Dataset Information

Banff Human Organ Transplant Consensus Gene Panel for the Detection of Antibody Mediated Rejection in Heart Allograft Biopsies.

ABSTRACT: 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.

SUBMITTER: Giarraputo A

PROVIDER: S-EPMC10515212 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Banff Human Organ Transplant Consensus Gene Panel for the Detection of Antibody Mediated Rejection in Heart Allograft Biopsies.

Giarraputo Alessia A Coutance Guillaume G Aubert Olivier O Fedrigo Marny M Mezine Fariza F Zielinski Dina D Mengel Michael M Bruneval Patrick P Duong van Huyen Jean-Paul JP Angelini Annalisa A Loupy Alexandre A

Transplant international : official journal of the European Society for Organ Transplantation 20230904

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 <i>in silico</i> the ability of this targeted panel to capture the antibody-medi ...[more]

PMID: 37745639

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Project description:IntroductionA decentralized and multi-platform-compatible molecular diagnostic tool for kidney transplant biopsies could improve the dissemination and exploitation of this technology, increasing its clinical impact. As a first step towards this molecular diagnostic tool, we developed and validated a classifier using the genes of the Banff-Human Organ Transplant (B-HOT) panel extracted from a historical Molecular Microscope® Diagnostic system microarray dataset. Furthermore, we evaluated the discriminative power of the B-HOT panel in a clinical scenario.Materials and methodsGene expression data from 1,181 kidney transplant biopsies were used as training data for three random forest models to predict kidney transplant biopsy Banff categories, including non-rejection (NR), antibody-mediated rejection (ABMR), and T-cell-mediated rejection (TCMR). Performance was evaluated using nested cross-validation. The three models used different sets of input features: the first model (B-HOT Model) was trained on only the genes included in the B-HOT panel, the second model (Feature Selection Model) was based on sequential forward feature selection from all available genes, and the third model (B-HOT+ Model) was based on the combination of the two models, i.e. B-HOT panel genes plus highly predictive genes from the sequential forward feature selection. After performance assessment on cross-validation, the best-performing model was validated on an external independent dataset based on a different microarray version.ResultsThe best performances were achieved by the B-HOT+ Model, a multilabel random forest model trained on B-HOT panel genes with the addition of the 6 most predictive genes of the Feature Selection Model (ST7, KLRC4-KLRK1, TRBC1, TRBV6-5, TRBV19, and ZFX), with a mean accuracy of 92.1% during cross-validation. On the validation set, the same model achieved Area Under the ROC Curve (AUC) of 0.965 and 0.982 for NR and ABMR respectively.DiscussionThis kidney transplant biopsy classifier is one step closer to the development of a decentralized kidney transplant biopsy classifier that is effective on data derived from different gene expression platforms. The B-HOT panel proved to be a reliable highly-predictive panel for kidney transplant rejection classification. Furthermore, we propose to include the aforementioned 6 genes in the B-HOT panel for further optimization of this commercially available panel.

Project description:BackgroundNoninvasive monitoring of heart allograft health is important to improve clinical outcomes. MicroRNAs (miRs) are promising biomarkers of cardiovascular disease and limited studies suggest they can be used to noninvasively diagnose acute heart transplant rejection.MethodsThe Genomic Research Alliance for Transplantation (GRAfT) is a multicenter prospective cohort study that phenotyped heart transplant patients from 5 mid-Atlantic centers. Patients who had no history of rejection after transplant were compared to patients with acute cellular rejection (ACR) or antibody-mediated rejection (AMR). Small RNA sequencing was performed on plasma samples collected at the time of an endomyocardial biopsy. Differential miR expression was performed with adjustment for clinical covariates. Regression was used to develop miR panels with high diagnostic accuracy for ACR and AMR. These panels were then validated in independent samples from GRAfT and Stanford University. Receiver operating characteristic curves were generated and area under the curve (AUC) statistics calculated. Distinct ACR and AMR clinical scores were developed to translate miR expression data for clinical use.ResultsThe GRAfT cohort had a median age of 52 years, with 35% females and 45% Black patients. Between GRAfT and Stanford, we included 157 heart transplant patients: 108 controls and 49 with rejection (50 ACR and 38 AMR episodes). After differential miR expression and regression analysis, we identified 12 miRs that accurately discriminate ACR and 17 miRs in AMR. Independent validation of the miR panels within GRAfT led to an ACR AUC 0.92 (95% confidence interval [CI]: 0.86-0.98) and AMR AUC 0.82 (95% CI: 0.74-0.90). The externally validated ACR AUC was 0.72 (95% CI: 0.59-0.82). We developed distinct ACR and AMR miR clinical scores (range 0-100), a score ≥ 65, identified ACR with 86% sensitivity, 76% specificity, and 98% negative predictive value, for AMR score performance was 82%, 84% and 97%, respectively.ConclusionsWe identified novel miRs that had excellent performance to noninvasively diagnose acute rejection after heart transplantation. Once rigorously validated, the unique clinical ACR and AMR scores usher in an era whereby genomic biomarkers can be used to screen and diagnose the subtype of rejection. These novel biomarkers may potentially alleviate the need for an endomyocardial biopsy while facilitating the initiation of targeted therapy based on the noninvasive diagnosis of ACR or AMR.

Dataset Information

Banff Human Organ Transplant Consensus Gene Panel for the Detection of Antibody Mediated Rejection in Heart Allograft Biopsies.

Publications

Banff Human Organ Transplant Consensus Gene Panel for the Detection of Antibody Mediated Rejection in Heart Allograft Biopsies.

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