The major mechanism by which cancer arises is through somatic mutations. These mutations can lead to alterations in gene regulation as well as changes in protein structure and function. Individual tumors can contain hundreds to thousands of mutations. It is critical to distinguish mutations that have an important role defining the cancer - driver mutations - from mutations that are unimportant to the tumor - passenger mutations. Differentiating driver and passenger events is essential for understanding cancer disease mechanisms, which can help guide treatment decisions as well as identify novel targets for treatment. Genomic probing with technologies such as expression arrays and high-throughput RNA sequencing provide insight into changes in gene regulation in cancer, but determining the tumorigenic role of a coding mutation is less clear. Genomic data coupled with pathway information may provide insight into the functional impact of a mutation to particular genes. We are developing a mutation prediction method based on integrated pathway analysis to discriminate loss-of-function, neutral, and gain-of-function mutations. Utilizing the set of regulatory interactions annotated for a given gene, we can detect a discrepancy in the downstream effects of an altered gene compared to what is expected from its upstream influences. We show that a score based on this discrepancy is highly predictive of the presence of a mutation and that the directionality of this discrepancy also reflects the gain- or loss-of-function in a gene. Application of our method to a set of known driver mutations reveals that there is a significantly strong signal for loss- and gain- of functional mutations in the surrounding network, demonstrating the sensitivity of this approach. In addition, when applied to the negative control of passenger mutations, the method predicts little pathway impact, indicating this approach also has high specificity. Application of this approach to all recurrent mutations in ovarian and glioblastoma multiforme cancers from the TCGA project identifies several important driver mutations across these cohorts. We also highlight the novel utility of this specific approach by comparison to earlier published approaches including MutSig, CHASM, and MutPred.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 2985. doi:1538-7445.AM2012-2985