METHODS FOR PREDICTING GRAFT ALTERATIONS

Abstract

The invention relates to a method for predicting graft alterations in a transplanted patient, comprising a step of determining the expression levels of bactericidal/permeability-increasing protein (BPI), chemokine (C motif) ligand 1 (XCL1) and thioredoxin domain containing 3 (TXNDC3) genes in a biological sample obtained from said transplanted patient.

Claims

1. A method for predicting graft alterations in a transplanted patient, wherein the method comprises a step of determining the expression levels of bactericidal/permeability-increasing protein (BPI), chemokine (C motif) ligand 1 (XCL1) and thioredoxin domain containing 3 (TXNDC3) genes in a biological sample obtained from said transplanted patient.

2. The method according to claim 1, wherein the method further comprises a step of comparing said expression levels with their respective predetermined reference levels, wherein a decrease in the expression levels of BPI, XCL1 and TXNDC3 genes is predictive of graft alterations.

3. The method according to claim 1, wherein said graft alterations are interstitial fibrosis and tubular atrophy (IFTA), inflammatory IFTA and alloimmune lesions.

4. The method according to claim 1, wherein the transplanted patient is a kidney transplanted patient.

5. The method according to claim 1, wherein said biological sample is a urine sample or a blood sample.

6. The method according to claim 1, wherein said biological sample is obtained 3-months post-transplantation.

7. The method according to claim 1, wherein the expression levels of BPI, XCL1 and TXNDC3 genes is determined by measuring the amount of nucleic acid transcripts (mRNA) of said genes.

8. A kit suitable for performing the method according to claim 1, wherein said kit comprises means for measuring the expression levels of BPI, XCL1 and TXNDC3 genes.

9. (canceled)

10. The kit according to claim 8, wherein said means for measuring the expression levels of BPI, XCL1 and TXNDC3 genes are nucleic acid primers and/or probes specific for said genes.

11. The kit according to claim 8, wherein said means for measuring the expression levels of BPI, XCL1 and TXNDC3 genes are antibodies or aptamers specifically binding to proteins encoded by said genes.

12. A method for adjusting the immunosuppressive treatment administered to a transplanted patient following its transplantation, wherein the method comprises the steps of: (i) determining the expression levels of bactericidal/permeability-increasing protein (BPI), chemokine (C motif) ligand 1 (XCL1) and thioredoxin domain containing 3 (TXNDC3) genes in a biological sample obtained from said transplanted patient, and (ii) adjusting the immunosuppressive treatment.

13. The method according to claim 11, wherein the step of adjusting the immunosuppressive treatment consists of modulating the amount administered to the transplanted patient and/or administering another immunosuppressive drug to the transplanted patient.

Description

FIGURES

[0087] FIG. 1: Quantification of interstitial fibrosis using an automated image analysis pre-(A) and 1-year post-transplantation (B) and eGFR with MDRD formula 3 months post-transplantation (C) and at 1 year post transplantation (D).

[0088] FIG. 2: ROC curves comparing NL group to the others combined. BPI, TXNDC3, XCL1 and the sum of values for these three genes exhibit significant discrimination (p=0.043, 0.029, 0.025 and 0.00015, respectively).

[0089] FIG. 3: Venn diagram of differential genes for each group compared to NL. The number of differential genes for each comparison is displayed with the overlap.

[0090] FIG. 4: Blood phenotype displaying significant differential subsets compared to the NL group.

EXAMPLE: Blood Gene Expression at 3 Months Post-Transplantation Predicts 1-Year Histological Features in Renal Transplantation

[0091] Material & Methods

[0092] Concise Methods

[0093] Patients: A multicentre cohort of 79 renal transplant patients were analyzed in this study, given a grant by the French Health Ministry (no id RCB: DGS2006/0200) and approved by the University Hospital Ethical Committee and the Committee for the Protection of Patients from Biological Risks of Nantes (France). All patients gave informed consent. Six French transplant centers participated to the study (Nantes, Paris-Necker, Lyon-Heriot, Grenoble, Toulouse and Nice). Inclusion criteria were: adult patients under 66 years old who received a first isolated kidney transplantation from a heart-beating deceased donor of less than 60 years old.

