Abstract B1-38: PARADIGM-SHIFT: Predicts the functional impact of ‘driver modules’ in multiple cancers using pathway impact analysis

Abstract

Abstract The major mechanism by which cancer arises is through genomic alterations. These alterations can lead to changes in gene regulation, protein structure, and function. Individual tumors can contain hundreds to thousands of alterations. It is critical to distinguish alterations that have an important role defining the cancer – drivers – from alterations that are unimportant to the tumor – passengers. Driver genes can lead to significant changes in their pathways; however, alterations in a single gene may not be sufficient to explain all the pathway perturbations across patients. Additional alterations could range from DNA copy number changes, gene-fusions, or even lesser understood non-coding mutations. Identifying these ‘driver modules’ is essential for understanding cancer disease mechanisms, which can help guide treatment decisions as well as identify novel targets for treatment. We have developed a functional impact prediction method called PARADIGM-SHIFT based on integrated pathway analysis to discriminate loss-of-function, neutral, and gain-of-function alterations. Utilizing the set of regulatory interactions annotated for a given gene, we can detect a shift in the downstream effects of an altered gene compared to what is expected from its upstream influences. Additionally, since these shifts in pathway signal can be detected for all samples, PARADIGM-SHIFT can be used to identify additional genomic alterations that lead to similar changes to the altered pathway to form ‘driver modules.’ Application of our method to the TCGA Pan-Cancer cohort identifies many genes with significant alterations that lead to loss- and gain- of function. PARADIGM-SHIFT then identifies several additional genomic alterations, including non-coding mutations variants, which are significantly implicated in these pathway changes. This analysis offers insight into the mechanism of non-coding mutations orthogonal to sequence-based methods by interpreting the pathway consequences of these variants. Citation Format: Sam Ng, Christopher Benz, David Haussler, Joshua M. Stuart. PARADIGM-SHIFT: Predicts the functional impact of ‘driver modules’ in multiple cancers using pathway impact analysis. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-38.