TY - JOUR
T1 - Understanding Drug Resistance in Breast Cancer with Mathematical Oncology
AU - Brocato, Terisse
AU - Dogra, Prashant
AU - Koay, Eugene J
AU - Day, Armin
AU - Chuang, Yao-Li
AU - Wang, Zhihui
AU - Cristini, Vittorio
N1 - Funding Information:
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.).
PY - 2014/6/1
Y1 - 2014/6/1
N2 - 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.
AB - 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.
KW - Computer simulation
KW - Mathematical modeling
KW - Molecular signaling network
KW - Physical barrier
KW - Translational research
KW - Tumor growth and invasion
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U2 - 10.1007/s12609-014-0143-2
DO - 10.1007/s12609-014-0143-2
M3 - Article
C2 - 24891927
AN - SCOPUS:84905727409
SN - 1943-4588
VL - 6
SP - 110
EP - 120
JO - Current Breast Cancer Reports
JF - Current Breast Cancer Reports
IS - 2
ER -