Abstract 5227: Quantitative network models of signaling and drug response in melanoma.

Abstract

Abstract Use of targeted drug combinations are potentially key to preventing emergence of resistance to initially successful single agents such as RAF inhibitors. Thus, systematic nomination of novel drug combinations is important to develop effective therapeutic strategies. Here, we present an experimental-computational network pharmacology method to infer quantitative signaling networks in tumor cells, predict response to perturbations, and ultimately, nominate targeted drug combinations that will generate a desired phenotypic response. We use a series of targeted drugs, singly or in combination to perturb cancer cells. We measure quantitative proteomic (total and phoshpo-protein levels) and phenotypic (e.g. cell viability, apoptosis, cell cycle progression) response profiles to perturbations. Proteomic responses are measured with the reverse phase protein array technology. Next, we infer quantitative network models, using the response profiles as training sets. Solution space of all network model configurations is prohibitively large and Monte Carlo based inference algorithms fail to generate accurate network models in sizes relevant for cancer biology. In order to solve the inference problem, we have adapted an iterative and probabilistic inference algorithm, belief propagation from statistical physics. Resulting network models are based on simple non-linear differential equations and quantitatively link signaling events to phenotypic changes. We have generated quantitative network models of signaling in RAF inhibitor resistant melanoma cell lines in sizes (i.e. ∼100 nodes) unreachable by other network inference algorithms. We have nominated novel combination therapies through combinatorial in silico perturbations of all nodes in derived networks and experimentally tested the predicted phenotypic responses. Citation Format: Anil Korkut, Weiqing Wang, Evan Molinelli, Martin Miller, Poorvi Kaushik, Arman Aksoy, Chris Sander. Quantitative network models of signaling and drug response in melanoma. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5227. doi:10.1158/1538-7445.AM2013-5227