Khalil, A.; Supek, F.

Tumors often show an initial response to chemotherapy, but then develop resistance, leading to relapse and poor prognosis. We hypothesized that a genomic comparison of mutations in pre-treated versus treatment-naive tumors would serve to identify genes that confer resistance. A challenge in such an analysis is that therapy alters mutation burdens and signatures, confounding association studies and complicating identifying causal, selected mutations. We developed DiffInvex, a framework for identifying changes in selection acting on individual genes in somatic genomes. Crucially, DiffInvex draws on a mutation rate baseline that accounts for these shifts in neutral mutagenesis during cancer evolution. We applied DiffInvex to 9,953 cancer whole-genomes from 29 cancer types from 8 studies, containing both WGS of treatment-naive tumors and tumors pre-treated by various drugs, identifying genes where point mutations are under conditional positive or negative selection for a certain chemotherapeutic, suggesting resistance mechanisms occurring via point mutation. DiffInvex confirmed well-known chemoresistance-driver mutations in EGFR, ESR1, KIT and AR genes as being under conditional positive selection, with additional cancer types identified for EGFR and KIT. Additionally, DiffInvex identified 11 genes with treatment-associated selection for different classes of therapeutics. In most cases, these genes were common cancer genes including PIK3CA, APC, MAP2K4 and MAP3K1. This suggests that tumor resistance to therapy via mutation often occurs via selective advantages conferred by known driver genes, rather than via mutations in specialized resistance genes. Various gene-chemotherapy associations were further supported in tests for functional impact of mutations, again implemented in a conditional selection setting, as well as replicating in independent panel or exome sequencing data. In addition to nominating drug resistance genes that could be targeted by future therapeutics, DiffInvex can also be applied to diverse analysis in cancer evolution, such as comparing normal and tumoral tissues, or analyzing subclonal evolution, identifying changes in selection over time.