[0094] Histopathological Analyses:

[0095] Histological diagnostic was centralized and performed according to the updated Banff classification 2009 .sup.13 by 2 pathologists (K.R. and J.P.D.V.H.) blinded to the blood signatures. Pre and 1-year post-transplantation biopsies were assessed. Patients were classified in the 4 following groups: 1) normal, 2) isolated IFTA without inflammation (IFTA), 3) isolated IFTA with inflammation (i-IFTA), 4) alloimmune lesions (ALLO group) gathering borderline changes, acute or chronic T-cell mediated rejection and acute or chronic antibody-mediated rejection (c-ABMR). A diagnosis of isolated IFTA with or without inflammation (IFTA and i-IFTA) was given when no other patent histological associated lesion was observed and when the IFTA was not observed on the pre-implantation histology graft. The i-IFTA was defined by a ti-score>0 i.e. by the presence of interstitial inflammation on the scarred cortex area (i-atr) associated or not with inflammation in normal parenchyma (i-Banff) with an insufficient scoring to give a borderline change or T cell mediated rejection diagnosis (i-Banff=0 or 1).sup.27. Patients with BK-virus nephropathy, as defined by the blood rate of virus replication by RT-PCR>4 log and renal dysfunction, were excluded from the study.

[0096] Blood Sample Preparation:

[0097] Venous blood samples were collected at 3 month post-transplantation in EDTA vacutainers and processed for analysis within 4 hours. Peripheral Blood Mononuclear Cells (PBMC) were separated on a Ficoll layer (Eurobio, Les Ulis, France). RNA was extracted from peripheral blood using the TRIzol method (Invitrogen, Cergy Pontoise, France).

[0098] Microarrays Analysis:

[0099] Analysis was performed on Affymetrix Human Genome U133 Plus 2.0 arrays. Microarray data from all samples were normalized using the robust multi-array algorithm (RMA) and a batch correction was performed with Combat algorithm according microarrays processed on Nantes and on the other centers data. A student test modified for multitesting (p-value <0.05) and a fold change of at least 1.25 were used to identify differentially expressed genes. The biological significance of selected genes was assessed using GOminer software.sup.28. Raw microarray data were deposited in the Gene Expression Ominbus (GEO) database and are accessible through GEO series accession number GSE57731.

[0100] Blood Phenotyping:

[0101] Flow cytometry was performed on a BD LSRII (BD Biosciences, Pont de Claix, France) and data were analyzed with FlowJo software (TriStar Inc, Ashland, Oreg., USA) using five combinations of monoclonal antibodies (mAb). All the experiments were performed on frozen PBMC.

[0102] Statistical analysis: non-parametric Mann-Whitney or Kruskal Wallis tests were used for group comparisons. Logistic regression was used to assess relationship between 12-months biopsy (NL versus other groups altogether) and co-variables. Statistical tests were done using R software. Graph PadPrism v.4 software (Graph Pad Prism Software, San Diego, Calif., USA) was used and differences were represented as follows: * when p<0.05, ** when p<0.01 and *** when p<0.001.

[0103] Results

[0104] Demographic Characteristics:

[0105] 125 first-time kidney graft recipients were screened from which 79 fulfilled the complete one year follow-up with interpretable histology prior to graft implantation and at one year. Mean of age of recipients was 43.67 years ±SD: 10.47 (range: 20.10-63.0) with 73% male and mean donor age was 41.31±12.41 (12.0-65.0) with 61% male (table 1). The mean of HLA-A-B-DR incompatibility was 3.44±1.09 (0.0-5.0). All patients received Tacrolimus and mycophenolate mofetil. All patients received Basiliximab induction therapy except 2 who received rabbit thymoglobulin. Only 2 patients were free of steroids, one with diabetes and the other leucopoenia. No death occurred during the first year post-transplant, with an eGFR mean at one year of 57.14 mL/min/1.73 m.sup.2±16.88 (17.71-108.0) and a daily proteinuria of 0.26 g/24 h±0.31 (0.0-2.0). Eight patients (10%) presented an acute rejection episode within the first year. At one year, 4 patients displayed DSA.

