Potential impact of microarray diagnosis of T cell-mediated rejection in kidney transplants: the INTERCOM study
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ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Jessica Chang
PROVIDER: E-GEOD-48581 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
Similar Datasets
Project description:Microarray analysis of human kidneys with acute kidney injury (AKI) has been limited because such kidneys are seldom biopsied. However, all kidney transplants experience AKI, and early kidney transplants without rejection are an excellent model for human AKI: they are screened to exclude chronic kidney disease, frequently biopsied, and have extensive follow-up. We used histopathology and microarrays to compare indication biopsies from 28 transplants with AKI to 11 pristine protocol biopsies of stable transplants. Kidneys with AKI showed increased expression of 394 injury-repair response associated transcripts, including many known epithelial injury molecules (e.g. ITGB6, LCN2), tissue remodeling molecules (e.g. VCAN), and inflammation molecules (S100A8, ITGB3). Many other genes also predict the phenotype, depending on statistical filtering rules, including AKI biomarkers as HAVCR1 and IL18. Most mouse orthologs of the top injury-repair transcripts were increased in published mouse AKI models. Pathway analysis of the injury-repair transcripts revealed similarities to cancer, development, and cell movement. The injury-repair transcript score AKI kidneys correlated with reduced function, future recovery, brain death, and need for dialysis, but not future graft loss. In contrast, histologic features of "acute tubular injury" did not correlate with function or with the molecular changes. Thus the injury-repair associated transcripts represent a massive coordinate injury-repair response of kidney parenchyma to AKI, similar to mouse AKI models, and provide an objective measure for assessing the severity of AKI in kidney biopsies and validation for the use of many AKI biomarkers. AKI biopsies sample names and CEL files are from GSE21374. All consenting renal transplant patients undergoing biopsies for cause as standard of care between 09/2004 and 10/2007 at the university of Alberta or between 11/2006 and 02/2007 at the University of Illinois were included in the analysis. In addition to the cores required for standard histopathology, we collected one core for gene expression studies. the relationship between gene expression in the biopsy and subsequent graft loss was analyzed. This dataset is part of the TransQST collection.
2012-02-21 | E-GEOD-30718 | biostudies-arrayexpress
Project description:Histologic diagnosis of T cell-mediated rejection in kidney transplant biopsies has limited reproducibility because it is based on non-specific lesions using arbitrary rules that are subject to differing interpretations. We used microarray results from 403 indication biopsies previously given histologic diagnoses to develop a molecular classifier that assigned a molecular T cell-mediated rejection score to each biopsy. Independent assessment of the biopsies by multiple pathologists confirmed considerable disagreement on the presence of TCMR features: 79-88% accuracy and 35-69% sensitivity. The agreement of the molecular T cell-mediated rejection score with the histology diagnosis was similar to agreement among individual pathologists: accuracy 89%, sensitivity 51%. However, the score also predicted the consensus among pathologists, being highest when all agreed. Many discrepancies between the scores and the histologic diagnoses were in situations where histology is unreliable e.g. scarred biopsies. The score correlated with histologic lesions and gene sets associated with T cell-mediated rejection. The transcripts most often selected by the classifier were expressed in effector T cells, dendritic cells, or macrophages or inducible by interferon-gamma. Thus the T cell-mediated rejection score offers an objective assessment of kidney transplant biopsies, predicting the consensus opinion among multiple pathologists, and offering insights into underlying disease mechanisms. Antibody-mediated rejection is a major cause of kidney transplant failure, but the current diagnostic system misses most cases due to dependency on subjective non-standardized tests. We hypothesized that molecular features could provide a test to address this problem. We classified 403 biopsies by a reference standard based on microcirculation lesions and donor-specific HLA antibody, and used microarray analysis to develop a classifier that assigned each biopsy a score reflecting the probability of antibody-mediated rejection. The scores correlated with donor-specific antibody and histologic lesions: 42/45 biopsies with antibody-mediated rejection scores >0.5 had both donor-specific antibody and microcirculation lesions. Intermediate scores (0.2-0.5) were more ambiguous, but became more specific combined with donor-specific antibody. Compared to diagnoses based on histology-plus-donor-specific antibody, the scores had sensitivity 0.67; specificity 0.90. Donor-specific antibody improved the specificity to 0.97. The score correlated not only with diagnoses of individual pathologists but with the consensus among multiple pathologists. The classifier used transcripts expressed in endothelial cells (e.g. CDH13, DARC, ROBO4) and NK cells (e.g. CX3CR1, FGFBP2), as well as IFNG-inducible transcripts e.g. CXCL11. Thus the molecular phenotype of antibody-mediated rejection provides not only an objective test that predicts microcirculation lesions and donor-specific HLA antibody, but also offers mechanistic insights. All consenting renal transplant patients undergoing biopsies for cause as standard of care. 403 samples and 8 controls (nephrectomies). This dataset is part of the TransQST collection.
