title: Understanding Drug Resistance in Breast Cancer with Mathematical Oncology.
abstract: Chemotherapy is 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 due to 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 (e.g., 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.
authors.affiliation: Department of Chemical and Nuclear Engineering and Center for Biomedical Engineering, University of New Mexico, Albuquerque, NM 87131.
authors.forename: Terisse
authors.initials: T
authors.lastname: Brocato
authors.affiliation: Department of Pathology, University of New Mexico, Albuquerque, NM 87131.
authors.forename: Prashant
authors.initials: P
authors.lastname: Dogra
authors.affiliation: Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030 ; Department of Nanomedicine, Houston Methodist Research Institute, Houston, TX 77030.
authors.forename: Eugene J
authors.initials: EJ
authors.lastname: Koay
authors.affiliation: Department of Pathology, University of New Mexico, Albuquerque, NM 87131.
authors.forename: Armin
authors.initials: A
authors.lastname: Day
authors.affiliation: Department of Pathology, University of New Mexico, Albuquerque, NM 87131.
authors.forename: Yao-Li
authors.initials: YL
authors.lastname: Chuang
authors.affiliation: Department of Pathology, University of New Mexico, Albuquerque, NM 87131.
authors.forename: Zhihui
authors.initials: Z
authors.lastname: Wang
authors.affiliation: Department of Chemical and Nuclear Engineering and Center for Biomedical Engineering, University of New Mexico, Albuquerque, NM 87131 ; Department of Pathology, University of New Mexico, Albuquerque, NM 87131 ; Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
authors.forename: Vittorio
authors.initials: V
authors.lastname: Cristini
keywords: computer simulation; mathematical modeling; molecular signaling network; physical property; translational research; tumor growth and invasion