Abstract: Mathematical modeling of cancer growth and treatment
Mathematical models for cancer treatment have potential application to optimizing therapy, and can lead to a better understanding of how anti-cancer treatments work. In the last decade, the use of immunotherapies and targeted therapies has greatly increased, while more traditional therapies such as chemotherapy and radiation remain widely used clinically. This presentation will cover two specific areas relating to modeling anti-cancer therapy: (a) developing models for cellular response to drugs and drug combinations, and (b) modeling the killing of cancer cells by T cells, which are believed to be the primary immune cells involved in anti-cancer responses. Cellular response to drugs has been described by several mathematical formulations that in many cases do not describe experimental results well. Our peak damage and additive damage model formulations, which perform better, will be discussed, as will the elusive concept of “synergy,” transient drug resistance, and the challenge of modeling multiple exposures. On the topic of cytolytic T cells, while numerous mathematical models have rested on the assumption that the dominant mechanism of cell kill is by direct contact, here modeling that supports an alternative mechanism involving a diffusible factor will be presented. Overall, these models are expected to have application as components of larger-scale models for predicting treatment response, and for providing insight into clinically-used therapies.