使用QuantHDP在超过8,000个TCGA全基因组中发现结构变异特征

Many cancers have inherent defects in DNA damage response (DDR) which influence their sensitivity to tumor-targeting therapies. Examples include increased activity of immunotherapies in cancers with mismatch-repair deficiency and sensitivity to PARP inhibitors and platinum-based therapies in the context of homologous recombination (HR) deficiency. However, we are currently unable to reliably determine which DDR defects are present in a given cancer sample, which severely limits our ability to exploit these therapeutic vulnerabilities. Structural variants (SVs), or genomic rearrangements formed as a product of aberrant double strand break repair, hold promise as biomarkers of DDR state. Indeed, SVs affect a larger proportion of the cancer genome than any other form of genetic alteration and have features that reflect their mechanism of formation. Here, we develop QuantHDP, a novel computational method for detecting SV signatures which leverages complex modeling of genetic features to distinguish between cancers with different DDR alterations and potentially identify clinically relevant biomarkers. QuantHDP models SV features with probability distributions that reflect our knowledge of the biological mechanisms that generate them, accounts for expected differences in signature content by cancer type, and automatically infers the number of signatures present in a given dataset. In addition to recapitulating multiple previously established associations with defects in DDR - including signatures of BRCA1, BRCA2, and CDK12 alterations - we also uncover novel signatures that warrant further investigation.