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

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

Requests for reprints: Vittorio Cristini, School of Health Information Sciences, University of Texas Health Science Center, 7000 Fannin #600, Houston, TX 77030. Phone: 713-500-3965; Fax: 713-500-3929; E-mail: vittorio.cristini{at}uth.tmc.edu.

Key Words: breast cancer • drug response • mathematical model

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):4484–92]

Major Findings: By extracting mathematical model parameter values for drug and nutrient delivery from monolayer (one-dimensional) experiments and using the functional relationships to compute drug delivery in MCF-7 spheroid (three-dimensional) experiments, we use the model to quantify the diffusion barrier effect, which alone can result in poor response to chemotherapy both from diminished drug delivery and from lack of nutrients required to maintain proliferative conditions.