MEMCover: integrated analysis of mutual exclusivity and functional network reveals dysregulated pathways across multiple cancer types

Abstract

We introduce a classification of mutual exclusivity into three basic classes: within tissue type exclusivity, across tissue type exclusivity and between tissue type exclusivity. We then combined across-cancer mutual exclusivity with interactions data to uncover pan-cancer dysregulated pathways. Our new method, Mutual Exclusivity Module Cover (MEMCover) not only identified previously known Pan-Cancer dysregulated subnetworks but also novel subnetworks whose across cancer role has not been appreciated well before. In addition, we demonstrate the existence of mutual exclusivity hubs, putatively corresponding to cancer drivers with strong growth advantages. Finally, we show that while mutually exclusive pairs within or across cancer types are predominantly functionally interacting, the pairs in between cancer mutual exclusivity class are more often disconnected in functional networks.

BeWith: A Between-Within method to discover relationships between cancer modules via integrated analysis of mutual exclusivity, co-occurrence and functional interactions

“…On the other hand, mutually exclusive mutations might also reflect a situation where mutations in two genes are associated with two different cancer types or subtypes. We have previously observed that within cancer type mutual exclusivity is more enriched with physically interacting pairs of genes compared to between cancer type mutual exclusivity [3]. Thus, the presence or absence of interactions between genes with mutually exclusive mutations might provide hints toward the nature of mutual exclusivity.…”

“…While many methods to identify cancer related modules exist, such modules are typically identified by focusing on the relationships of genes within a module. In particular, there have been several previous attempts to combine mutual exclusivity and functional interactions for module identification [1–3,19]. However, most of these methods were primarily focused on finding functional modules that include genes with mutually exclusive mutations without considering the relationships between such modules.…”

Network-Based Method for Inferring Cancer Progression at the Pathway Level from Cross-Sectional Mutation Data

“…Interestingly, gene set (APC, EPHA3, EVC2) in our results has the same coverage degree but larger exclusive degree (90.9%) than gene set (APC, EPHA3, TCF7L2) in [20], showing that the gene set (APC, EPHA3, EVC2) is more likely in the same functional pathway [30]. APC and EPHA3 have stable co-expression together with a cytoplasmic form of BirA for efficient biotinylation of AP-tagged EPHA3 C-terminus [31].…”

Comutation and exclusion analysis in human tumors: A tool for cancer biology studies and for rational selection of multitargeted therapeutic approaches

“…Despite all this evidence, at present there is no systematic analysis of codependent mutations across all types of cancer. The only studies available deal with whole genes instead of specific mutations (Babur et al, 2015;Ciriello et al, 2011;Cui, 2010;Kim, Cho, Dao, & Przytycka, 2015;Rubio-Perez et al, 2015;Yeang, McCormick, & Levine, 2008), ignoring the fact that different mutations in the same gene can have widely different effects, for example, mutations in the epidermal growth factor receptor gene (EGFR) G735S, G796S, and E804G induce its oncogenic activation in prostate cancer, while R841K has no functional relevance (Cai et al, 2008).…”