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

• Published in: Cancer research (Chicago, Ill.) Vol. 69; no. 10; pp. 4484 - 4492
• Main Authors: FRIEBOES, Hermann B, EDGERTON, Mary E, FRUEHAUF, John P, ROSE, Felicity R. A. J, WORRALL, Lisa K, GATENBY, Robert A, FERRARI, Mauro, CRISTINI, Vittorio
• Format: Journal Article
• Language: English
• Published: Philadelphia, PA American Association for Cancer Research 15-05-2009
• Subjects: Antineoplastic agents; Antineoplastic Agents - therapeutic use; Apoptosis; Biological and medical sciences; Breast Neoplasms - drug therapy; Breast Neoplasms - pathology; Cell Culture Techniques; Cell Death; Cell Line, Tumor; Cell Survival; Computers, Molecular; Female; Gynecology. Andrology. Obstetrics; Humans; Mammary gland diseases; Medical sciences; Models, Biological; Models, Theoretical; Necrosis; Pharmacology. Drug treatments; Predictive Value of Tests; Tumors; Drug; Mammary gland diseases; Breast disease; Prediction; Breast cancer; Malignant tumor; Predictive factor; Modeling; Cancer
• ISSN: 0008-5472; 1538-7445
• DOI: 10.1158/0008-5472.can-08-3740

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.