Key result
Biopsy mRNA-seq profiling predicts ISHLT 2R heart transplant rejection with ~0.77 AUC.
Why the study?
Endomyocardial biopsy histological interpretation has well-recognized variability, prompting the need for an EMB evaluation methodology based solely on gene expression.
Does an unsupervised mRNA-seq classification algorithm objectively categorize heart transplant endomyocardial biopsies and correlate with biological rejection pathways, circulating biomarkers, and histological grades in post-heart transplant recipients?
Cross-Sectional (n=47)
Blinded histological review by two external pathologists
Yes
Does an unsupervised mRNA-seq classification algorithm objectively categorize heart transplant endomyocardial biopsies and correlate with biological rejection pathways, circulating biomarkers, and histological grades in post-heart transplant recipients?
Answers are grounded in this paper and cite their sources.
An unsupervised mRNA-seq algorithm adjusting for clinical confounders classifies cardiac allograft biopsies into three biologically distinct groups that correlate strongly with immune activation, mitochondrial function, and circulating BNP/cytokines, offering a potential objective alternative to variable histological grading.
BACKGROUND: Endomyocardial biopsy (EMB) is currently considered the gold standard for diagnosing cardiac allograft rejection. However, significant limitations related to histological interpretation variability are well-recognized. We sought to develop a methodology to evaluate EMB solely based on gene expression, without relying on histology interpretation. METHODS: Sixty-four EMBs were obtained from 47 post-heart transplant recipients, who were evaluated for allograft rejection. EMBs were subjected to mRNA sequencing, in which an unsupervised classification algorithm was used to identify the molecular signatures that best classified the EMBs. Cytokine and natriuretic peptide peripheral blood profiling was also performed. Subsequently, we performed gene network analysis to identify the gene modules and gene ontology to understand their biological relevance. We correlated our findings with the unsupervised and histological classifications. RESULTS: Our algorithm classifies EMBs into three categories based solely on clusters of gene expression: unsupervised classes 1, 2, and 3. Unsupervised and histological classifications were closely related, with stronger gene module-phenotype correlations for the unsupervised classes. Gene ontology enrichment analysis revealed processes impacting on the regulation of cardiac and mitochondrial function, immune response, and tissue injury response. Significant levels of cytokines and natriuretic peptides were detected following the unsupervised classification. CONCLUSION: We have developed an unsupervised algorithm that classifies EMBs into three distinct categories, without relying on histology interpretation. These categories were highly correlated with mitochondrial, immune, and tissue injury response. Significant cytokine and natriuretic peptide levels were detected within the unsupervised classification. If further validated, the unsupervised classification could offer a more objective EMB evaluation.
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Romero et al. (2023) conducted a cross-sectional in Cardiac allograft rejection in post-heart transplant recipients (n=47). Unsupervised mRNA-seq classification using optimal transport methodology vs. Standard ISHLT histological grading was evaluated on Classification of endomyocardial biopsies into unsupervised molecular classes and prediction of ISHLT histological grades (AUC 0.77 for ISHLT grade 2R (0.74 for 1/2R; 0.67 for 0R), p=0.02 for BNP difference across unsupervised classes). Unsupervised mRNA-seq classification categorized 64 heart transplant biopsies into 3 distinct molecular classes, predicting ISHLT 2R rejection with an AUC of 0.77 and showing significant BNP differences.
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