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

ABSTRACT: Structural variations (SVs) in cancer cells often impact large genomic regions with functional consequences. However, identification of SVs under positive selection is a challenging task because little is known about the genomic features related to the background breakpoint distribution in different cancers. We report 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-bounda

SUBMITTER: Martinez-Fundichely A 

PROVIDER: S-EPMC9512825 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature