Molecular and Functional Noninvasive Immune Monitoring in the ESCAPE Study for Prediction of Subclinical Renal Allograft Rejection.

BACKGROUND: Subclinical acute rejection (sc-AR) is a main cause for functional decline and kidney graft loss and may only be assessed through surveillance biopsies. METHODS: The predictive capacity of 2 novel noninvasive blood biomarkers, the transcriptional kidney Solid Organ Response Test (kSORT), and the IFN-γ enzyme-linked immunosorbent spot assay (ELISPOT) assay were assessed in the Evaluation of Sub-Clinical Acute rejection PrEdiction (ESCAPE) Study in 75 consecutive kidney transplants who received 6-month protocol biopsies. Both assays were run individually and in combination to optimize the use of these techniques to predict sc-AR risk. RESULTS: Subclinical acute rejection was observed in 22 (29.3%) patients (17 T cell-mediated subclinical rejection [sc-TCMR], 5 antibody-mediated subclinical rejection [sc-ABMR]), whereas 53 (70.7%) showed a noninjured, preserved (stable [STA]) parenchyma. High-risk (HR), low-risk, and indeterminate-risk kSORT scores were observed in 15 (20%), 50 (66.7%), and 10 (13.3%) patients, respectively. The ELISPOT assay was positive in 31 (41%) and negative in 44 (58.7%) patients. The kSORT assay showed high accuracy predicting sc-AR (specificity, 98%; positive predictive value 93%) (all sc-ABMR and 58% sc-TCMR showed HR-kSORT), whereas the ELISPOT showed high precision ruling out sc-TCMR (specificity = 70%, negative predictive value = 92.5%), but could not predict sc-ABMR, unlike kSORT. The predictive probabilities for sc-AR, sc-TCMR, and sc-ABMR were significantly higher when combining both biomarkers (area under the curve > 0.85, P < 0.001) and independently predicted the risk of 6-month sc-AR in a multivariate regression analysis. CONCLUSIONS: Combining a molecular and immune cell functional assay may help to identify HR patients for sc-AR, distinguishing between different driving alloimmune effector mechanisms.

Intragraft Antiviral-Specific Gene Expression as a Distinctive Transcriptional Signature for Studies in Polyomavirus-Associated Nephropathy.

BACKGROUND: Polyomavirus nephropathy (PVAN) is a common cause of kidney allograft dysfunction and loss. To identify PVAN-specific gene expression and underlying molecular mechanisms, we analyzed kidney biopsies with and without PVAN. METHODS: The study included 168 posttransplant renal allograft biopsies (T cell-mediated rejection [TCMR] = 26, PVAN = 10, normal functioning graft = 73, and interstitial fibrosis/tubular atrophy = 59) from 168 unique kidney allograft recipients. We performed gene expression assays and bioinformatics analysis to identify a set of PVAN-specific genes. Validity and relevance of a subset of these genes are validated by quantitative polymerase chain reaction and immunohistochemistry. RESULTS: Unsupervised hierarchical clustering analysis of all the biopsies revealed high similarity between PVAN and TCMR gene expression. Increased statistical stringency identified 158 and 252 unique PVAN and TCMR injury-specific gene transcripts respectively. Although TCMR-specific genes were overwhelmingly involved in immune response costimulation and TCR signaling, PVAN-specific genes were mainly related to DNA replication process, RNA polymerase assembly, and pathogen recognition receptors. A principal component analysis (PCA) using these genes further confirmed the most optimal separation between the 3 different clinical phenotypes. Validation of 4 PVAN-specific genes (RPS15, complement factor D, lactotransferrin, and nitric oxide synthase interacting protein) by quantitative polymerase chain reaction and confirmation by immunohistochemistry of 2 PVAN-specific proteins with antiviral function (lactotransferrin and IFN-inducible transmembrane 1) was done. CONCLUSIONS: In conclusion, even though PVAN and TCMR kidney allografts share great similarities on gene perturbation, PVAN-specific genes were identified with well-known antiviral properties that provide tools for discerning PVAN and AR as well as attractive targets for rational drug design.

A Computational Gene Expression Score for Predicting Immune Injury in Renal Allografts.

