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McKenna MT, Weis JA, Brock A, et al. Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer. Transl Oncol. 2018;11(3):732-742doi: 10.1016/j.tranon.2018.03.009.
McKenna, M. T., Weis, J. A., Brock, A., Quaranta, V., & Yankeelov, T. E. (2018). Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer. Translational oncology, 11(3), 732-742. https://doi.org/10.1016/j.tranon.2018.03.009
McKenna, Matthew T, et al. "Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer." Translational oncology vol. 11,3 (2018): 732-742. doi: https://doi.org/10.1016/j.tranon.2018.03.009
McKenna MT, Weis JA, Brock A, Quaranta V, Yankeelov TE. Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer. Transl Oncol. 2018 Jun;11(3):732-742. doi: 10.1016/j.tranon.2018.03.009. Epub 2018 Apr 16. PMID: 29674173; PMCID: PMC6056758.
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Transl Oncol. 2018 Jun;11(3):732-742. doi: 10.1016/j.tranon.2018.03.009. Epub 2018 Apr 16.

Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer.

Translational oncology

Matthew T McKenna, Jared A Weis, Amy Brock, Vito Quaranta, Thomas E Yankeelov

Affiliations

  1. Vanderbilt University Institute of Imaging Science, Nashville, TN; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN.
  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 Biomedical Engineering, The University of Texas at Austin, Austin, TX; Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX; Department of Oncology, The University of Texas at Austin, Austin, TX; Institute for Computational and Engineering Sciences, The University of Texas at Austin, Austin, TX; Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX. Electronic address: [email protected].

PMID: 29674173 PMCID: PMC6056758 DOI: 10.1016/j.tranon.2018.03.009

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.

Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

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