Characterizing Mutually Exclusive Driver Mutations in PanCancer Efforts on cancer genomic study have revealed that mutations in cancerdriving oncogenes tend to mutually exclusive. To understand this tumorigenic mechanism and common cancer features, largescale genomic study and molecular analyses across many cancer types are essential. We compiled somatic mutation and transcriptome profiles for a pancancer dataset that contains tumor tissue samples from 32 cancer types for over 10,000 patients. The genomic and molecular analysis was applied to four pancancer subgroups including pangastrointestinal, pangynecological, pankidney and pansquamous, as well as the entire patient group. We identified seven genes, TP53, PIK3CA, PTEN, PIK3R1, LRP1B, CDH1, and TRRAP exhibited mutually exclusive mutation patterns across all cancer types (adjusted  mathrmp  ). The mutations in these genes occurred in 55.9% of all cancer patients and over 70% of patients with pangastrointestinal, pangynecological and pansquamous. Excepting LRP1B, the other six genes are included in the known cancer pathways. Notably, TP53, PIK3CA, and PIK3R1 are involved in over 20 cancerrelated pathways. The coexpression network analysis suggested that LRP1B was associated with multiple genes that showed significant enrichment in Wnt signal pathway (adjusted  mathrmp0.0023 ) and ERBB signal pathway (adjusted  mathrmp0.0097 ). We further constructed a regulatory network to infer novel regulations of LRP1B in cancer. In addition, based on the mutations in the seven genes, we divided the patients into different groups for survival analysis. We found that the survival rate of the patient group with TP53 mutations was significantly shorter than in other groups (log rank  mathrmp 5.88mathrme10 ) in pangynecological. Our analysis systematically revealed the mutually exclusive mutation patterns of seven cancer genes across 32 cancer types. It helps us to better understanding the relations among different cancer types and pancancer subgroups, providing new insights into common mechanisms underlying diverse cancer types.

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.