Abstract

In the first part of the talk, we focus on the conceptual development of alternative treatment strategies that leverage the principles of evolution to mitigate treatment resistance. We introduce this broad class of drug scheduling strategies known as evolutionary therapies and explain how mathematical modeling can aid by providing patient-specific predictions as a decision-support tool for providing clinical insight. Next, we explore the practical implementation of an evolutionary therapy steering strategy within an in vivo model of non-small-cell lung cancer treated with ALK inhibitors. Treatment-naïve tumors are associated with more convex exposure-response curves (low doses provide sufficient responses) while evolved-resistance tumors are generally more concave (requiring high doses for equivalent responses). Resistance to ALK inhibitors in vivo occurs gradually, as tumors acquire cooperating genetic and epigenetic adaptive changes. Thus, we hypothesized the existence of a critical point in the time-evolution of ALK -positive tumors where it is optimal to switch from continuous treatment to high-dose / low-dose to mitigate the onset of gradual resistance. In vivo validation provides evidence that evolutionary steering is a viable strategy for predicting the onset of resistance and developing resistance management treatment strategies. Thus far, we neglect the fact that cancer growth can be described as a caricature of the renewal process of the tissue of origin, where the tissue architecture and spatial correlations have a strong influence on the evolutionary dynamics within a tumor. To incorporate these characteristics, we introduce agent-based modeling methods that integrate clinical spatial data to make inferences on the role of microenvironmental mechanism of immune escape, and define implications on therapy.