Identification of common blood gene signatures for the diagnosis of renal and cardiac acute allograft rejection.

To test, whether 10 genes, diagnostic of renal allograft rejection in blood, are able to diagnose and predict cardiac allograft rejection, we analyzed 250 blood samples from heart transplant recipients with and without acute rejection (AR) and with cytomegalovirus (CMV) infection by QPCR. A QPCR-based logistic regression model was built on 5 of these 10 genes (AR threshold composite score >37%  = AR) and tested for AR prediction in an independent set of 109 samples, where it correctly diagnosed AR with 89% accuracy, with no misclassifications for AR ISHLT grade 1b. CMV infection did not confound the AR score. The genes correctly diagnosed AR in a blood sample within 6 months prior to biopsy diagnosis with 80% sensitivity and untreated grade 1b AR episodes had persistently elevated scores until 6 months after biopsy diagnosis. The gene score was also correlated with presence or absence of cardiac allograft vasculopathy (CAV) irrespective of rejection grade. In conclusion, there is a common transcriptional axis of immunological trafficking in peripheral blood in both renal and cardiac organ transplant rejection, across a diverse recipient age range. A common gene signature, initially identified in the setting of renal transplant rejection, can be utilized serially after cardiac transplantation, to diagnose and predict biopsy confirmed acute heart transplant rejection

A urinary Common Rejection Module (uCRM) score for non-invasive kidney transplant monitoring.

A Common Rejection Module (CRM) consisting of 11 genes expressed in allograft biopsies was previously reported to serve as a biomarker for acute rejection (AR), correlate with the extent of graft injury, and predict future allograft damage. We investigated the use of this gene panel on the urine cell pellet of kidney transplant patients. Urinary cell sediments collected from patients with biopsy-confirmed acute rejection, borderline AR (bAR), BK virus nephropathy (BKVN), and stable kidney grafts with normal protocol biopsies (STA) were analyzed for expression of these 11 genes using quantitative polymerase chain reaction (qPCR). We assessed these 11 CRM genes for their abundance, autocorrelation, and individual expression levels. Expression of 10/11 genes were elevated in AR when compared to STA. Psmb9 and Cxcl10could classify AR versus STA as accurately as the 11-gene model (sensitivity = 93.6%, specificity = 97.6%). A uCRM score, based on the geometric mean of the expression levels, could distinguish AR from STA with high accuracy (AUC = 0.9886) and correlated specifically with histologic measures of tubulitis and interstitial inflammation rather than tubular atrophy, glomerulosclerosis, intimal proliferation, tubular vacuolization or acute glomerulitis. This urine gene expression-based score may enable the non-invasive and quantitative monitoring of AR

A combined biomarker and clinical panel for chronic graft versus host disease diagnosis.

Whilst many chronic graft versus host disease (cGVHD) biomarkers have been previously reported, few have been verified in an independent cGVHD cohort. We aimed to verify the diagnostic accuracy of previously reported markers of cGVHD in a multi-centre Chronic GVHD Consortium. A total of 42 RNA and 18 protein candidate biomarkers were assessed amongst 59 cGVHD cases and 33 matched non-GVHD controls. Total RNA was isolated from PBMC, and RNA markers were quantified using PCR. Serum protein markers were quantified using ELISA. A combined 3 RNA biomarker (IRS2, PLEKHF1 and IL1R2) and 2 clinical variables (recipient CMV serostatus and conditioning regimen intensity) panel accurately (AUC 0.81) segregated cGVHD cases from controls. Other studied RNA and protein markers were not confirmed as accurate cGVHD diagnostic biomarkers. The studied markers failed to segregate higher risk cGVHD (per overall NIH 0-3 score, and overlap versus classic cGVHD status). These data support the need for multiple independent verification studies for the ultimate clinical application of cGVHD diagnostic biomarkers

