In Situ Immune Profiling of Heart Transplant Biopsies Improves Diagnostic Accuracy and Rejection Risk Stratification.
Ontology highlight
ABSTRACT:
SUBMITTER: Peyster EG
PROVIDER: S-EPMC7188920 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundThe diagnosis of antibody-mediated rejection (AMR) is reached using the Banff Classification for Allograft Pathology, which now includes gene expression analysis. In this study, we investigate the application of 'increased expression of thoroughly validated gene transcripts/classifiers strongly associated with AMR' as diagnostic criteria.MethodWe used quantitative real-time polymerase chain reaction for 10 genes associated with AMR in a retrospective cohort of 297 transplant biopsies, including biopsies that met the full diagnostic criteria for AMR, even without molecular data (AMR, n = 27), biopsies that showed features of AMR, but that would only meet criteria for AMR with increased transcripts [suspicious for AMR (AMRsusp), n = 49] and biopsies that would never meet criteria for AMR (No-AMR, n = 221).ResultsA 10-gene AMR score trained by a receiver-operating characteristic to identify AMR found 16 cases with a high score among the AMRsusp cases (AMRsusp-high) that had significantly worse graft survival than those with a low score (AMRsusp-low; n = 33). In both univariate and multivariate Cox regression analysis, the AMR 10-gene score was significantly associated with an increased hazard ratio (HR) for graft loss (GL) in the AMRsusp group (HR = 1.109, P = 0.004 and HR = 1.138, P = 0.012, respectively), but not in the whole cohort. Net reclassification index and integrated discrimination improvement analyses demonstrated improved risk classification and superior discrimination, respectively, for GL when considering the gene score in addition to histological and serological data, but only in the AMRsusp group, not the whole cohort.ConclusionsThis study provides evidence that a gene score strongly associated with AMR helps identify cases at higher risk of GL in biopsies that are suspicious for AMR but do not meet full criteria.
| S-EPMC9317169 | biostudies-literature
Project description:T cell-mediated rejection (TCMR) and antibody-mediated rejection (ABMR) are severe post-transplantation complications for heart transplantation (HTx), whose molecular and immunological pathogenesis remains unclear. In the present study, the mRNA microarray data set GSE124897 containing 645 stable, 52 TCMR and 144 ABMR endomyocardial biopsies was obtained to screen for differentially expressed genes (DEGs) between rejected and stable HTx samples and to investigate immune cell infiltration. Functional enrichment analyses indicated roles of the DEGs primarily in immune-related mechanisms. Protein-protein interaction networks were then constructed, and ICAM1, CD44, HLA-A and HLA-B were identified as hub genes using the maximal clique centrality method. Immune cell infiltration analysis revealed differences in adaptive and innate immune cell populations between TCMR, ABMR and stable HTx samples. Additionally, hub gene expression levels significantly correlated with the degree and composition of immune cell infiltration in HTx rejection samples. Furthermore, drug-gene interactions were constructed, and 12 FDA-approved drugs were predicted to target hub genes. Finally, an external GSE2596 data set was used to validate the expression of the hub genes, and ROC curves indicated all four hub genes had promising diagnostic value for HTx rejection. This study provides a comprehensive perspective of molecular and immunological regulatory mechanisms underlying HTx rejection.
| S-EPMC7812257 | biostudies-literature
Project description:PBMC samples from heart transplant patients were profiled. Sample classification was based on endomyocardial biopsy at the time of sample collection. Keywords = Transplant Keywords = Transplantation Keywords = Heart Transplant Keywords = Graft Rejection Keywords = Rejection Keywords = PBMC Keywords = Immune Response Keywords = Peripheral Blood Keywords: other
2005-09-01 | GSE2445 | GEO
Project description:Different types of kidney transplantations are performed worldwide, including biologically diverse donor/recipient combinations, which entail distinct patient/graft outcomes. Thus, proper immunological and non-immunological risk stratification should be considered, especially for patients included in interventional randomized clinical trials. This paper was prepared by a working group within the European Society for Organ Transplantation, which submitted a Broad Scientific Advice request to the European Medicines Agency (EMA) relating to clinical trial endpoints in kidney transplantation. After collaborative interactions, the EMA sent its final response in December 2020, highlighting the following: 1) transplantations performed between human leukocyte antigen (HLA)-identical donors and recipients carry significantly lower immunological risk than those from HLA-mismatched donors; 2) for the same allogeneic molecular HLA mismatch load, kidney grafts from living donors carry significantly lower immunological risk because they are better preserved and therefore less immunogenic than grafts from deceased donors; 3) single-antigen bead testing is the gold standard to establish the repertoire of serological sensitization and is used to define the presence of a recipient's circulating donor-specific antibodies (HLA-DSA); 4) molecular HLA mismatch analysis should help to further improve organ allocation compatibility and stratify immunological risk for primary alloimmune activation, but without consensus regarding which algorithm and cut-off to use it is difficult to integrate information into clinical practice/study design; 5) further clinical validation of other immune assays, such as those measuring anti-donor cellular memory (T/B cell ELISpot assays) and non-HLA-DSA, is needed; 6) routine clinical tests that reliably measure innate immune alloreactivity are lacking.
