Modeling Resistance and Recurrence Patterns of Combined Targeted-Chemoradiotherapy Predicts Benefit of Shorter Induction Period

Abstract

The optimal integration of molecularly targeted therapies with concurrent chemotherapy and radiation (CRT) to improve cures in genotype-defined cancers is unknown. Here, we develop a bio-mathematical framework to simulate patterns of local versus distant recurrences in a heterogeneous lung cancer population receiving combined targeted therapy and CRT. Our validated model predicts that targeted induction before CRT, an approach currently being tested in clinical trials, may render adjuvant targeted therapy less effective due to proliferation of drug-resistant cancer cells when using very long induction periods. Furthermore, our simulations not only demonstrate the competing effects of drug-resistant cell expansion versus overall tumor regression as a function of induction length, but can also directly estimate the probability of observing an improvement in progression-free survival at a given cohort size. We thus demonstrate that such stochastic biological simulations have the potential to quantitatively inform the design of multimodality clinical trials in genotype-defined cancers.