DiffDriver Detects Context-Associated Selection of Cancer Driver Genes

Abstract

Positive selection of somatic mutations is a driving force for cancer progression. Growing evidence shows that the emergence of a driver mutation in a tumor sample depends on individual-specific contexts, for example environmental exposures or the germline genetic background. Mutations in a driver gene could potentially bring different growth advantages in different contexts, resulting in differential selection of genes in varying contexts. Identifying which contexts modulate selection strength provides critical insights into the selection forces driving tumorigenesis. However, due to the sparsity of somatic mutations and heterogeneous background mutational process across positions and individuals, identification of differential selection has limited power with current statistical tools and is prone to false positives. To address this, we developed a powerful statistical method, DiffDriver, that identifies associations between contexts and selection strength of a driver gene across individuals. DiffDriver accounted for variations of mutation rates across bases and individuals, while taking advantage of functional information of sequences to improve power. In simulations, DiffDriver reduced false positives and boosted power compared to current methods. Multiple individual-level factors created significant heterogeneity in the strength of selection acting on driver genes: 24.3% of driver genes showed differential selection in at least one of the clinical traits studied, and 8.2% of genes had differential selection in different tumor immune subtypes. These results provide insights into the context-associated forces driving cancer evolution.