Abstract A25: Using clique relaxations to identify highly connected clusters in molecular networks in cancer.

Abstract

Abstract Graph theory approaches are now increasingly used to navigate through big data from “omics” applications for delineating signature events in cancer subtypes, identification of new molecular targets and assigning significance of specific mutations. While usual suspects such as p53 and Rb are widely known to all researchers, ability to identify additional, highly relevant, potentially targetable genes implicated in cancer remains of great importance. Identification of cancer signatures and driver mutations turned out to be more challenging/ more complex than we originally thought as frequency of specific mutations can vary significantly leaving some potentially highly relevant (and potentially targetable) events out of the picture. We proposed to identify such targetable genes as parts of large “highly connected” clusters (also known as functional modules) in the corresponding networks. Using our recently developed algorithms, we have been able to exactly identify the largest highly connected cluster in sparse networks with up to several billion nodes. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):A25. Citation Format: Lidia V. Kulemina, Grigory Pastukhov, Alexander Veremyev, Vladimir L. Boginski. Using clique relaxations to identify highly connected clusters in molecular networks in cancer. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr A25.