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Unsupervised mRNA-seq classification of heart transplant endomyocardial biopsies
Erick Romero1, Esteban Tabak2, Gregory Fishbein3
1Division of Cardiovascular Medicine, UC Davis Medical Center, Sacramento, California, USA.
Clinical Transplantation
|May 8, 2023
Summary
A new gene expression algorithm classifies endomyocardial biopsies (EMBs) into three categories, offering an objective alternative to histology for diagnosing heart transplant rejection. This method correlates with immune and tissue injury responses.
Area of Science:
- Cardiology
- Immunology
- Genomics
Background:
- Endomyocardial biopsy (EMB) is the standard for diagnosing cardiac allograft rejection but suffers from interpretation variability.
- Histological assessment of EMBs is subjective and requires expert interpretation.
- Developing objective diagnostic methods for heart transplant rejection is crucial.
Purpose of the Study:
- To develop a gene expression-based methodology for evaluating EMBs, independent of histological interpretation.
- To identify molecular signatures that can classify EMBs objectively.
- To correlate gene expression patterns with clinical outcomes and rejection status.
Main Methods:
- mRNA sequencing was performed on 64 EMBs from 47 heart transplant recipients.
- An unsupervised classification algorithm identified molecular signatures for EMB classification.
- Gene network and ontology analyses were conducted, alongside peripheral blood cytokine and natriuretic peptide profiling.
Main Results:
- The algorithm classified EMBs into three unsupervised categories (classes 1, 2, and 3) based on gene expression.
- These unsupervised classes showed strong correlations with gene modules related to cardiac and mitochondrial function, immune response, and tissue injury.
- Significant levels of cytokines and natriuretic peptides were detected, correlating with the unsupervised classification.
Conclusions:
- An unsupervised gene expression algorithm for EMB classification has been developed, bypassing the need for histology interpretation.
- The identified categories are linked to key biological processes involved in rejection and graft health.
- This gene expression-based approach may offer a more objective and reproducible method for evaluating cardiac allograft rejection.
