Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer |
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Authors: | Matthew T McKenna Jared A Weis Amy Brock Vito Quaranta Thomas E Yankeelov |
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Institution: | 2. Department of Biomedical Engineering, Vanderbilt University, Nashville, TN;3. Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX;4. Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, TN;5. Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX;11. Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX |
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Abstract: | Medical oncology is in need of a mathematical modeling toolkit that can leverage clinically-available measurements to optimize treatment selection and schedules for patients. Just as the therapeutic choice has been optimized to match tumor genetics, the delivery of those therapeutics should be optimized based on patient-specific pharmacokinetic/pharmacodynamic properties. Under the current approach to treatment response planning and assessment, there does not exist an efficient method to consolidate biomarker changes into a holistic understanding of treatment response. While the majority of research on chemotherapies focus on cellular and genetic mechanisms of resistance, there are numerous patient-specific and tumor-specific measures that contribute to treatment response. New approaches that consolidate multimodal information into actionable data are needed. Mathematical modeling offers a solution to this problem. In this perspective, we first focus on the particular case of breast cancer to highlight how mathematical models have shaped the current approaches to treatment. Then we compare chemotherapy to radiation therapy. Finally, we identify opportunities to improve chemotherapy treatments using the model of radiation therapy. We posit that mathematical models can improve the application of anticancer therapeutics in the era of precision medicine. By highlighting a number of historical examples of the contributions of mathematical models to cancer therapy, we hope that this contribution serves to engage investigators who may not have previously considered how mathematical modeling can provide real insights into breast cancer therapy. |
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Keywords: | Address all correspondence to: Thomas E Yankeelov Ph D Department of Biomedical Engineering Cockrell School of Engineering The University of Texas at Austin 107 W Dean Keeton BME Building 1 University Station C0800 Austin Texas 78712 |
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