2013-02-13 | E-GEOD-36059 | biostudies-arrayexpress
Project description:Kidney transplants that develop dysfunction or proteinuria after one year post transplant are at considerable risk for progression to renal failure. Identifying the molecules associated with graft failure could potentially lead to interventions that would slow the progression of organ failure. We analyzed the relationship between gene expression in late biopsies for cause in human kidney transplants and subsequent graft loss, and assessed the predictive value of gene expression. We evaluated the performance of these genes in an independent , and in a population of early biopsies that have a very low risk of subsequent graft failure. All consenting renal transplant patients undergoing biopsies for cause as standard of care between 09/2004 and 10/2007 at the university of Alberta or between 11/2006 and 02/2007 at the University of Illinois were included in the analysis. In addition, biopsies obtained from Minnesota between 09/2006 and 09/2007 were used as an independent . In addition to the cores required for standard histopathology, we collected one core for gene expression studies. the relationship between gene expression in the biopsy and subsequent graft loss was analyzed. This dataset is part of the TransQST collection.
2010-05-24 | E-GEOD-21374 | biostudies-arrayexpress
Project description:Molecular phenotyping of biopsies affords opportunities for increased precision and improved disease classification to address the limitations of conventional histologic diagnostic systems. We applied archetypal analysis, an unsupervised method similar to cluster analysis, to microarray data from 1208 prospectively collected kidney transplant biopsies from 13 centers. Seven machine learning-generated cross-validated classifier scores per biopsy were used as input for the archetypal analysis. Six archetypes representing extreme phenotypes were generated: no rejection; T cell-mediated rejection (TCMR); three phenotypes associated with antibody-mediated rejection (ABMR) - early-stage, fully-developed, and late-stage; and mixed rejection (TCMR plus early-stage ABMR). Each biopsy was assigned six scores, one for each archetype, that together represent a probabilistic assessment of that biopsy based on its rejection-related molecular properties. Viewed as clusters, the archetypes were similar to existing histologic Banff categories, but there was 32% disagreement, much of it probably reflecting the “noise” in the current histologic assessment system. Graft survival was worst for fully-developed and late-stage ABMR and was better predicted by molecular archetype scores than histologic diagnoses. The results provide a system for precision molecular assessment of biopsies and a new standard for recalibrating conventional diagnostic systems. (ClinicalTrials.gov NTC1299168) We applied archetypal analysis, an unsupervised method similar to cluster analysis, to microarray data from 1208 prospectively collected kidney transplant biopsies from 13 centers. This dataset is part of the TransQST collection.
2017-07-10 | E-GEOD-98320 | biostudies-arrayexpress
Project description:Histologic assessment of kidney transplant biopsies relies on cortex rather than medulla, but for microarray studies, the proportion cortex in a biopsy is typically unknown and could affect the molecular readings. The present study aimed to develop a molecular estimate of proportion cortex in biopsies and examine its effect on molecular diagnoses. Microarrays from 26 kidney transplant biopsies divided into cortex and medulla components and processed separately showed that many of the most significant differences were in glomerular genes e.g. NPHS2, NPHS1, CLIC5, PTPRO, PLA2R1, PLCE1, PODXL and REN. Using NPHS2 (podocin) to estimate proportion cortex, we examined whether proportion cortex influenced molecular assessment in the Molecular Microscope Diagnostic System. In 1190 unselected kidney transplant indication biopsies (Clinicaltrials.govNCT01299168), only 11% had <50% cortex. Molecular scores for ABMR, TCMR, and injury were independent of proportion cortex. Rejection was diagnosed in many biopsies that were mostly or all medulla. Agreement in molecular diagnoses in paired cortex/medulla samples (23/26) was similar to biological replicates (32/37). We conclude that NPHS2 expression can estimate proportion cortex; that proportion cortex has little influence on molecular diagnosis of rejection, and that, although histology cannot assess medulla, rejection does occur in medulla as well as cortex. We studied 26 pairs of cortex/medulla biopsies from 26 patients (4 unpaired), characterizing the clinical and histological features, and defined the mRNA phenotype with Affymetrix expression microarrays. We also studied 37 pairs of biopsies from biological replicates and 12 pairs from technical replicates. This dataset is part of the TransQST collection.
2017-04-27 | E-GEOD-95611 | biostudies-arrayexpress
Project description:Polyoma virus nephropathy (PVAN) is a common cause of kidney allograft dysfunction and loss. Microscopic descriptions of PVAN are very similar to T-cell mediated rejection (TCMR) and have unclear underlying molecular mechanisms. To identify PVAN-specific gene expression, we analyzed 162 kidney biopsies with and without PVAN for global gene expression. Unsupervised hierarchical clustering analysis of all 162 biopsies revealed high similarity between PVAN and TCMR gene expression. Increasing the stringency for the specificity (p <0.001 and >2-fold expression) between PVAN and TCMR, 158 and 252 unique PVAN and TCMR injury-specific probesets were observed, respectively. While TCMR-specific probeset were overwhelmingly involved in immune response costimulation (CTLA4, CD28, CD86) and TCR (NFATC2, LCP2) signaling, PVAN-specific probesets were mainly related to viral replication process (IFITM1, LTF, NOSIP, RARRES3), RNA polymerase assembly (POLR2l, TAF10, RPS15) and pathogen recognition receptors (C1QA, C3, CFD). A principal component analysis using these genes further confirmed the most optimal separation between the 3 different clinical phenotypes. Validation of 4 PVAN-specific probesets (RPS15, CFD, LTF, and NOSIP) by QPCR and further confirmation by IHC of 2 PVAN-specific proteins with anti-viral function (LTF and IFITM1) was done, showing significantly higher expression within interstitial cellular infiltrates and in tubuli in PVAN specimens as compared to TCMR and NL kidney biopsies. In conclusion, even though PVAN and TCMR kidney allografts share great similarities on gene perturbation, particular PVAN-specific transcripts were identified with well-known anti-viral properties that provide tools for discerning PVAN and AR as well as attractive targets for rational drug design. A total of 168 kidney biopsies from kidney transplant patients were used. The kidney biopsies included renal biopsies from PVAN (n=10), T cell mediated cellular rejection (n=26), patients with IFTA (n=59), patients with stable grafts (STA) (n=73). This dataset is part of the TransQST collection.