TABLE-US-00001 TABLE 1 Demographic characteristics of the cohort (A) and distribution of Banff criteria (B) (mean ±SD and range or percentages in brackets). P-value of Kruskal-Wallis test is indicated. All NL IFTA iIFTA ALLO A (n = 79) (n = 45) (n = 14) (n = 14) (n = 6) p-value Estimated GFR 53.65 ± 17.78 58.04 ± 17.66 46.34 ± 14.47 45.09 ± 13.71 56.34 ± 25.81 0.082 at 3 months ± (18.39-100.0) (23.87-100.0) (24.12-71.51) (18.39-63.10) (34.46-97.80) mL/min/1.73 m.sup.2 Estimated GFR 57.14 ± 16.88 60.91 ± 16.13 49.00 ± 10.50 52.40 ± 22.18 57.23 ± 16.72 0.071 at 1 year ± (17.71-108.0) (26.56-108)   (35.35-66.60) (17.71-94.36) (34.41-79.03) mL/min/1.73 m.sup.2 Proteinuria at 3 0.23 ± 0.17 0.23 ± 0.19 0.23 ± 0.15 0.23 ± 0.17 0.13 ± 0.08 0.65 months ± g/24 h (0.04-0.78) (0.04-0.78) (0.10-0.68) (0.05-0.55) (0.04-0.20) Proteinuria at 1 0.26 ± 0.31 0.22 ± 0.19   0.20 ± 0.0.11 0.45 ± 0.59 0.18 ± 0.14 0.94 year ± g/24 h (0.0-2.0)  (0.0-0.97)  (0.09-0.41) (0.04-0.36) (0.04-0.36) Male recipient 55/75 29/43 11/13 11/13 4/6 0.072 (73%)   (67%)   (84%)   (84%)   (66%)   Recipient's age 43.67 ± 10.47 43.77 ± 11.01 45.13 ± 10.86 43.33 ± 10.20 40.57 ± 7.382 0.77 (20.10-63.0)  (20.10-63.00) (24.00-59.40) (25.90-59.40) (27.20-48.60) Male donor 44/72 26/41  8/13  8/12 2/6 0.57 (61%)   (63%)   (62%)   (67%)   (33%)   Donor's age 41.31 ± 12.41 41.10 ± 12.86 44.31 ± 14.12 37.83 ± 9.64 43.17 ± 11.11 0.49 (12.00-65.00) (12.00-65.00) (20.00-61.00) (20.00-50.00) (29.00-54.00) Recurrent disease 19/75 13/42  3/13  2/14 1/6 0.60 (25.3%) (31.0%) (23.1%) (14.%)  (16.7%) HLA-ABDR 3.44 ± 1.09 3.43 ± 1.17 3.31 ± 1.11 3.50 ± 0.94 3.67 ± 1.03 0.95 missmatches (0.0-5.0) (0.0-5.0) (1.0-4.0) (1.0-5.0) (2.0-5.0) Basiliximab 70/72 40/41 13/13 11/12 6/6 nd induction (97.2%) (97.6%) (100%)  (91.7%) (100%)  Tacrolimus use 76/76 43/43 13/13 14/14 6/6 nd at initial (100%)  (100%)   (100%)  (100%) (100%)  treatment Corticosteroids 70/72 40/41 13/13 11/12 6/6 nd (97.2%) (97.6%) (100%)  (91.7%) (100%)  DSA at 1 year  4/73  2/41  0/14  1/12 1/6  (5.5%) (4.5%) (0%)   (8.3%)   (16.7%) ARE occurrence  8/77  4/44  1/13  2/14 1/6 nd during the first (10.4%) (9.1%) (7.7%)   (14.3%) (16.7%) year All NL IFTA i-IFTA ALLO Significant B (n = 79) (n = 45) (n = 14) (n = 14) (n = 6) p-value comparison g 0.089 ± 0.33  0.0 ± 0.0 0.14 ± 0.36   0.29 ± 0.0.61   0.17 ± 0.0.41 0.028 NL vs IFTA: (0-2) (0-0) (0-1) (0-2) (0-1) p<0.05 ptc 0.053 ± 0.28  0 ± 0 0 ± 0 0.15 ± 0.38 0.33 ± 0.82 0.028 ns (0-2) (0-0) (0-0) (0-1) (0-2) i 0.089 ± 0.33  0.022 ± 0.15  0 ± 0   0.14 ± 0.0.36 0.67 ± 0.82 0.002 iIFTA vs ALLO: (0-2) (0-0) (0-1) (0-2) p<0.05 NL vs ALLO: p<0.0001 IFTA vs ALLO: p<0.0001 t 0.20 ± 0.54 0.044 ± 0.21  0 ± 0 0.36 ± 0.63  1.5 ± 0.84 P<0.0001 i-IFTA vs ALLO: (0-2) (0-1) (-0) (0-0) (0-0) p<0.0001 NL vs ALLO: p<0.0001 IFTA vs ALLO: p<0.0001 cg 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 (0-0) (0-0) (0-0) (0-0) (0-0) ci  0.66 ± 0.088 0.13 ± 0.34 1.07 ± 0.27   1.43 ± 0.0.65 1.83 ± 0.98 P<0.0001 NL vs IFTA: (0-3) (0-1) (1-2) (1-3) (1-3) p<0.0001 NL vs i-IFTA: p<0.000 NL vs ALLO: p<0.0001 ct 0.85 ± 0.72 0.47 ± 0.50 1.07 ± 0.27 1.43 ± 0.65 1.83 ± 0.98 P<0.0001 ±**: NL vs IFTA; (0-3) (0-1) (0-2) (1-3) (1-3) ***: NL vs i-IFTA; ***: NL vs ALLO; ti 0.44 ± 0.68 0.18 ± 0.39 0 ± 0 1.07 ± 0.27 1.83 ± 0.98 ±***: NL vs i-IFTA; (0-3) (0-1) 2) (1-3) ***: NL vs ALLO; ***: IFTA vs i-IFTA; ***: IFTA vs ALLO

