrated by means of the examples below without the intention of limiting its scope of protection.

Example 1. Material and methods

Example 1.1. Determination of cytokines, chemokines and other soluble molecules by enzyme-linked immunosorbent assay (ELISA)

Samples were subjected to ELISA assay by using specific commercial kits for each molecule and following the manufacturer’s instructions.

Example 1.2. Determination of mitochondrial DNA

Mitochondrial DNA was isolated using a DNeasy Blood &Tissue Kit (QIAGEN). The standard human mitochondrial DNA was obtained from mitochondria isolated. The standard curve of mitochondrial DNA was obtained in each assay for absolute quantification of mitochondrial DNA. Quantitative PCR was employed to measure mitochondrial DNA using TB Green TM Premix Ex Taq (TAKARA BIO INC, Japan) in an iQ5 Real- Time PCR System (Bio- Rad). The following primer was used: human mitochondrial cytochrome b (F: 5h-

CCCCACAAACCCCATTACTAAACCCA- 3' (SEQ ID NO: 1) and R 5'- TTCATCATGCGGAGATGTTGGATGG- 3 ) (SEQ ID NO: 2).

Example 1.3. Determination of membrane protein expression in Human Umbilical Vein Endothelial Cells (HUVECs)

HUVECs were cultured in Gibco™ Medium 2000 supplemented with Low Serum Growth Supplement (ThermoFisher Scientific) in the absence of antibiotics. 10 ⁵ cells/well were cultured in 24-well plates and incubated 16 h in the presence of organ preservation solution recollected from explanted livers after cold ischemia storage. Membrane expression of those molecules was determined by flow cytometry. Cultured HUVECs were detached from wells and then stained with specific fluorescent stained antibodies.

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

Example 2. Results

In a prospective clinical study performed in 80 transplanted patients (Table 3 and Table 4), 80 markers (Tables 1 and 2) released by the donor liver have been identified, which are differentially expressed to a greater or lesser degree in those who suffer acute rejection (Table 5, Table 6 and Figure 1). The detection of these markers is performed in the first 48 hours after liver transplantation. Among all these markers, those with statistical differences of over 0.001 (Table 1) were selected for further analysis. Figures 1 to 4 show the existing differences in the levels of the markers whose differences are below 0.001. In turn, the expression levels of the markers were combined with other information to generate an algorithm which comprises the following combination of biomarkers: Fibronectin, Hyaluronic acid, MIP1B, mitDNA, MMP7, MMP8, Angiogenin and ANGPT2 (Figure 4). Additionally, all other possible combinations were calculated among the markers that showed highly significant differences below 0.001 (Table 8). These highly significant markers were also combined with the remaining markers (Table 9), establishing combinations ranging from 2 markers, 3 markers, and so forth, up to combinations using the total number of markers. The area under the ROC curve was calculated for each marker combination, considering only those combinations that exhibited areas under the curve exceeding 0.7. With these markers, an algorithm was generated and refined until two differentiated groups were obtained (Table 7, Figure 2 and Figure 3). This algorithm makes it possible to determine in transplanted patients those who are going to suffer an episode of acute rejection. This algorithm has been validated with dozens of liver transplant patients and shows specificity and sensitivity values of 99.6% (Figure 4). In addition, the algorithm has positive predictive values of 100%, while the negative predictive values are 95%. TABLE 1. List of main biomarkers, including the biological process, as well as their identifier in protein databases (Uniprot ID) and their gene identifier (NCBI Entrez Gene ID).

TABLE 2. List of other biomarkers, including the biological process, as well as their identifier in protein databases (Uniprot ID) and their gene identifier (NCBI Entrez Gene ID).

TABLE 3. Demographic and clinical data of total organ donors and receptors. Continuous variables are expressed as mean ± SD; median (range). Qualitative variables are expressed as frequency (%). DBD, donation after brain death; DCD, donation after cardiac death; NRP, normothermic regional perfusion; HCV, hepatitis C virus; HB V, hepatitis B virus; NASH, non-alcoholic steohepatitis; KT, Kehr tube. TABLE 4. Demographic and clinical data of organ donors and receptors which fulfil the inclusion criteria (No DCD, no re-transplant and no anastomoses with a Kehr tube). Continuous variables are expressed as mean ± SD; median (range). Qualitative variables are expressed as frequency HBV, hepatitis B virus; NASH, nonalcoholic steohepatitis.

TABLE 5. Quantification of selected markers in the studied samples. Variables are expressed as median (range).

Fibronectin (nmol/pl) 1.592 (18-17.497)

Hyaluronan (nmol/pl) 80.84 (12.14-323.40)

Mitochondrial ONA (pg/ml) 5.27 (0.10-125.70)

MIP1B (pg/ml) 87.45 (0-629.8)

MMP3 (pg/ml) 232.3 (15.07-4,649)

MMP7 (pg/ml) 242.4 (62.07-1,653)

MMP8 (pg/ml) 744.1 (2.81-9,219)

Angiogenin (pg/ml) 96.45 (4.94-1,765)

TABLE 6. Quantification of the rest of markers in the studied samples. Variables are expressed as median (range).

IP10 (pg/ml) 172.1 (0-995)

LDH (Relative units/ml) 2.5 (1-4.2)

Live mitochondria (number/ml) 34,764 (3,013-195,093)

LPS (EU/ml) 0.077 (0.039-0.242)

MIP-1A (pg/ml) 4 (0-19.31 )

MCP1 (pg/ml) 1,270 (0-8,863)

Nucleosomes (Relative units/ml) 37 (6-183)

Soluble specks of ASC (number/ml) 25,762 (1,936-305,204)

P-Selectine (pg/ml) 67,005 (0-295,855)

IL-18 (pg/ml) 94.51 (0-5,998)

IL-18BP (pg/ml) 302.2 (101.1-1 ,175)

Variables are expressed as median (range).

TABLE 7. Confusion Matrix after processing every sample by 2 different algorithmic methodologies, for all selected biomarkers. Method 1 samples were clustered in 2 different groups (Rejection and Non Renection). Method 2, samples were clustered in 3 different categories, rejection, non-rejection and surveillance. The column labelled as total represents percentage of the number of algorithm matches when a patient is a rejecter or non-rejecter for selected markers.

Rejecton 38 0 — 100 %

1

Non Rejection 3 59 — 95 %

Rejection 26.7 0 0 100 %

2 Non Rejection 0 46.7 0 100 %

Surveillance 0 0 26.7 100 %

TABLE 8. Results after the ROC analysis when comparing the minimal combination of key markers to achieve area under the curve (AUC) values above 0.7. AUC represents the area under the ROC curve. AUCs value above 0.7 indicates reliabilities above 70%. t- . TABLE 9. Results after the ROC analysis when comparing the minimal combination of all markers to achieve area under the curve (AUC) values above 0.7. AUC represents the area under the ROC curve. AUC values above 0.7 indicates reliabilities above 70%.

AUC represents the area under the ROC curve. AUC values above 0.7 indicates reliabilities above 70%.