Abstract
Positive selection on somatic mutations is the driving force for cancer progression. Growing evidence shows that the emergence of a driver mutation in a tumor sample depends on individual-specific factors, for example environmental exposures or the individual's germline genetic background. We term these individual-level factors as the "contexts" of a tumor. Our hypothesis is that mutations in a driver gene can bring different growth advantages in different contexts, resulting in "differential selection" on these 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 on a driver gene across individuals. DiffDriver accounts for variations of mutation rates across bases and individuals, while taking advantage of functional information of sequences to improve the power. Through simulations, we show DiffDriver reduces false positives and boosts power compared to current methods. Our results highlight that multiple individual-level factors create significant heterogeneity in the strength of selection acting on driver genes and 33% of driver genes showed differential selection in at least one of the contexts studied, including tumor clinical traits and tumor immune microenvironment subtypes. These results provided new insights into the context-dependent forces driving cancer evolution.