Abstract A24: Network models of signaling and drug response in melanoma.

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Abstract Novel combination therapies are potentially key to preventing emergence of resistance to initially successful single agent therapies such as RAF inhibitors in melanoma. In order to systematically nominate novel and effective drug combinations, we have developed an integrated systems biology technology (perturbation biology) for inferring quantitative and predictive network models of signaling. Our modeling strategy combines systematic perturbation experiments, measurement of response profiles to perturbations, inference of network models and simulation of models with in silico perturbations. As a key component of the overall strategy, we have adapted the belief propagation (BP) inference algorithm from statistical physics to construct signaling models with several hundreds of measured entities. In parallel, we have developed a pathway informatics tool to automatically extract prior information from multiple signaling databases. We applied this approach to derive signaling network models in RAF inhibitor resistant melanoma cells and nominate drug combinations to overcome resistance in melanoma. First, we systematically perturb melanoma cells with a set of targeted drugs as single and paired agents. Next, we quantitatively measured proteomic (total and phospho-protein levels) and phenotypic (e.g. cell cycle arrest, cellular viability) responses to perturbations. We incorporated the generic prior information and context specific perturbation response data to construct quantitative network models, which capture multiple oncogenic pathways in melanoma. As shown by cross validation calculations, use of prior information significantly improved predictive power of models. Using the inferred network models of signaling, we systematically predicted response to tens of thousands of in silico perturbations. Our modeling and simulation strategy expanded the volume of drug response profile from few thousand experimental data points to millions of in silico data points. In addition, our models provided quantitative descriptions of signaling events in melanoma. Our perturbation biology technology is suitable for applications in diverse areas of molecular biology beyond cancer research. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):A24. Citation Format: Anil Korkut, Weiqing Wang, Evan Molinelli, Martin Miller, Poorvi Kaushik, Arman Aksoy, Xiaohong Jing, Nick Gauthier, Chris Sander. Network models of signaling and drug response in melanoma. [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 A24.