Understanding drug resistance in breast cancer with mathematical oncology

被引：23

作者：

Brocato T. ^{[1
]}

Dogra P. ^{[2
]}

Koay E.J. ^{[3
,4
]}

Day A. ^{[2
]}

Chuang Y.-L. ^{[2
]}

Wang Z. ^{[2
]}

Cristini V. ^{[1
,2
,5
]}

机构：

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

[2] Department of Pathology, University of New Mexico, 1 University of New Mexico, Albuquerque

[3] Department of Radiation Oncology, University of Texas, MD Anderson Cancer Center, Houston, TX

[4] Department of Nanomedicine, Houston Methodist Research Institute, Houston, TX

[5] Department of Mathematics, Faculty of Science, King Abdulaziz University

来源：

Current Breast Cancer Reports | 2014年 / 6卷 / 2期

基金：

美国国家卫生研究院; 美国国家科学基金会;

关键词：

Computer simulation; Mathematical modeling; Molecular signaling network; Physical barrier; Translational research; Tumor growth and invasion;

D O I：

10.1007/s12609-014-0143-2

中图分类号：

学科分类号：

摘要：

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. © Springer Science+Business Media 2014.

引用

页码：110 / 120

页数：10

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