Pharmacogenomics-Driven Multimodal Data Integration Improves Predictions of Adverse Drug Reactions in Cancer Patients using Machine Learning

Accurately predicting adverse drug reactions (ADRs) in cancer remains challenging. We applied a pharmacogenomics-driven machine learning framework that integrates genomic, environmental, and comorbidity data to enhance ADR prediction. Using UK Biobank, we analysed 26,235 antineoplastic-treated patients, identifying ADRs via ICD-10 codes. Features included GWAS-derived SNPs from 169 pharmacogenes, curated PharmGKB variants, demographics, lifestyle, laboratory biomarkers, and comorbidities. Five supervised models were trained; subgroup analyses assessed drug-specific and ADR-specific cohort performance. Logistic regression and multilayer perceptron models performed best. In drug-specific cohort, genetic data alone achieved AUC-ROC 0.82 (LR) and 0.80 (MLP), improving to 0.85 and 0.86 when all features were included. For secondary thrombocytopenia, LR and MLP achieved AUC-ROC 0.94 using genetic data only and 0.97 with all features. SHAP and univariate analyses highlighted female gender, elevated cystatin C, and alkaline phosphatase (all p < 0.001); haematologic and digestive cancers showed higher risk compared to other cancer types. This integrative approach supports data-driven clinical decision-making to reduce ADRs.