Global Geo-Pharmacogenomics: Environmental Mutational Signatures Drive Population-Level Heterogeneity in Anticancer Drug Response

The interplay between the environmental exposome and the cancer genome remains a critical gap in precision oncology. While somatic mutational signatures—genomic fossils imprinted by exposures such as ultraviolet radiation; tobacco smoke; and industrial pollutants—are well characterised for their etiological significance; their functional impact on therapeutic efficacy remains largely unexplored. We hypothesised that these environmental genomic scars induce distinct pharmacogenomic vulnerabilities and resistance mechanisms that vary by geographical exposure patterns. This study employs two complementary analytical frameworks. First, a linear regression-based pharmacogenomic screen across four datasets (GDSC1, GDSC2, CTRP, CCLE; 1001 cell lines, 31 cancer types) identified 608 statistically significant (p < 0. 01) mutational signature–drug interactions, revealing that UV-associated signature SBS7a is associated with broad-spectrum therapeutic resistance, including to BRAF inhibitors (PLX-4720, p < 10−4), while pollution-driven oxidative stress (SBS18) is associated with sensitivity to p38 MAPK inhibition (VX-702, r = −0. 45, p < 10−9). Second, an XGBoost predictive model trained exclusively on 33, 679 GDSC2 records using a 1265-feature matrix integrating 40 SBS signatures, drug chemistry descriptors, proteomic features, and two satellite-derived environmental variables (NASA PM2. 5 and UV) —achieved R2 = 0. 7973 on a 20% holdout set (grouped cross-validation R2 = 0. 7296). SHAP analysis revealed that satellite-derived PM2. 5 (ZonePM25) ranked 7th of 1265 features, exceeding all 40 individual SBS mutational signatures. Synthesising these findings with satellite-derived atmospheric data, we constructed an exploratory spatially interpolated risk surface spanning 122 nations, generating the hypothesis that uniform drug efficacy assumptions may not apply globally. These findings suggest that a patient’s environmental exposure history may constitute a measurable pharmacogenomic variable. This exploratory framework warrants validation in independent datasets and with individual-level geographic data before clinical application.