BACKGROUND: Whole genome microarray meta-analyses of 1030 kidney, heart, lung and liver allograft biopsies identified a common immune response module (CRM) of 11 genes that define acute rejection (AR) across different engrafted tissues. We evaluated if the CRM genes can provide a molecular microscope to quantify graft injury in acute rejection (AR) and predict risk of progressive interstitial fibrosis and tubular atrophy (IFTA) in histologically normal kidney biopsies. METHODS: Computational modeling was done on tissue qPCR based gene expression measurements for the 11 CRM genes in 146 independent renal allografts from 122 unique patients with AR (n = 54) and no-AR (n = 92). 24 demographically matched patients with no-AR had 6 and 24 month paired protocol biopsies; all had histologically normal 6 month biopsies, and 12 had evidence of progressive IFTA (pIFTA) on their 24 month biopsies. Results were correlated with demographic, clinical and pathology variables. RESULTS: The 11 gene qPCR based tissue CRM score (tCRM) was significantly increased in AR (5.68 ± 0.91) when compared to STA (1.29 ± 0.28; p < 0.001) and pIFTA (7.94 ± 2.278 versus 2.28 ± 0.66; p = 0.04), with greatest significance for CXCL9 and CXCL10 in AR (p <0.001) and CD6 (p<0.01), CXCL9 (p<0.05), and LCK (p<0.01) in pIFTA. tCRM was a significant independent correlate of biopsy confirmed AR (p < 0.001; AUC of 0.900; 95% CI = 0.705-903). Gene expression modeling of 6 month biopsies across 7/11 genes (CD6, INPP5D, ISG20, NKG7, PSMB9, RUNX3, and TAP1) significantly (p = 0.037) predicted the development of pIFTA at 24 months. CONCLUSIONS: Genome-wide tissue gene expression data mining has supported the development of a tCRM-qPCR based assay for evaluating graft immune inflammation. The tCRM score quantifies injury in AR and stratifies patients at increased risk of future pIFTA prior to any perturbation of graft function or histology.

A rapid noninvasive assay for the detection of renal transplant injury.

BACKGROUND: The copy number of donor-derived cell-free DNA (dd-cfDNA) in blood correlates with acute rejection (AR) in heart transplantation. We analyzed urinary dd-cfDNA as a surrogate marker of kidney transplant injury. METHODS: Sixty-three biopsy-matched urine samples (41 stable and 22 allograft injury) were analyzed from female recipients of male donors for chromosome Y (donor)-specific dd-cfDNA. All biopsies were semiquantitatively scored by a single pathologist. Standard statistical measures of correlation and significance were used. RESULTS: There was baseline scatter for urinary dd-cfDNA/µg urine creatinine across different patients, even at the time of stable graft (STA) function (undetected to 12.26 copies). The mean urinary dd-cfDNA in AR (20.5 ± 13.9) was significantly greater compared with STA (2.4 ± 3.3; P<0.0001) or those with chronic allograft injury (CAI; 2.4 ± 2.4; P=0.001) but no different from BK virus nephropathy (BKVN; 20.3±15.7; P=0.98). In AR and BKVN, the intrapatient drift was highly significant versus STA or CAI patients (10.3 ± 7.4 in AR; 12.3 ± 8.4 in BKVN vs. -0.5 ± 3.5 in STA and 2.3 ± 2.6 in CAI; P<0.05). Urinary dd-cfDNA correlated with protein/creatinine ratio (r=0.48; P<0.014) and calculated glomerular filtration rate (r=-0.52; P<0.007) but was most sensitive for acute allograft injury (area under the curve=0.80; P<0.0006; 95% confidence interval, 0.67-0.93). CONCLUSION: Urinary dd-cfDNA after renal transplantation has patient specific thresholds, reflecting the apoptotic injury load of the donor organ. Serial monitoring of urinary dd-cfDNA can be a surrogate sensitive biomarker of acute injury in the donor organ but lacks the specificity to distinguish between AR and BKVN injury.

Non-HLA antibodies to immunogenic epitopes predict the evolution of chronic renal allograft injury.

Chronic allograft injury (CAI) results from a humoral response to mismatches in immunogenic epitopes between the donor and recipient. Although alloantibodies against HLA antigens contribute to the pathogenesis of CAI, alloantibodies against non-HLA antigens likely contribute as well. Here, we used high-density protein arrays to identify non-HLA antibodies in CAI and subsequently validated a subset in a cohort of 172 serum samples collected serially post-transplantation. There were 38 de novo non-HLA antibodies that significantly associated with the development of CAI (P<0.01) on protocol post-transplant biopsies, with enrichment of their corresponding antigens in the renal cortex. Baseline levels of preformed antibodies to MIG (also called CXCL9), ITAC (also called CXCL11), IFN-γ, and glial-derived neurotrophic factor positively correlated with histologic injury at 24 months. Measuring levels of these four antibodies could help clinicians predict the development of CAI with >80% sensitivity and 100% specificity. In conclusion, pretransplant serum levels of a defined panel of alloantibodies targeting non-HLA immunogenic antigens associate with histologic CAI in the post-transplant period. Validation in a larger, prospective transplant cohort may lead to a noninvasive method to predict and monitor for CAI.