Modeling Breast Cancer Proliferation, Drug Synergies, and Alternating Therapies

Abstract

Estrogen receptor positive (ER+) breast cancer is responsive to a number of targeted therapies used clinically. Unfortunately, the continuous application of targeted therapy often results in resistance. Mathematical modeling of the dynamics of cancer cell drug responses can help find better therapies that not only hold proliferation in check but also potentially stave off resistance. Toward this end, we developed a mathematical model that can simulate various mono, combination and alternating therapies for ER+ breast cancer cells at different doses over long time scales. The model is used to look for optimal drug combinations and predicts a significant synergism between Cdk4/6 inhibitors in combination with the anti-estrogen fulvestrant, which may help explain the clinical success of adding CDK4/6 inhibitors to anti-estrogen therapy. Lastly, the model is used to optimize an alternating treatment protocol that works as well as monotherapy while using less total drug dose.