Identification of driver pathways in cancer based on combinatorial patterns of somatic gene mutations

With the availability of high-throughput technologies, a huge number of biological data (e.g., somatic mutation, DNA methylation and gene expression) in multiple cancers have been generated. A major challenge is to identify functional and vital driver mutation import for the initiation and progression of cancer. In this paper, we introduce a novel method, named Co-occurring mutated metagene Genetic Algorithm (CoGA), to solve the maximum weight submatrix problem, with the aim of distinguishing mutated driver pathways in cancer. The algorithm relies on the combinatorial properties of mutations in the same pathways: high coverage and mutual exclusivity, and the possible properties of mutations in different pathways: co-occurring pattern. We carried out the experiment with glioblastoma multiform (GBM) data. The experimental results show that compared with the original model, our algorithm has more potential to identify driver pathways in cancer with biological significance.