Regulators of homologous recombination deficiency identified by machine learning using somatic multi-omics data
Renan Valieris, Lucas Rosa, Luan Martins, Alexandre Defelicibus, Dirce Maria Carraro, Diana Noronha Nunes, Emmanuel Dias-Neto, Rafael Rosales, Israel Tojal da Silva
TL;DR
This study uses machine learning and multi-omics data to discover new genetic factors linked to homologous recombination deficiency in various cancers.
Contribution
The study introduces a machine learning framework that identifies novel regulators of homologous recombination deficiency beyond BRCA1/2.
Findings
The model achieved high predictive performance using somatic multi-omics data from over 8,000 patients.
SHAP-based analysis revealed shared and cancer-specific molecular determinants of HRD.
Findings expand the known HRD-associated alterations and suggest integrative AI can improve patient stratification for HR-targeted therapies.
Abstract
Using somatic multi-omics data and explainable artificial intelligence, this study identifies novel alterations underlying homologous recombination repair deficiency across cancers. Homologous recombination deficiency (HRD) is a critical biomarker for guiding targeted therapies, yet the full range of somatic alterations driving HRD across cancers remains incompletely characterized. Here, we present a tumor-agnostic machine learning framework that integrates somatic multi-omics data, including copy-number variations, single-nucleotide variants, DNA methylation, and gene expression from over 8,000 patients in The Cancer Genome Atlas. Using a genome-wide mutational signature–based HRD score as ground truth, our model achieved high predictive performance and leveraged SHAP-based explainability to uncover HRD regulators beyond BRCA1/2. Cross-tumor analysis revealed both shared and cancer…
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Taxonomy
TopicsPARP inhibition in cancer therapy · Cancer Genomics and Diagnostics · Genomics and Rare Diseases