Understanding Drug Resistance in Breast Cancer with Mathematical Oncology

Overview

Journal Curr Breast Cancer Rep

Date 2014 Jun 4

PMID 24891927

Citations 20

Authors

Terisse Brocato

Prashant Dogra

Eugene J Koay

Armin Day

Yao-Li Chuang

Zhihui Wang

Vittorio Cristini

Affiliations

Soon will be listed here.

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.

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