[0106] Demographic Characteristics According to Histological Features:

[0107] Histological analyses were performed on pre- and 1-year post-transplant biopsies for the 79 patients. Pre-implantation biopsies were used to exclude pre-existing features from the analysis i.e. only features appearing after transplantation are analyzed. Patients were classified into four categories following the 2009 Banff classification.sup.13: 45 patients were classified with normal biopsy (NL), 14 isolated IFTA without inflammation (IFTA), 14 isolated IFTA with inflammation (i-IFTA) and 6 presenting alloimmune lesions (ALLO), combining borderline changes, acute or chronic T-cell mediated rejection and acute or chronic antibody-mediated rejection (c-ABMR) (table 1). There was no functional difference between the 4 groups in estimated graft function (eGFR) using the MDRD formula either 3 months post-transplant, when blood samples were harvested for cell phenotype and microarrays analysis, or 1 year post-transplant when histology was performed (FIG. 1, table 1). However, patients with IFTA and i-IFTA displayed around 10 ml/min/1.72 m.sup.2 lower eGFR at 3 months and one year than NL or ALLO patients. This difference is not statistically significant and maybe related to the sample size in each group. Finally, the ALLO immune group tended to have younger recipients than the other groups. Independent quantification of fibrosis using an automated image analysis procedure confirmed a significantly higher percentage of fibrosis 1-year post-transplantation in IFTA and i-IFTA patients compared to NL patients (p<0.05 and p<0.001, respectively), whereas no difference was observed at the time of transplantation (FIG. 1).

[0108] A 3-Month Blood Gene Signature for Normal Histology:

[0109] We first compared the blood gene expression of the patients with NL histology with all the other groups (IFTA, i-IFTA and ALLO) to assess whether gene expression in the blood at 3 months could be associated with “normal vs abnormal” histology at one year. Six genes were significantly differentially expressed between the NL group and the others (supplementary table 1). In addition, three of them, BPI (bactericidal/permeability-increasing protein), XCL1 (chemokine (C motif) ligand 1) and TXNDC3 (thioredoxin domain containing 3) exhibited significant area under the curve (AUC) in receiver operating characteristic analyses (ROC)(supplementary table 1) and the simple combination of these 3 markers, i.e the sum of expression values, allowed the differentiation of NL patients from others, with an AUC of 0.73 (CI.sub.95%=[0.61, 0.85], p=0.00015) (FIG. 2). Exclusion of the 2 patients who received rabbit thymoglobulin induction and the 2 who were free of steroids resulted in a significant AUC of 0.77 (CI.sub.95%=[0.65, 0.88], p<0.0001). In addition, univariate/multivariate analyses highlighted that 3-months eGFR (MDRD formula) and the sum of these three genes were significantly associated with prediction of 1-year biopsy status (Wald p-value=0.025 and 0.0085, respectively). When both parameters were combined, the accuracy of prediction improves with an AUC of 0.75 (CI.sub.95%=[0.64, 0.87], p<0.0001) and a bootstrap resampling validation confirmed the stability of this composite biomarker displaying an AUC of 0.76 (CI.sub.95%=[0.64, 0.86], p<0.0001).