| S-EPMC9163827 | biostudies-literature
Project description:MOLECULAR PROFILING IMPROVES DIAGNOSES OF REJECTION AND INFECTION IN TRANSPLANTED ORGANS. The monitoring of transplanted hearts is currently based on histological evaluation of endomyocardial biopsies, a method that is fairly insensitive and that does not always accurately discriminate between rejection and infection in the heart. Accurate diagnosis of rejection and infection is absolutely crucial, however, as the respective treatments are completely different. Using microarrays we analyzed gene expression in 76 cardiac biopsies from 40 heart recipients undergoing rejection, no rejection, or T. cruzi infection. We found a set of 98 genes whose expression patterns were typical of acute rejection, and 87 genes that discriminated between rejection and T. cruzi infection. These sets revealed acute rejection episodes up to two weeks earlier, and trypanosome infection up to two months earlier than did histological evaluation. When applied to raw data from other institutions, the two sets of predictive genes were also able to accurately pinpoint acute rejection of lung and kidney transplants, as well as bacterial infections in kidneys. In addition to their usefulness as diagnostic tools, the data suggest that there are similarities in the biology of the processes involved in rejection of different grafts and also in the tissue responses to pathogens as diverse as bacteria and protozoa. pre-rejection: GSM47949, GSM48048, GSM49625 non-rejection: GSM47957, GSM48045, GSM48049, GSM48057, GSM48072, GSM48089, GSM48092, GSM49624, GSM49654, GSM49656, GSM49657, GSM49658, GSM49661, GSM49666, GSM49748, GSM49750, GSM49751, GSM49752, GSM49754, GSM49756, GSM49757, GSM49758, GSM49762, GSM49765, GSM49766, GSM49767, GSM49769, GSM49771, GSM49773, GSM49774, GSM49781, GSM49782, GSM49783, GSM49784 acute rejection: GSM48040, GSM48043, GSM48046, GSM48047, GSM48050, GSM48053, GSM48056, GSM49655, GSM49747, GSM49749, GSM49755, GSM49759, GSM49761, GSM49763, GSM49764, GSM49780 pre-Chagas disease reactivation: GSM48089, GSM49756, GSM49757, GSM49758, GSM49767, GSM49769, GSM49771 Chagas disease reactivation: GSM49753, GSM49760, GSM49768, GSM49772, GSM49775, GSM49776, GSM49777, GSM49778 Toxoplasma myocarditis: GSM48058 first subset ("summer"): GSM47949, GSM47957, GSM48040, GSM48043, GSM48045, GSM48046, GSM48047, GSM48048, GSM48049, GSM48050, GSM48053, GSM48056, GSM48057, GSM48058, GSM48072, GSM48089, GSM48092, GSM49624, GSM49625, GSM49654, GSM49655, GSM49656, GSM49657, GSM49658, GSM49661, GSM49666, GSM49747, GSM49748, GSM49749, GSM49750, GSM49751, GSM49752 second subset ("winter"): GSM49753, GSM49754, GSM49755, GSM49756, GSM49757, GSM49758, GSM49759, GSM49760, GSM49761, GSM49762, GSM49763, GSM49764, GSM49765, GSM49766, GSM49767, GSM49768, GSM49769, GSM49770, GSM49771, GSM49772, GSM49773, GSM49774, GSM49775, GSM49776, GSM49777, GSM49778, GSM49780, GSM49781, GSM49782, GSM49783, GSM49784 Keywords = cardiac transplant Keywords = acute rejection Keywords = infection Keywords: repeat sample
2005-10-24 | GSE2596 | 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:PBMC samples from heart transplant patients were profiled. Sample classification was based on endomyocardial biopsy at the time of sample collection. Keywords = Transplant Keywords = Transplantation Keywords = Heart Transplant Keywords = Graft Rejection Keywords = Rejection Keywords = PBMC Keywords = Immune Response Keywords = Peripheral Blood
2010-06-10 | E-GEOD-2445 | biostudies-arrayexpress
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:To assess their utility in routine neuropathology, we prospectively integrated DNA methylation-based CNS tumor classification and targeted gene panel sequencing of tumor and constitutional DNA with blinded neuropathological reference diagnostics for a population-based cohort of > 1,200 newly-diagnosed pediatric patients.
2023-01-30 | GSE215240 | GEO
Project description:The molecular refinement of the diagnosis of heart allograft rejection based on whole-transcriptome analyses faces several hurdles that greatly limit its widespread clinical application. The targeted Banff Human Organ Transplant gene panel (B-HOT, including 770 genes of interest) has been developed to facilitate reproducible and cost-effective gene expression analysis of solid organ allografts. We aimed to determine in silico the ability of this targeted panel to capture the antibody-mediated rejection (AMR) molecular profile using whole-transcriptome data from 137 heart allograft biopsies (71 biopsies reflecting the entire landscape of histologic AMR, 66 non-AMR control biopsies including cellular rejection and non-rejection cases). Differential gene expression, pathway and network analyses demonstrated that the B-HOT panel captured biologically and clinically relevant genes (IFNG-inducible, NK-cells, injury, monocytes-macrophage, B-cell-related genes), pathways (interleukin and interferon signaling, neutrophil degranulation, immunoregulatory interactions, endothelial activation) and networks reflecting the pathophysiological mechanisms underlying the AMR process previously identified in whole-transcriptome analysis. Our findings support the potential clinical use of the B-HOT-gene panel as a reliable proxy to whole-transcriptome analysis for the gene expression profiling of cardiac allograft rejection.
| S-EPMC10515212 | biostudies-literature