The molecular phenotypes of injury, steatohepatitis, and fibrosis in liver transplant biopsies in the INTERLIVER study
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE193135 | GEO | 2022/01/09
REPOSITORIES: GEO
Similar Datasets
Project description:Molecular diagnosis of rejection is emerging in kidney, heart, and lung transplant biopsies and could offer insights for liver transplant biopsies. Groups differed in median time post-transplant e.g. R3injury 99 days vs. R4late 3117 days. R2TCMR biopsies expressed typical TCMR-related transcripts e.g. intense IFNG-induced effects. R3injury displayed increased expression of parenchymal injury transcripts (e.g. hypoxia-inducible factor EGLN1). R4late biopsies showed immunoglobulin transcripts and injury-related transcripts. R2TCMR correlated with histologic rejection although with many discrepancies, and R4late with fibrosis. R2TCMR, R3injury, and R4late correlated with liver function abnormalities. Supervised classifiers trained on histologic rejection showed less agreement with histology than unsupervised R2TCMR scores. No confirmed cases of clinical ABMR were present in the population, and strategies that previously revealed antibody-mediated rejection (ABMR) in kidney and heart transplants failed to reveal a liver ABMR phenotype. In conclusion, molecular analysis of liver transplant biopsies detects rejection, has the potential to resolve ambiguities, and could assist with immunosuppressive management.
2020-05-06 | GSE145780 | GEO
Project description:We previously reported a microarray-based diagnostic system for heart transplant endomyocardial biopsies (EMBs), using either 3-archetype (3AA) or 4-archetype (4AA) unsupervised algorithms to estimate rejection. The present study aimed to examine the stability of machine-learning algorithms in new biopsies, compare 3AA vs. 4AA algorithms, assess supervised binary classifiers trained on histologic or molecular diagnoses, create a report combining many scores into an ensemble of estimates, and examine possible automated sign-outs. Algorithms derived in cohort 331 performed similarly in new biopsies despite differences in case mix. In the combined cohort, the 4AA model, including a parenchymal injury score, retained correlations with histologic rejection and DSA similar to the 3AA model. Supervised molecular classifiers predicted molecular rejection (AUCs>0.87) better than histologic rejection (AUCs<0.78), even when trained on histology diagnoses. A report incorporating many AA and binary classifier scores interpreted by one expert showed highly significant agreement with histology (p<0.001), but with many discrepancies as expected from the known noise in histology. An automated random forest score closely predicted expert diagnoses, confirming potential for automated sign-outs.Molecular algorithms are stable in new populations and can be assembled into an ensemble that combines many supervised and unsupervised estimates of the molecular disease states. (ClinicalTrials.gov NCT02670408).
2019-08-16 | GSE124897 | GEO
Project description:The first-generation Molecular Microscope® (MMDx) system for heart transplant endomyocardial biopsies used expression of rejection-associated transcripts (RATs) to diagnose T cell-mediated rejection (TCMR) and antibody-mediated rejection (ABMR) but also detected acute injury. However, the ideal system should detect rejection without being influenced by injury, to permit analysis of the relationship between rejection and parenchymal injury. To achieve this, we developed a new rejection classification in an expanded cohort of 3230 biopsies: 1641 from INTERHEART (ClinicalTrials.gov #NCT02670408), plus 1589 service biopsies added to improve the power of the machine learning algorithms. The new system used six rejection classifiers instead of RATs and generated seven rejection archetypes: No Rejection 48%; Minor 24%; TCMR1 2.3%; TCMR2 2.7%; TCMR/Mixed 2.7%; EABMR 3.9%; and FABMR 16%. Using rejection classifiers eliminated cross-reactions with acute injury, permitting separate assessment of rejection and injury. TCMR was associated with severe recent injury and late atrophy-fibrosis and rarely had normal parenchyma. ABMR was better tolerated, seldom producing severe injury, but in later biopsies was often associated with atrophy-fibrosis, indicating long term risk. Graft survival and LVEF were reduced in hearts with TCMR, but also in hearts with severe-recent injury and atrophy-fibrosis, even without rejection.