TABLE-US-00002 SUPPLEMENTARY TABLE 1 6 genes significantly differential between normal group and others groups. ROC .sup.  95% Symbol GeneName Entrez FC p p-value AUC IC AUC GPR84 G protein-coupled receptor 53831 −1.33 0.044 0.073 0.62 0.49-0.74 84 BPI bactericidal/permeability- 671 −1.45 0.039 0.043 0.63 0.51-0.76 increasing protein XCL1 chemokine (C motif) ligand 6375 −1.27 0.025 0.025 0.65 0.52-0.77 1 LGALSL lectin, galactoside-binding- 29094 −1.28 0.043 0.066 0.62 0.50-0.75 like TXNDC3 thioredoxin domain 51314 −1.25 0.041 0.029 0.64 0.52-0.76 containing 3 (spermatozoa) BNIP3 BCL2/adenovirus E1B 664 1.28 0.044 0.15 0.63 0.45-0.81 19kDa interacting protein 3

[0110] Gene Signatures Associated with 1-Year Histological Lesions:

[0111] in order to identify blood specific gene signatures at three months associated with each histologic feature at one year, the three groups, IFTA, i-IFTA and ALLO, were compared separately to the NL group (FIG. 3). Ninety three genes (56 down- and 37 up-regulated) were differentially expressed between the IFTA and NL groups. Gene ontology (GO) analysis evidenced gene categories mainly related to cell homeostasis and coagulation/platelet function (table 2), such as integrins alpha 2b and beta 3 (CD41/CD61) and lectins (CLEC1B, -4A, -12A, -12B), which are down-expressed in IFTA compared to NL. Similarly, 27 genes (10 down- and 17 up-regulated) were differentially expressed between the i-IFTA and NL groups. Among these, we identified immune-related genes, such as the under-expressed genes CD8B, XCL1, IL31RA (interleukin 31 receptor A), MIR142 (microRNA 142) and the over-expressed genes CX3CR1 (chemokine (C-X3-C motif) receptor 1), KLRF1 (killer cell lectin-like receptor subfamily F, member 1), EGR1 (early growth response 1), TMEM1764A and TMEM176B (transmembrane proteins 176A and 176B). No significant GO enrichment was found between these two groups of patients, likely due to the too low number of genes. Finally, 291 genes (218 down- and 73 up-regulated) were differentially expressed between the ALLO and NL groups. As expected, GO analysis also highlighted significant enrichments of immune-related genes (table 3) with a total of 54 genes, among which genes coding for CD24 molecule, interleukin 1 receptors (type I, type II and antagonist (IL1R1, IL1R2, IL1R1V), metalloproteinase 8 and 9 (MMP8, MMP9) down-regulated in the ALLO group. Using the cell-type enrichment analysis form Enrichr database.sup.14, we identified an enrichment of cell type transcripts related to CD33.sup.+ myeloid cells and CD14.sup.+ monocytes (p<0.001 and 0.004, respectively) in the ALLO group.

[0112] Altogether, we identified three distinct and significant differential gene signatures in blood at 3 months post-transplantation, associated with 1-year IFTA, i-IFTA and ALLO events with a gradient in favour of immunity related genes in the last group, suggesting that blood signature may be worth considering in clinic. Interestingly, only a small overlap was found between the signatures, reinforcing their specificity (FIG. 3).

