Prediction of Drug Response in Breast Cancer Using Integrative Experimental/Computational Modeling

¹School of Health Information Sciences and ²Division of Nanomedicine, and ³Department of Biomedical Engineering, University of Texas Health Science Center, Houston, Texas; Departments of ⁴Anatomic Pathology and ⁵Experimental Therapeutics, and ⁶Systems Biology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas; ⁷Department of Bioengineering, Rice University, Houston, Texas; ⁸Department of Biomedical Engineering, The University of Texas, Austin Texas; ⁹Division of Hematology/Oncology, Department of Medicine, University of California, Irvine Medical Center, Orange, California; ¹⁰School of Pharmacy, Centre for Biomolecular Sciences, University Park, University of Nottingham, United Kingdom; ¹¹Departments of Radiology and Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida

^* To whom correspondence should be addressed. E-mail: vittorio.cristini{at}uth.tmc.edu.

	Abstract

Nearly 30% of women with early-stage breast cancer develop recurrent disease attributed to resistance to systemic therapy. Prevailing models of chemotherapy failure describe three resistant phenotypes: cells with alterations in transmembrane drug transport, increased detoxification and repair pathways, and alterations leading to failure of apoptosis. Proliferative activity correlates with tumor sensitivity. Cell-cycle status, controlling proliferation, depends on local concentration of oxygen and nutrients. Although physiologic resistance due to diffusion gradients of these substances and drugs is a recognized phenomenon, it has been difficult to quantify its role with any accuracy that can be exploited clinically. We implement a mathematical model of tumor drug response that hypothesizes specific functional relationships linking tumor growth and regression to the underlying phenotype. The model incorporates the effects of local drug, oxygen, and nutrient concentrations within the three-dimensional tumor volume, and includes the experimentally observed resistant phenotypes of individual cells. We conclude that this integrative method, tightly coupling computational modeling with biological data, enhances the value of knowledge gained from current pharmacokinetic measurements, and, further, that such an approach could predict resistance based on specific tumor properties and thus improve treatment outcome. [Cancer Res 2009;69(10):OF1–9]

Key Words: breast cancer, drug response, mathematical model