1. Abstract 674: Predicting cellular response to therapy in breast cancer using mathematical modeling
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Wei He, Diane M. Demas, Isabel Conde, Yassi Fallah, William T. Baumann, and Ayesha N. Shajahan-Haq
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Cancer Research ,Oncology - Abstract
About 70% of all breast cancer tumors are estrogen receptor positive (ER+) and are treated with antiestrogen therapies. While the inevitability of developing resistance to these therapies remains uncontested, little is known about the mechanism and prevention of resistance. Our ultimate goal is to use mathematical modeling to optimize dynamic therapies that decrease proliferation and stave off resistance. In this initial study, we used MCF7 cells as a model of ER+ breast cancer and estrogen deprivation as a surrogate for aromatase inhibitors. We developed long-term estrogen deprived MCF7s (LTEDs) that proliferate similarly to untreated MCF7s but are resistant to antiestrogens. We collected time-course data for simultaneous gene expression and protein levels (NanoString Pan Cancer panel, a non-amplification based digital method) over 6 weeks to capture early molecular adaptations of deprived MCF7s and compare them to those present in LTEDs. PCA analysis of the mRNA and protein data shows a dramatic change in the first component due to estrogen deprivation, and a dramatic change in the second component associated with long-term resistance. Correlation analysis shows a large number of cell cycle genes that are similarly regulated, decreasing with estrogen deprivation and increasing with the onset of resistance. There is also a highly-correlated group of genes associated with resistance. This knowledge allowed us to hypothesize a molecular mechanism for resistance that can be tested experimentally. To begin building a dynamic model, we measured a 7-day time course of estrogen related proteins. The model is built around ER signaling and the cell cycle, and simulates protein and proliferation changes in response to deprivation and antiestrogen (ICI182,780; ICI) treatment. To determine which treatments to model in addition to estrogen deprivation and ICI, we looked for a promising sequential therapy to limit proliferation and, hopefully, stave off resistance compared to a single non-stop therapy. We found that alternating the targeting of CDK4/6 using Palbociclib (clinical anti-cancer therapy) with targeting of RUNX1 (gene that is increased at the onset of resistance in our model), could be a plausible strategy to inhibit endocrine resistance. Future work will involve extending the model to longer time scales and using it for treatment optimization. Citation Format: Wei He, Diane M. Demas, Isabel Conde, Yassi Fallah, William T. Baumann, Ayesha N. Shajahan-Haq. Predicting cellular response to therapy in breast cancer using mathematical modeling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 674.
- Published
- 2019
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