TABLE-US-00003 TABLE 2 GO enrichment for the IFTA group compared to the NL. Only GO with false discovery rate (FDR) inferior to 5% were selected. GO Total Changed Enrich- Category GO Name genes genes ment FDR 1 GO: 0050896 response to stimulus 4180 36 2.0 .001 2 GO: 0046903 secretion 636 13 4.7 .001 3 GO: 0002576 platelet degranulation 82 6 16.8 .001 4 GO: 0030168 platelet activation 233 8 7.9 0.003 5 GO: 0007608 sensory perception of 365 9 5.7 0.005 smell 6 GO: 0006887 exocytosis 222 7 7.2 0.008 7 GO: 0007606 sensory perception of 407 9 5.1 .008 chemical stimulus 8 GO: 0032940 secretion by cell 530 10 4.3 0.010 9 GO: 0050878 regulation of body 454 9 4.5 0.014 fluid levels 10 GO: 0007599 hemostasis 386 8 4.8 0.023 11 GO: 0006950 response to stress 2170 21 2.2 0.024 12 GO: 0050817 coagulation 383 8 4.8 0.024 13 GO: 0007596 blood coagulation 380 8 4.8 0.025 14 GO: 0045595 regulation of cell 586 10 3.9 0.026 differentiation 15 GO: 0001775 cell activation 616 10 3.7 0.029 16 GO: 0050793 regulation of 787 11 3.2 0.046 developmental process

TABLE-US-00004 TABLE 3 GO enrichment for the ALLO group compared to the NL. Only GO with FDR inferior to 5% were selected. GO Total Changed Enrich- Category GO Name genes genes ment FDR 1 GO: 0002376 immune system process 1243 42 2.3 0.000 2 GO: 0006952 defense response 773 31 2.7 0.000 3 GO: 0006955 immune response 761 28 2.5 0.003 4 GO: 0009617 response to bacterium 229 14 4.1 0.004 5 GO: 0042742 defense response to 107 9 5.6 0.011 bacterium 6 GO: 0002444 myeloid leukocyte 29 5 11.6 0.019 mediated immunity

[0113] Unique Enrichment of Differential Immune-Related Genes in IFTA, i-IFTA and ALLO Groups:

[0114] IFTA and i-IFTA display, respectively, 93 and 27 genes that are differentially expressed at three months compared to the NL group. Among them, there were only 2 common genes (BAGE3 (B melanoma antigen family, member 3) and DNAJA1P5 (DnaJ (Hsp40) homolog, subfamily A, member 1 pseudogene 5)) (FIG. 3), suggesting different signatures between the two IFTA situations depending on the inflammatory component. To further ascertain this, IFTA and i-IFTA were directly compared and 258 genes were found to be differentially expressed. GO analysis and Enrichr database revealed an enrichment of 58 unique genes, some related to CD14.sup.+ monocytes, including over-expressed genes coding for immune-related molecules such as CD36, CD93, TLR5 (toll-like receptor 5) and CCR2 (chemokine (C—C motif) receptor 2) (table 4). Similarly, the i-IFTA and ALLO groups were directly compared and we found that, among the 291 differential genes associated with the ALLO group and the 27 associated with i-IFTA, only one gene, (BAGE3) was common to the two situations, stressing the idea that the cellular mechanisms and most overall immune mechanisms of alloimmune reactions and i-IFTA are distinct.

TABLE-US-00005 TABLE 4 GO enrichment for the i-IFTA group compared to the IFTA group. Only GO with a FDR inferior to 5% were selected. GO Total Changed Enrich- Category GO Name genes genes ment FDR 1 GO: 0001775 cell activation 616 27 3.3 0.000 2 GO: 0002376 immune system 1243 37 2.3 0.001 process 3 GO: 0042116 macrophage 28 6 16.3  0.002 activation 4 GO: 0045321 leukocyte activation 402 18 3.4 0.002 5 GO: 0002682 regulation of immune 595 22 2.8 0.004 system process 6 GO: 0002274 myeloid leukocyte 75 7 7.1 0.017 activation 7 GO: 0002237 response to molecule 136 9 5.0 0.017 of bacterial origin 8 GO: 0006952 defense response 773 24 2.4 0.020 9 GO: 0032496 response to 125 8 4.9 0.038 lipopolysaccharide 10 GO: 0009617 response to 229 11 3.7 0.040 bacterium 11 GO: 0006950 response to stress 2170 47 1.6 0.041

[0115] Blood Phenotype Analysis: Peripheral Blood from the IFTA (n=10), i-IFTA (n=10) and NL Groups (n=36) were Analyzed at Three Months Following Transplantation Using Flow Cytometry.

