Pathway based identification of rare mutation effect in cancer

Abstract

Identifying driver mutation is important in understanding disease mechanism and future application of custom tailored therapeutic decision. Functional analysis of mutational impact usually focuses on the gene expression level of the mutated gene. However, complex regulatory network may cause differential gene expression among functional neighbors of the mutated gene. We suggest a new approach for discovering rare mutations that have real impact in the context of pathway; philosophy of our method is iteratively combining rare mutations until no more mutations can be added, under the condition that the combined mutational event can statistically discriminate pathway level mRNA expression between groups with and without mutation events. Our approach is shown to sensitively capture mutations that change pathway level gene expression at breast cancer data.