70% of newly diagnosed invasive breast cancers express estrogen receptor-a (ER);advanced ER+ breast cancer remains an incurable disease. We will create a Center for Cancer Systems Biology (CCSB) and build predictive computational and mathematical models of how ER regulates molecular signaling and cellular functions to affect the risk of neoplastic transformation in the normal breast, and responsiveness to endocrine therapies in breast cancer. Robust predictive models will enable a greater understanding of ER action in the regulation of cell fate, leading to discoveries that contribute to reducing breast cancer mortality. A fully integrated and productive group of senior investigators with an established track-record of collaborative peer reviewed funding and publications, and joint education and training activities, will be supported by Core A: Administration, Evaluation and Planning. The critical informatics infrastructure required will be provided by Core B: Bioinformatics Infrastructure and Data Integration. Data will be obtained in a unique series of human breast cancer cells, rodent models, and human breast cancer specimens from women treated with TAM as their only form of adjuvant therapy for invasive breast cancer (Component 1). We will integrate methods from two different fields to model ER-regulated signaling (Component 2) by extracting small, subnetwork topologies by computational bioinformatics and using these models to inform mathematical modeling. Predictions from these models will be validated in vitro and in vivo, with extensive iterative modeling guided by experimental data and the robustness of model predictions.
Over 40,000 American women will die of breast cancer this year, one every 13 minutes. We will create a new Center for Cancer Systems Biology and build predictive computational and mathematical models of how ER acts to affect breast cancer risk and responsiveness to endocrine therapies in breast cancer. A greater understanding of ER action will lead to discoveries that contribute to reducing breast cancer mortality.
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