[0116] Comparison of the three groups together did not highlight any significant difference in cell frequency. Thus, no phenotype parameter was used to predict 1-year histology in association with gene expression. However, when IFTA was compared to the NL group only, the frequency of unswitched memory CD27.sup.+IgD.sup.+ B cells was significantly lower in the IFTA group (NL=7.70%, CI.sub.95%=[7.48, 12.49]; IFTA=4.48%, CI.sub.95%=[3.50, 7.84], p=0.047) (FIG. 4A). The i-IFTA group, compared to the NL group, was characterized by a lower CD27.sup.−IgD.sup.−IgM.sup.− double negative memory B cell frequency (NL=85.0%, CI.sub.95%=[73.34, 84.49]; i-IFTA=72.05%, CI.sub.95%=[63.15, 80.65], p=0.034) (FIG. 4B) and by a lower expression of NKp80 (MFI) on secretor (NL=1940, CI.sub.95%=[1773, 2192]; i-IFTA=1292, CI.sub.95%=[936.1, 1865], p=0.018) and transitional (p=0.043; NL=1532, CI.sub.95%=[1376, 1705]; i-IFTA=1137, CI.sub.95%[818.7, 1527]) NK cells compared to the NL group (FIG. 4C, 4D).

[0117] Altogether there is a lower frequency of unswitched memory B cells in IFTA and a decrease in double negative memory B cells and NKp80 expression on secretory and transitional NK cells in i-IFTA. No other modification was found at a cellular level.

DISCUSSION

[0118] Histological analysis of allograft biopsy remains the gold standard for assessing graft alterations. Several studies demonstrate that such alterations are associated with changes in transcript sets representing inflammation and injury in renal allografts.sup.7, 8, 12, 15, 16. The presence of histological lesions such as fibrosis and inflammation in 1-year protocol biopsies is associated with reduced graft function and survival.sup.6. However, biopsies can involve severe complications.sup.9, 10.

[0119] We assessed gene expression and cell phenotype in blood from 79 first-time kidney recipients 3 months post-transplant compared with histological statuses one year post-transplantation surveillance biopsy with the aim of determining whether blood could be a valuable compartment for early prediction of graft alterations, reducing the risk of complication and providing tool for early decision-making. Consistent with the development of a predictive biomarker, i.e. before functional alteration, no change in renal function was observed in our cohort between 3 months and 1 year of follow-up. To reduce variability in lesion interpretation as much as possible, histological diagnostic was performed independently by 2 pathologists. Fibrosis quantification, using automated image analysis′, confirmed the significant occurrence of fibrosis between the pre-implantation biopsy and the one-year surveillance biopsies in the IFTA and i-IFTA groups.

[0120] The comparison of patients with normal histology and the 3 groups of patients with abnormal histological features allowed the identification of 6 differentially expressed genes. The combination of 3 of them allows a good discrimination, with an AUC of 0.73 (CI.sub.95%=[0.61, 0.85], p=0.00015). This sum of expression of these 3 genes and the 3-months eGFR were found to be independent predictors of 12-month graft histology and their combination allows reaching a good discriminatory accuracy with an AUC of 0.76 (CI.sub.95%=[0.64, 0.86], p<0.0001) after bootstrap resampling validation. This composite biomarker gives a potential early, minimally invasive biomarker with good predictive ability to distinguish at 3-months post-transplantation patients who will exhibit abnormal biopsy 1 year after transplantation. The identification of predictive biomarkers raises the possibility to stratify patients, avoiding 1-year biopsy for patients predicted to have normal histology and adapting treatment. Such prophylactic treatment would also be of interest for patients with high-risk allografts, including those with a positive cross-match.sup.18, who could benefit from early adjustments to their immunosuppressive regimens.

[0121] Our data confirm that blood is a good compartment, not only for identifying biomarkers associated with allograft injuries as previously reported in acute rejection.sup.11, 12, but also for predicting histological lesions. In addition, because taking samples is minimally invasive and easily repeated, blood potentially represents an optimal and safe source for predictive biomarkers.

[0122] Our data also suggest that 3 months may be a good point for early detection of abnormal events. Indeed, Mengel and colleagues showed that gene expression analysis in 6-weeks protocol biopsies mostly reflected the injury-repair response to implantation stresses following transplantation.sup.19. This suggests that molecular events impacting allograft outcomes are initially hidden by tissue repair response, for example an initial increase in adaptive immune-associated genes 1 month post-transplantation is later reduced in a longitudinal analysis.sup.20. The stability of our signature over time needs to be investigated in order to define during which time lapse its sensitivity is optimal.

