Modeling tissue-specific breakpoint proximity of structural variations from 2,382 whole-genomes to identify cancer drivers

Abstract

Structural variations (SVs) in cancer cells often impact large genomic regions with functional consequences. However, little is known about the genomic features related to the breakpoint distribution of SVs in different cancers, a prerequisite to distinguish loci under positive selection from those with neutral evolution. We developed a method that uses a generalized additive model to investigate the breakpoint proximity curves from 2,382 whole-genomes of 32 cancer types. We find that a multivariate model, which includes linear and nonlinear partial contributions of various tissue-specific features and their interaction terms, can explain up to 57% of the observed deviance of breakpoint proximity. In particular, three-dimensional genomic features such as topologically associating domains (TADs), TAD-boundaries and their interaction with other features show significant contributions. The model is validated by identification of known cancer genes and revealed putative drivers in novel cancers that have previous evidence of therapeutic relevance in other cancers.