2016-09-30 | E-GEOD-72925 | biostudies-arrayexpress
Project description:In deceased donor kidney transplantation, acute kidney injury (AKI) prioir to surgery is a major determinant of delayed graft function (DGF), but AKI is histologically silent and difficult to assess. We hypothesized that a molecular measurement of AKI would add power to conventional risk assessments to predict the early poor allograft function at first week post transplantation. We performed microarrays on implantation biopsies taken during reperfusion in 70 deceased donor kidneys from 53 donors. Early poor function was classified by two definitions on day 7 post-transplantation: serum creatinine greater than 265 umol/L (3 mg/dL) or the requirement for dialysis. Donor age and related risk scores (Irish, Schold, KDRI) associated with worse early function, as expected, but histologic features (glomerulosclerosis; pathology risk scores (Remuzzi, MAPI)) correlated with donor age but not with poor function. However, molecular AKI signal, previously defined in kidneys with early injury, was the best single predictor of poor allograft function. The combination of donor age and the AKI signal improved the prediction of poor function. In addition, asssessments of tissue quality particularly donor age, Banff ct, Irish and KDRI scores, showed negative correlative trend with late graft function, whereas the AKI signal did not. Thus donor age and the molecular AKI signal are the main predictors of early impaired function, but have little impact on survival.
2013-02-13 | E-GEOD-37838 | biostudies-arrayexpress
Project description:The authors report that in INTERCOM, a prospective international study of 300 kidney transplant biopsies, a microarray-based molecular score for T cell-mediated rejection changed the assessment of 26% of all biopsies, illustrating the potential of precision diagnostics to impact practice.
2013-09-15 | GSE48581 | GEO
Project description:To unveil the molecular mechanisms underlying TCMR, we utilized spatial transcriptomics on post-transplant TCMR and non-TCMR patients. Contrary to the prevailing T cell-centric view, our findings reveal uniform inflammatory responses across glomerular and tubulointerstitial regions in TCMR.
2024-02-16 | GSE252137 | GEO
Project description:Background: Biopsy-based diagnosis is essential for maintaining renal allograft longevity by ensuring prompt treatment for graft complications. Although histologic assessment remains the gold standard, it carries significant limitations such as subjective interpretation, suboptimal reproducibility, and imprecise quantitation of disease burden. It is hoped that molecular diagnostics could enhance the efficiency, accuracy, and reproducibility of traditional histologic methods. Methods: Quantitative label-free mass spectrometry analysis was performed on a set of formalin-fixed, paraffin-embedded (FFPE) biopsies from renal transplant patients, comprising five biopsies with the diagnosis of T-cell-mediated rejection (TCMR), five biopsies for polyomavirus BK nephropathy (BKPyVN), and normal kidney control tissue (STA). Using the differential protein expression result as a classifier, three different machine learning algorithms were tested to build a molecular diagnostic model for TCMR. Results: The label-free proteomics method yielded 800-1350 proteins that could be quantified with high confidence per sample by single-shot measurements. Among these candidate proteins, 329 and 467 proteins were defined as differentially expressed proteins (DEPs) for TCMR in comparison with STA and BKPyVN, respectively. Comparing the FFPE quantitative proteomics data set obtained in this study using label-free method with a data set we previously reported using isobaric labeling technology, a classifier pool comprised of features from DEPs commonly quantified in both data sets, was generated for TCMR prediction. Leave-one-out cross-validation result demonstrated that the random forest (RF)-based model achieved the best predictive power. In a follow-up blind test using an independent sample set, the RF-based model yields 80% accuracy for TCMR and 100% for STA. When applying the established RF-based model to two public transcriptome datasets, 78.1%-82.9% sensitivity and 58.7%-64.4% specificity was achieved respectively. Conclusions: This proof-of-principle study demonstrates the clinical feasibility of proteomics profiling for FFPE biopsies using an accurate, efficient, and cost-effective platform integrated of quantitative label-free mass spectrometry analysis with a machine learning-derived diagnostic model. It costs less than 10 dollars per test.
2023-02-01 | PXD038601 | Pride