biostudies-literatureDong XNational Natural Science Foundation of China (National Science Foundation of China)36https://www.ebi.ac.uk/biostudies/studies/S-EPMC11794852biostudies-literatureUnknown9(1)Advances in cancer genomics have significantly expanded our understanding of cancer biology. However, the high cost of drug development limits our ability to translate this knowledge into precise treatments. Approved non-oncology drugs, comprising a large repository of chemical entities, offer a promising avenue for repurposing in cancer therapy. Herein we present CHANCE, a supervised machine learning model designed to predict the anticancer activities of non-oncology drugs for specific patients by simultaneously considering personalized coding and non-coding mutations. Utilizing protein-protein interaction networks, CHANCE harmonizes multilevel mutation annotations and integrates pharmacological information across different drugs into a single model. We systematically benchmarked the perfNPJ precision oncologyPersonalized prediction of anticancer potential of non-oncology drugs through learning from genome derived molecular pathways.PMC11794852320706758197049431801122Liu HYang HYi XWu LDong XYou TWang HWang JWei YTong THu JWang XLi MJfalsePersonalized prediction of anticancer potential of non-oncology drugs through learning from genome derived molecular pathways.Advances in cancer genomics have significantly expanded our understanding of cancer biology. However, the high cost of drug development limits our ability to translate this knowledge into precise treatments. Approved non-oncology drugs, comprising a large repository of chemical entities, offer a promising avenue for repurposing in cancer therapy. Herein we present CHANCE, a supervised machine learning model designed to predict the anticancer activities of non-oncology drugs for specific patients by simultaneously considering personalized coding and non-coding mutations. Utilizing protein-protein interaction networks, CHANCE harmonizes multilevel mutation annotations and integrates pharmacological information across different drugs into a single model. We systematically benchmarked the perf2025-01-01T00:00:00Z2025 Feb2026-06-02T10:16:28.798Z2025-04-04T07:26:25.613ZS-EPMC117948523990522310.1038/s41698-025-00813-z