Dynamics of Tumor Subpopulations in Response to Targeted Therapies

Drugs targeting the specific genetic expression of a patient’s tumor have revolutionized the treatment of metastatic cancer. However, these tumors commonly recur due to the somatic evolution of drug persistent and resistant subpopulations during treatment. To better understand and combat acquired drug resistance, we developed a coupled, non-linear, differential system to model the dynamics of resistant and persistent tumor subpopulations. A Gompertz growth model is used to simulate bounded cell growth, while a general stochastic evolutionary pathway leading to drug resistance is implemented, based on in-vitro observations. Work will be presented on model development, and its parameterization based on measured tumor responses in lung cancer patients treated with targeted therapy. We further performed a comprehensive sensitivity analysis of model behavior in response to varying degrees of genomic instability, and derive estimates of initial pre-existing/persisting drug resistance that hold independent of parameter choice.