Integrating machine learning and pharmacogenomics for biomarker discovery, identification and prioritization of potential drug candidates in ovarian cancer

Ovarian cancer remains a major global health concern and leading cause of mortality among women due to late diagnosis, therapeutic resistance, and limited predictive biomarkers for treatment response. There is an urgent need for integrative approaches to improve early detection and treatment outcomes. In this study, we integrated machine learning and pharmacogenomics to identify drug-sensitive biomarkers and prioritize therapeutic candidates in ovarian cancer. Pharmacogenomic data were obtained from the Cancer Cell Line Encyclopaedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC-v2), including gene expression and drug response profiles across ovarian cancer cell lines. Predictive models were developed for six FDA-approved drugs using Elastic Net, Ridge, and Lasso regression, demonstrating robust predictive performance achieving Pearson correlation (r) to 0.65 and Spearman correlation (ρ) to 0.63 on validation sets. Biomarker analysis identified key genes associated with drug response, including CCR10 and PLEKHH2, WNT9B, ITPRID1, CHRNG, and DIRAS3. Ligand-based similarity screening against the COCONUT database followed by molecular docking and MD simulation identified three promising compounds (662142, 733302, and 883576) with improved binding affinity and conserved interactions with Topoisomerase-1. This integrative framework highlights the potential of combining machine learning, pharmacogenomics, and molecular modelling for biomarker discovery and drug prioritization in ovarian cancer.