Unsupervised detection of cancer driver mutations with parsimony-guided learning

摘要

Methods are needed to reliably prioritize biologically active driver mutations over inactive passengers in high-throughput cancer sequencing datasets. We present ParsSNP, an unsupervised functional impact predictor that is guided by parsimony. ParsSNP uses an expectation-maximization framework to find mutations that explain tumor incidence broadly, without using pre-defined training labels that can introduce biases. We compare ParsSNP to five existing tools (CanDrA, CHASM, FATHMM Cancer, TransFIC, Condel) across five distinct benchmarks. ParsSNP outperformed the existing tools in 24 out of 25 comparisons. To investigate the real-world benefit of these improvements, ParsSNP was applied to an independent dataset of thirty patients with diffuse-type gastric cancer. It identified many known and likely driver mutations that other methods did not detect, including truncation mutations in known tumor suppressors and the recurrent driver RHOA Y42C. In conclusion, ParsSNP uses an innovative, parsimony-based approach to prioritize cancer driver mutations and provides dramatic improvements over existing methods.