ExpHRD: an Individualized, Transcriptome-based Prediction Model for Homologous Recombination Deficiency Assessment in Cancer

Overview

Journal BMC Bioinformatics

Publisher Biomed Central

Specialty Biology

Date 2024 Jul 12

PMID 38997639

Authors

Jae Jun Lee

Hyun Ju Kang

Donghyo Kim

Si On Lim

Stephanie S Kim

Gahyun Kim

Sanguk Kim

Jin-Ku Lee

Jinho Kim

Affiliations

Soon will be listed here.

Abstract

Background: Homologous recombination deficiency (HRD) stands as a clinical indicator for discerning responsive outcomes to platinum-based chemotherapy and poly ADP-ribose polymerase (PARP) inhibitors. One of the conventional approaches to HRD prognostication has generally centered on identifying deleterious mutations within the BRCA1/2 genes, along with quantifying the genomic scars, such as Genomic Instability Score (GIS) estimation with scarHRD. However, the scarHRD method has limitations in scenarios involving tumors bereft of corresponding germline data. Although several RNA-seq-based HRD prediction algorithms have been developed, they mainly support cohort-wise classification, thereby yielding HRD status without furnishing an analogous quantitative metric akin to scarHRD. This study introduces the expHRD method, which operates as a novel transcriptome-based framework tailored to n-of-1-style HRD scoring.

Results: The prediction model has been established using the elastic net regression method in the Cancer Genome Atlas (TCGA) pan-cancer training set. The bootstrap technique derived the HRD geneset for applying the expHRD calculation. The expHRD demonstrated a notable correlation with scarHRD and superior performance in predicting HRD-high samples. We also performed intra- and extra-cohort evaluations for clinical feasibility in the TCGA-OV and the Genomic Data Commons (GDC) ovarian cancer cohort, respectively. The innovative web service designed for ease of use is poised to extend the realms of HRD prediction across diverse malignancies, with ovarian cancer standing as an emblematic example.

Conclusions: Our novel approach leverages the transcriptome data, enabling the prediction of HRD status with remarkable precision. This innovative method addresses the challenges associated with limited available data, opening new avenues for utilizing transcriptomics to inform clinical decisions.

Citing Articles

Deep Learning for Biomarker Discovery in Cancer Genomes.

Unger M, Loeffler C, Zigutyte L, Sainath S, Lenz T, Vibert J bioRxiv. 2025; .

PMID: 39829845 PMC: 11741323. DOI: 10.1101/2025.01.06.631471.

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