Pan-cancer multi-omics analysis of pharmacogenomic alterations
Background Pharmacogenomic (PGx) variation is an important determinant of inter-individual variability in drug response. While germline pharmacogenomics has been extensively studied, somatic alterations affecting these pharmacogenes in tumors remain less systematically characterised. Methods We performed a pan-cancer multi-omics analysis integrating somatic mutation, copy number alteration, and transcriptomic data across 765 patients with primary tumors spanning seven The Cancer Genome Atlas (TCGA) cancer types. Exploratory survival analyses were restricted to 733 patients with complete clinical and overall survival information. Using a curated panel of 40 pharmacogenes, we characterized gene-level alterations, summarised recurrent events across tumor types, and aggregated these signals into pharmacological modules representing key pharmacological processes. Results Pharmacogene alterations were generally infrequent at the individual gene level but showed recurrent, non-uniform patterns across cancers. A uniform-gene null model showed that descriptive combined scores were more concentrated than expected under a simplified uniform-gene background, with a global Gini index of 0.306 compared with a null mean of 0.248, and the top 10 gene-cancer pairs accounting for 10.1% of the total score compared with a null mean of 7.8%. Broad non-synonymous mutation, copy number amplification, and copy number deletion frequencies averaged 3.8%, 4.7%, and 0.3%, respectively, while the mean frequency of any integrated alteration was 8.6%. A descriptive prioritization score, defined as broad non-synonymous mutation frequency plus copy number amplification frequency, identified recurrent high-ranking alterations in transporter- and drug-handling genes, including ABCB1, ABCC1, and ABCC2. Sensitivity analyses supported cautious interpretation, including partial ranking robustness across stricter truncating/LoF-like mutation definitions, small-cohort exclusions, and cBioPortal GISTIC amplification sensitivity analyses. Systematic CNA-expression testing across 280 gene-cancer pairs identified 64 significant positive associations after FDR correction, with representative examples including GGH, ERCC1, and SLC29A1. Module-level pharmacogenomic alteration was not significantly associated with survival in exploratory treatment-agnostic survival models. Conclusion Our findings provide an exploratory TCGA-based map of somatic alterations of pharmacogenes across cancers and identify recurrent alteration patterns that warrant validation in treatment-annotated and external datasets.