The central goal of this proposal is to develop and experimentally validate a computational model of cancer-related signaling networks that can be used to identify subsets of patients that will respond to pathway- targeted therapeutics and to do this prior to initiation of clinical trials so that drugs with efficacy in small patient subsets are not missed. Our hypothesis is that a robust computational model can be developed iteratively from measurements of molecular and biological responses to pathway inhibitors in a """"""""system"""""""" comprised of approximately 60 well characterized cancer and normal breast cell lines grown in vitro. We will focus on agents that target the Raf-MEK-ERK signaling module. However, the model and number of therapeutics tested will increase as the approach proves successful. We expect that an important byproduct of this approach will be identification of pathway inhibitors that are best used in combination. This program will be executed in 4 projects and 5 cores. The central project will develop a discrete Pathway Logic model that manages the concentrations of states that are measured experimentally (e.g., proteins, metabolites, cellular structures) as entities in interacting pathways. The model defines pathway responses to perturbations using logical statements that describe how cellular components interact and propagate signals down pathways in a state concentration-dependent manner (e.g. an increased amount of phosphorylated protein might """"""""activate"""""""" a signal propagation rule) so that models can be developed that apply to individual cell lines or tumors and so can predict individual responses. Three experimental projects will provide measurements to support model development and test model predictions. One project will focus on cellular and molecular responses to signal transduction pathway siRNA and small molecule inhibitors in the breast cancer and normal cell lines. A second project will critically examine the influence of microenvironmental interactions (i.e. cell-ECM, cell-cell myoepithelial and stromal-interactions) on responses to Raf-MEK-ERK inhibitors. A third project will use the Bernards laboratory library of retroviral vectors encoding shRNAs to efficiently screen for inhibitors that confer resistance or sensitivity to Raf-MEK-ERK inhibitors. Data quality control will be insured since all projects will work through cores that standardize important aspects of cell culture, reagent development and validation, molecular profiling and cellular response.
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