Identifying Common Driver Pathways based on Pancancer Data The investigation of commonalities among different cancers is one of the important problems for deciphering cancers and will be helpful for personalized therapy in cancer treatment. Zhang et al. have presented the ComMDP method to solve this problem in 2017. However, when the number of samples among different cancers varies largely, accumulating the absolute weight value of every cancer, performed by the ComMDP method, may lead to missing some driver pathways. In this paper, an improved mathematical model is proposed by replacing the absolute weight values with the relative ratios of them, and introducing variance to minimize the dispersion of each ratio. By introducing a kind of short chromosome code and a greedy based recombination operator, a pathenogenetic algorithm PGAMDP is put forward for solving this model. Experimental results indicate that the PGAMDP algorithm is indeed able to detect some biologically meaningful gene sets which are missed by the ComMDP one. Hence it may become a useful complementary tool for identifying cancer pathways.

CoPath: discovering cooperative driver pathways using greedy mutual exclusivity and biclustering 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 wellknown) 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 biclustering 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 biclustering 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.