Abstract B03: Functional Stratification of Cancer Variants via Network Perturbations

Abstract

Abstract In the past decade, genome and exome sequencing projects have identified thousands of genetic variants in patients across a large number of cancer types. However, the explosion of genomic information has left many fundamental questions regarding genotype-phenotype relationships unresolved. One critical challenge is to distinguish causal disease mutations from non-pathogenic polymorphisms. Even when causal mutations are identified, the functional consequence of such mutations is often elusive. Classical one gene, one function, one disease models can not reconcile with the complexity that different mutations of the same gene often lead to different phenotypes. The extent to which network perturbations are involved in disease malfunction and how distinct interaction perturbation patterns can distinguish cancer mutations are largely unknown. Here we report a systematic approach to investigate genetic variant-specific effects on molecular interactions at large scale across diverse human cancers. Remarkably, in comparison to non-disease polymorphisms, disease mutations are more likely to associate with interaction perturbations. A large fraction of missense disease mutations are found to cause protein interaction alterations. While half result in loss of all their interactions, the other half exhibit selective elimination of specific interactions (edgetic). Different mutations of the same gene give rise to different interaction profiles, accounting for distinct disease outcomes. Edgetic mutations perturb interactions through disrupting specific interaction interfaces, and the perturbed partners are more likely expressed in relevant disease tissue. Together, our approach is insightful in prioritizing disease-causing variants, and uncovering patient mutation-specific disease mechanisms at a base-pair resolution, a critical step towards personalized precision medicine. Furthermore, our results suggest distinct interaction perturbations as a widespread mechanism underlying genetic heterogeneity, providing a fundamental link between genotype and phenotype in cancer. Citation Format: Nidhi Sahni, Song Yi. Functional Stratification of Cancer Variants via Network Perturbations [abstract]. In: Proceedings of the AACR Precision Medicine Series: Opportunities and Challenges of Exploiting Synthetic Lethality in Cancer; Jan 4-7, 2017; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2017;16(10 Suppl):Abstract nr B03.