CoPath: discovering cooperative driver pathways using greedy mutual exclusivity and bi-clustering

Ziying Yang, Guoxian Yu, Jiantao Yu, Maozu Guo, Jun Wang

Abstract: The carcinogenesis is typically involved with the cooperations of multiple pathways. Therefore, discovering cooperative driver pathways can provide more precise therapy to patients. Existing cooperative driver pathway identification methods can only identify few (or previously well-known) driver pathways, because of noisy or single type knowledge, or of the insufficient attention to pathway cooperations. To address these problems, we develop a novel approach called CoPath that leverages genomic alteration profiles and bi-clustering to discover cooperative driver pathways. Based on the mutation profiles reconstructed from somatic mutations and copy number variations, CoPath firstly uses a greedy search on the gene signaling network to identify mutually exclusive modules of genes, which have common downstream events in the network. Next, it incorporates the identified mutually exclusive modules and gene interaction network as prior knowledge, and introduces a dual regularized bi-clustering method on gene expression data to cluster cooperative mutually exclusive modules. Finally, it identifies the modules in the same cluster as cooperative driver pathways. Extensive experiments on real cancer genomics dataset (Breast from TCGA) shows that CoPath can not only detect individual driver pathways but also uncover more relations between potential driver genes and pathways than related competitive approaches.