[0123] Compared to the NL group, the distinct histological features of the three other groups of patients are associated with specific gene signatures with coherent biological profiles and very little overlap. The ALLO group exhibits a clear signature of immune-related genes evidencing active immunologic processes and thus validating our transcriptional approach. The over-expression of CD24, a gene expressed in mature B cells, could be explained by the B and mast cell infiltration reported following inflammation whereas the over-expression of MMP8/9 genes fits with tissue remodeling occurring in injured tissue.sup.5, 7, 21. In contrast with the two other groups of patients, the i-IFTA group is associated with few differential genes compared with the NL group but these include genes related to the immune system: TMEM1764B (also called TORID) and its partner TMEM1764A were up-regulated in i-IFTA (FC=1.8 and 2.0, respectively). Interestingly, these two genes have been shown to play a role in dendritic cell maturation and are required for the presentation of donor antigens to CD8.sup.+T cells.sup.22, 23. TMEM1764B and its partner TMEM1764A are also up-regulated in blood from kidney transplanted patients with acute rejection.sup.24. The presence of these genes in this particular situation fits with the increased macrophages/dendritic cell (CD68.sup.+ cells) infiltrate observed in biopsies with fibrosis and signs of inflammation.sup.6. In contrast, several other molecules, mainly related to lymphocytes, were found to have a lower expression in i-IFTA, including CD8B, XCL1, IL31RA and MIR142, markers of migration and homing. This probably correlates to migration of blood lymphocytes towards the graft, as previously described in one-year biopsies with fibrosis and inflammation.sup.6, which suggests that lymphocyte migration appears as soon as 3 months post-transplantation.

[0124] The IFTA group was associated with 93 differential genes compared with NL patients including some integrins and lectins and the enrichment of platelet and coagulation-related GO. The modulation of these genes coding for integrins may result from a blood cell adaptation induced by extracellular matrix modification in grafts with IFTA lesions as previously extensively described. This fits with the fact that administration of platelet activation inhibitor delays fibrosis during chronic renal allograft dysfunction.sup.25. Interestingly, and contrasting with our results, IFTA has been reported to be associated with an increase in immune-related genes in biopsies.sup.7,16, 21. However, in these studies, no distinction was made between patients with IFTA and those with i-IFTA, explaining why we only found differential immune-related genes in the blood from i-IFTA patients. For example, 16 out of 17 patients had an i-score equal to 2 or 3 in the IFTA group in Maluf et al., in our study, we would have classified these patients as i-IFTA.sup.21.

[0125] Altogether, our results clearly suggest a lower expression of immune-related genes in periphery as a reflection of the graft infiltration in i-IFTA but not in IFTA biopsies. Interestingly, these data are in accordance with the blood phenotype of the patients. We found a lower frequency of CD27.sup.−IgD.sup.−IgM.sup.− double negative memory B cells and decreased expression of NKp80 on secretory and transitional NK cells in i-IFTA compared to the NL group. These data fit with a traffic of immune cells from the blood to the allograft, on the site of inflammation and particularly with the higher B-cell related infiltrate observed in i-IFTA.sup.5.

[0126] Very few differential genes highlighted in recipient blood in this study have previously been reported in allograft biopsies. This is in accord with Flechner et al. who reported on very few common genes between circulating lymphocyte pool and biopsy in patients with well-functioning transplants or with acute rejection.sup.12. This may be due to compartment-specific cell localization and gene expression as well as movement of activated cells from the peripheral blood through the kidney, as described above.

[0127] In conclusion, while gene expression profiling in biopsy have been extensively reported to be associated with graft alteration, little attention has been paid to the patterns in periphery. Our study shows that gene expression in peripheral blood cells could predict which patients will have abnormal histology at 1-year post transplantation, allowing early adaptation of clinical management procedures prior to graft injury. These data suggest that blood may be a satisfactory compartment to indirectly predict graft outcome, reducing the risk of rejection and the need for hospitalization. It would thus be of interest to integrate these biomarkers in the clinical decision-making arsenal of physicians when considering the most appropriate clinical management.

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