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Understanding Drug Resistance in Breast Cancer with Mathematical Oncology

  • Risk, Prevention, and Screening (TA Patel, Section Editor)
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Abstract

Chemotherapy is the mainstay of treatment for the majority of patients with breast cancer but results in only 26 % of patients with distant metastasis living 5 years past treatment in the United States, largely because of drug resistance. The complexity of drug resistance calls for an integrated approach of mathematical modeling and experimental investigation to develop quantitative tools that reveal insights into drug resistance mechanisms, predict chemotherapy efficacy, and identify novel treatment approaches. This paper reviews recent modeling work for understanding cancer drug resistance through the use of computer simulations of molecular signaling networks and cancerous tissues, with a particular focus on breast cancer. These mathematical models are developed by drawing on current advances in molecular biology, physical characterization of tumors, and emerging drug delivery methods (eg, nanotherapeutics). We focus our discussion on representative modeling works that have provided quantitative insight into chemotherapy resistance in breast cancer and how drug resistance can be overcome or minimized to optimize chemotherapy treatment. We also discuss future directions of mathematical modeling in understanding drug resistance.

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Acknowledgments

This work has been supported in part by the National Science Foundation (NSF) Grant DMS-1263742 (Z.W., V.C.), NSF SBIR 1315372 (V.C.), the National Institutes of Health (NIH) Grant 1U54CA149196 (V.C.), 1U54CA143837 (E.K., V.C.), 1U54CA151668 (V.C.), and 1U54CA143907 (V.C.), the University of New Mexico Cancer Center Victor and Ruby Hansen Surface Professorship in Molecular Modeling of Cancer (V.C.), the Houston Methodist Research Institute (E.K., V.C.), and the Anne Eastland Spears Fellowship for GI Cancer Research (E.K.).

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Conflict of Interest

Terisse Brocato, Prashant Dogra, Eugene J. Koay, Armin Day, Yao-Li Chuang, Zhihui Wang, and Vittorio Cristini all declare that they have no conflict of interest.

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This article does not contain any studies with human or animal subjects performed by any of the authors.

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Authors and Affiliations

  1. Department of Chemical and Nuclear Engineering and Center for Biomedical Engineering, University of New Mexico, Albuquerque, NM, USA

    Terisse Brocato & Vittorio Cristini

  2. Department of Pathology, University of New Mexico, MSC08 4640, 1 University of New Mexico, Albuquerque, NM, 87131, USA

    Prashant Dogra, Armin Day, Yao-Li Chuang, Zhihui Wang & Vittorio Cristini

  3. Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Eugene J. Koay

  4. Department of Nanomedicine, Houston Methodist Research Institute, Houston, TX, USA

    Eugene J. Koay

  5. Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Vittorio Cristini

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  1. Terisse Brocato
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  2. Prashant Dogra
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  3. Eugene J. Koay
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  4. Armin Day
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  5. Yao-Li Chuang
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  6. Zhihui Wang
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  7. Vittorio Cristini
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Correspondence to Zhihui Wang or Vittorio Cristini.

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T. Brocato, P. Dogra, and E.J. Koay contributed equally to this work.

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Brocato, T., Dogra, P., Koay, E.J. et al. Understanding Drug Resistance in Breast Cancer with Mathematical Oncology. Curr Breast Cancer Rep 6, 110–120 (2014). https://doi.org/10.1007/s12609-014-0143-2

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