2024-04-02 | GSE262742 | GEO
Project description:Previous studies of rejection-associated transcript expression in heart transplant biopsies identified not only rejection but a group of early biopsies with injury but no rejection. The present analysis used an expanded population of biopsies to explore parenchymal injury in all biopsies, with or without rejection, and its relationship to function, and outcome. Archetypal analysis defined five injury clusters: no-injury (N=376); mild (N=526); moderate (N=110); severe (N=87); and late (N=221). The late group, 62% of which had no rejection, had molecular characteristics associated with atrophy-fibrosis, depressed LVEF, and increased graft loss independent of rejection status. In random forest analysis, low LVEF was more strongly associated with injury scores than with rejection scores. Three-year graft failure was best predicted using a combination of injury and rejection scores. In heart transplant biopsies, injury-related molecular scores correlate with dysfunction and risk of failure and identify an important new group of late heart transplants, many with no rejection, that have impaired function and a high risk of graft loss. (ClinicalTrials.gov #NCT02670408).
2021-12-28 | GSE150059 | GEO
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: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.
2013-02-13 | GSE36059 | GEO
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: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)
2017-07-10 | GSE98320 | GEO
Project description:Background. The Trifecta study (ClinicalTrials.gov #NCT04239703) is a prospective investigator-initiated trial to evaluate the relationship between plasma %dd-cfDNA at the time of indication biopsy and the molecular phenotype of the biopsy. Results. Median time of biopsy post-transplant was 455 days (5 days - 32 years), with case-mix similar to previous studies: 180 (60%) no rejection, 89 (30%) antibody-mediated rejection (ABMR), and 31 (10%) T cell-mediated rejection (TCMR) and mixed. In genome-wide mRNA measurements, all 20 top probesets correlating with %dd-cfDNA were previously annotated for association with ABMR and all rejection, either NK cell-expressed (e.g. GNLY, CCL4, TRDC, and S1PR5) or IFNG-inducible (e.g. PLA1A, IDO1, CXCL11, and WARS). Among gene set and classifier scores, %dd-cfDNA correlated very strongly with ABMR and all rejection, reasonably strongly with active TCMR, and weakly with inactive TCMR, kidney injury and atrophy-fibrosis. %dd-cfDNA was highest in active ABMR, mixed, and active TCMR but lower in late-stage ABMR and less active TCMR. By multivariate random forests and logistic regression, molecular rejection variables predicted %dd-cfDNA better than histologic variables. Conclusions. %dd-cfDNA at time of indication biopsy strongly correlates with active molecular rejection and has potential to reduce unnecessary biopsies.
2021-12-23 | GSE192444 | GEO
Project description:Improved understanding of lung transplant disease states is essential because failure rates are high, often due to chronic lung allograft dysfunction. However, histologic assessment of lung transplant transbronchial biopsies (TBBs) is difficult and often uninterpretable even with 10 pieces. All 242 single-piece TBBs produced reliable transcript measurements. Paired TBB pieces available from 12 patients showed significant similarity but also showed some sampling variance. Alveolar content, as estimated by surfactant transcript expression, was a source of sampling variance. To offset sampling variation, for analysis we selected 152 single-piece TBBs with high surfactant transcripts. Unsupervised archetypal analysis identified four idealized phenotypes (archetypes) and scored biopsies for their similarity to each: normal, T cell-mediated rejection (TCMR; T cell transcripts), antibody-mediated rejection (ABMR)-like (endothelial transcripts), and injury (macrophage transcripts). Molecular TCMR correlated with histologic TCMR. The relationship of molecular scores to histologic ABMR could not be assessed because of the paucity of ABMR in this population. Molecular assessment of single-piece TBBs can be used to classify lung transplant biopsies and correlated with rejection histology. Two or three pieces for each TBB will probably be needed to offset sampling variance.
2019-08-16 | GSE125478 | GEO