Modelling double strand break susceptibility to interrogate structural variation in cancer

Abstract

Structural variants (SVs) are known to play important roles in a variety of cancers, but their origins and functional consequences are still poorly understood. Many SVs are thought to emerge via errors in the repair processes following DNA double strand breaks (DSBs) and previous studies have experimentally measured DSB frequencies across the genome in cell lines. Using these data we derive the first quantitative genome-wide models of DSB susceptibility, based upon underlying chromatin and sequence features. These models are accurate and provide novel insights into the mutational mechanisms generating DSBs. Models trained in one cell type can be successfully applied to others, but a substantial proportion of DSBs appear to reflect cell type specific processes. Using model predictions as a proxy for susceptibility to DSBs in tumours, many SV enriched regions appear to be poorly explained by selectively neutral mutational bias alone. A substantial number of these regions show unexpectedly high SV breakpoint frequencies given their predicted susceptibility to mutation, and are therefore credible targets of positive selection in tumours. These putatively positively selected SV hotspots are enriched for genes previously shown to be oncogenic. In contrast, several hundred regions across the genome show unexpectedly low levels of SVs, given their relatively high susceptibility to mutation. These novel "coldspot" regions appear to be subject to purifying selection in tumours and are enriched for active promoters and enhancers. We conclude that models of DSB susceptibility offer a rigorous approach to the inference of SVs putatively subject to selection in tumours.