Characterization of intra-graft B cells during renal allograft rejection

Intra-graft CD20+ B-cell clusters are found during acute rejection of renal allografts and correlate with graft recovery following rejection injury. Here using archived kidney tissue we conducted immunohistochemical studies to measure specific subsets of pathogenic B cells during graft rejection. Cluster-forming CD20+ B cells in the rejected graft are likely derived from the recipient and are composed of mature B cells. These cells are activated (CD79a+), and present MHC Class II antigen (HLADR+) to CD4+ T cells. Some of these clusters contained memory B cells (CD27+) and they did not correlate with intra-graft C4d deposition or with detection of donor-specific antibody. Further, several non-cluster forming CD20− B-lineage CD38+ plasmablasts and plasma cells were found to infiltrate the rejected grafts and these cells strongly correlated with circulating donor-specific antibody, and to a lesser extent with intra-graft C4d. Both CD20+ B cells and CD38+ cells correlated with poor response of the rejection to steroids. Reduced graft survival was associated with the presence of CD20 cells in the graft. In conclusion, a specific subset of early lineage B cells appears to be an antigen-presenting cell and which when present in the rejected graft may support a steroid-resistant T-cell-mediated cellular rejection. Late lineage interstitial plasmablasts and plasma cells may also support humoral rejection. These studies suggest that detailed analysis of interstitial cellular infiltrates may allow better use of B-cell lineage specific treatments to improve graft outcomes

A computational gene expression score for predicing 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

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 urinary common rejection module (uCRM) score for non-invasive kidney transplant monitoring

A Common Rejection Module (CRM) consisting of 11 genes expressed in allograft biopsies was previously reported to serve as a biomarker for acute rejection (AR), correlate with the extent of graft injury, and predict future allograft damage. We investigated the use of this gene panel on the urine cell pellet of kidney transplant patients. Urinary cell sediments collected from patients with biopsy-confirmed acute rejection, borderline AR (bAR), BK virus nephropathy (BKVN), and stable kidney grafts with normal protocol biopsies (STA) were analyzed for expression of these 11 genes using quantitative polymerase chain reaction (qPCR). We assessed these 11 CRM genes for their abundance, autocorrelation, and individual expression levels. Expression of 10/11 genes were elevated in AR when compared to STA. Psmb9 and Cxcl10could classify AR versus STA as accurately as the 11-gene model (sensitivity = 93.6%, specificity = 97.6%). A uCRM score, based on the geometric mean of the expression levels, could distinguish AR from STA with high accuracy (AUC = 0.9886) and correlated specifically with histologic measures of tubulitis and interstitial inflammation rather than tubular atrophy, glomerulosclerosis, intimal proliferation, tubular vacuolization or acute glomerulitis. This urine gene expression-based score may enable the non-invasive and quantitative monitoring of AR

The kSORT assay to detect renal transplant patients at high risk for acute rejection: results of the multicenter AART study

Abstract Background: Development of noninvasive molecular assays to improve disease diagnosis and patient monitoring is a critical need. In renal transplantation, acute rejection (AR) increases the risk for chronic graft injury and failure. Noninvasive diagnostic assays to improve current late and nonspecific diagnosis of rejection are needed. We sought to develop a test using a simple blood gene expression assay to detect patients at high risk for AR. Methods and Findings: We developed a novel correlation-based algorithm by step-wise analysis of gene expression data in 558 blood samples from 436 renal transplant patients collected across eight transplant centers in the US, Mexico, and Spain between 5 February 2005 and 15 December 2012 in the Assessment of Acute Rejection in Renal Transplantation (AART)study. Gene expression was assessed by quantitative real-time PCR (QPCR) in one center. A 17-gene set the Kidney Solid Organ Response Test (kSORT) was selected in 143 samples for AR classification using discriminant analysis (area under the receiver operating characteristic curve [AUC] = 0.94; 95% CI 0.91-0.98), validated in 124 independent samples (AUC = 0.95; 95% CI 0.88-1.0) and evaluated for AR prediction in 191 serial samples, where it predicted AR up to 3 mo prior to detection by the current gold standard (biopsy). A novel reference-based algorithm (using 13 12-gene models) was developed in 100 independent samples to provide a numerical AR risk score, to classify patients as high risk versus low risk for AR. kSORT was able to detect AR in blood independent of age, time post-transplantation, and sample source without additional data normalization; AUC = 0.93 (95% CI 0.86-0.99). Further validation of kSORT is planned in prospective clinical observational and interventional trials. Conclusions: The kSORT blood QPCR assay is a noninvasive tool to detect high risk of AR of renal transplants