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

Abstract

The analysis of the mutational landscape of cancer, including mutual exclusivity and co-occurrence of mutations, has been instrumental in studying the disease. We hypothesized that exploring the interplay between co-occurrence, mutual exclusivity, and functional interactions between genes will further improve our understanding of the disease and help to uncover new relations between cancer driving genes and pathways. To this end, we designed a general framework, BeWith, for identifying modules with different combinations of mutation and interaction patterns. We focused on three different settings of the BeWith schema: (i) BeME-WithFun, in which the relations between modules are enriched with mutual exclusivity, while genes within each module are functionally related; (ii) BeME-WithCo, which combines mutual exclusivity between modules with co-occurrence within modules; and (iii) BeCo-WithMEFun, which ensures co-occurrence between modules, while the within module relations combine mutual exclusivity and functional interactions. We formulated the BeWith framework using Integer Linear Programming (ILP), enabling us to find optimally scoring sets of modules. Our results demonstrate the utility of BeWith in providing novel information about mutational patterns, driver genes, and pathways. In particular, BeME-WithFun helped identify functionally coherent modules that might be relevant for cancer progression. In addition to finding previously well-known drivers, the identified modules pointed to other novel findings such as the interaction between NCOR2 and NCOA3 in breast cancer. Additionally, an application of the BeME-WithCo setting revealed that gene groups differ with respect to their vulnerability to different mutagenic processes, and helped us to uncover pairs of genes with potentially synergistic effects, including a potential synergy between mutations in TP53 and the metastasis related DCC gene. Overall, BeWith not only helped us uncover relations between potential driver genes and pathways, but also provided additional insights on patterns of the mutational landscape, going beyond cancer driving mutations. Implementation is available at https://www.ncbi.nlm.nih.gov/CBBresearch/Przytycka/software/bewith.html

Personalized Cancer Therapy Prioritization Based on Driver Alteration Co-occurrence Patterns

“…Indeed, the analysis of the mutational landscape of cancer has also uncovered the existence of mutual exclusivity and co-occurrence patterns among driver gene alterations [16,69]. Many computational tools have been developed to identify those combinatorial patterns experimentally (i.e via CRISPR-Cas9 screens [70,71]) or computationally [72][73][74][75][76][77][78][79]. Patterns of mutual exclusivity can arise from functional redundancy, context-specific dependencies (i.e tumor type or sub-type specific driving alterations), or synthetic lethality interactions.…”

“…This commonly happens when several oncogenes are coamplified as part of the same genomic region and our method already accounts for this. However, co-occurrence patterns can also emerge as a result of the exposure to other mutagenic processes that increase the mutational burden, the chromosomal instability, or that leave specific mutational signatures [75,76,90]. Context or tumor type specific dependencies can also be a source of indirect associations with drug response.…”

Mining Pathway Associations from Networks of Mutual Exclusivity Interactions

“…A common approach for exploratory mining is to find biologically interesting sub-networks, also called modules. Recent examples of methods to find modules include BeWith [14], CDPath [55] and CD-CAP [24].…”

Network-based approaches elucidate differences within APOBEC and clock-like signatures in breast cancer

“…For example, the MUTYH signature mentioned above is known to be caused by inactivation of the MUTYH gene [13] but can also occur in cancers that do not harbor this aberration. With the observation that different mutations in functionally related genes can lead to the same cancer phenotype [14,15,16], cancer phenotypes are increasingly considered in the context of genetically dysregulated pathways rather than in the context of individual genes [17,18,19,20,21,22]. Hence, we postulated that identifying mutated subnetworks and differentially expressed gene groups that are associated with mutational signatures can provide new insights on the etiology of mutational signatures.…”