Related Experiment Video
Updated: Jun 24, 2026
Orthotopic Transplantation of Breast Tumors as Preclinical Models for Breast Cancer
Published on: May 18, 2020
Prediction of drug response in breast cancer using integrative experimental/computational modeling
Hermann B Frieboes1, Mary E Edgerton, John P Fruehauf
1School of Health Information Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
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.
Insights
Mathematical modeling helps predict breast cancer treatment resistance by linking tumor growth to cell phenotypes and drug concentrations. This approach could improve patient outcomes by personalizing therapy.
Area of Science:
- Oncology
- Mathematical Biology
- Pharmacology
Background:
- Nearly 30% of early-stage breast cancer patients experience disease recurrence due to systemic therapy resistance.
- Current models identify three resistant phenotypes: altered drug transport, enhanced detoxification/repair, and apoptosis evasion.
- Physiologic resistance from oxygen/nutrient diffusion gradients is known but difficult to quantify clinically.
Purpose of the Study:
- To develop a mathematical model integrating tumor growth, resistant phenotypes, and microenvironment factors.
- To link cellular resistance mechanisms to observable tumor growth and regression dynamics.
- To explore computational approaches for predicting and overcoming chemotherapy resistance.
Main Methods:
- Implemented a 3D mathematical model of tumor drug response.
- Incorporated effects of local drug, oxygen, and nutrient concentrations.
- Integrated experimentally observed resistant cell phenotypes into the model.
- Hypothesized functional relationships between tumor growth/regression and cellular phenotypes.
Main Results:
- The model links tumor growth and regression to underlying cellular phenotypes and microenvironmental conditions.
- Computational modeling provides a framework to quantify the role of diffusion gradients in resistance.
- The study demonstrates the potential to predict resistance based on specific tumor properties.
Conclusions:
- An integrative computational modeling approach enhances the value of pharmacokinetic data.
- This method can predict treatment resistance based on tumor characteristics.
- Improved prediction of resistance can lead to better treatment strategies and patient outcomes.
