Understanding Drug Resistance in Breast Cancer with Mathematical Oncology

Terisse Brocato, Prashant Dogra, Eugene J Koay, Armin Day, Yao-Li Chuang, Zhihui Wang, Vittorio Cristini

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)110-120
Number of pages11
JournalCurrent Breast Cancer Reports
Issue number2
StatePublished - Jun 1 2014


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

ASJC Scopus subject areas

  • Oncology


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