Identification of outcome-related driver mutations in cancer using conditional co-occurrence distributions

Abstract

The methods proposed for the detection of cancer driver mutations are based on the estimation of background mutation rate, impact on protein function, or network influence. Instead, we focus on those influencing patient survival. For this, an approximation of the log-rank test has been systematically applied even though it assumes a large and similar number of patients in both risk groups, which is violated in cancer genomics. Here, we propose VALORATE, a novel algorithm for the estimation of the null distribution for the log-rank test independently of the number of mutations. VALORATE is based on conditional distributions of the co-occurrences between events and mutations. The results using simulations, comparisons with other methods, TCGA and ICGC cancer datasets, and validations, suggests that VALORATE is accurate, fast, and can identify known and novel gene mutations. Our proposal and results may have important implications in cancer biology, in bioinformatics analyses, and ultimately in precision medicine.