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

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 optim...

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Main Authors: Matthew T. McKenna, Jared A. Weis, Amy Brock, Vito Quaranta, Thomas E. Yankeelov
Format: Article
Language:English
Published: Elsevier 2018-06-01
Series:Translational Oncology
Online Access:http://www.sciencedirect.com/science/article/pii/S193652331830113X
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spelling doaj-ef2bff32a0954ef8ae8d544972cb0fbe2020-11-24T21:19:03ZengElsevierTranslational Oncology1936-52332018-06-01113732742Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast CancerMatthew T. McKenna0Jared A. Weis1Amy Brock2Vito Quaranta3Thomas E. Yankeelov4Vanderbilt University Institute of Imaging Science, Nashville, TN; Department of Biomedical Engineering, Vanderbilt University, Nashville, TNDepartment of Biomedical Engineering, Vanderbilt University, Nashville, TNDepartment of Biomedical Engineering, The University of Texas at Austin, Austin, TXDepartment of Cancer Biology, Vanderbilt University School of Medicine, Nashville, TNDepartment 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; 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.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.http://www.sciencedirect.com/science/article/pii/S193652331830113X
collection DOAJ
language English
format Article
sources DOAJ
author Matthew T. McKenna
Jared A. Weis
Amy Brock
Vito Quaranta
Thomas E. Yankeelov
spellingShingle Matthew T. McKenna
Jared A. Weis
Amy Brock
Vito Quaranta
Thomas E. Yankeelov
Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer
Translational Oncology
author_facet Matthew T. McKenna
Jared A. Weis
Amy Brock
Vito Quaranta
Thomas E. Yankeelov
author_sort Matthew T. McKenna
title Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer
title_short Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer
title_full Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer
title_fullStr Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer
title_full_unstemmed Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer
title_sort precision medicine with imprecise therapy: computational modeling for chemotherapy in breast cancer
publisher Elsevier
series Translational Oncology
issn 1936-5233
publishDate 2018-06-01
description 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.
url http://www.sciencedirect.com/science/article/pii/S193652331830113X
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