"Using mathematical modeling to design protocols for preclinical testing of evolutionary therapies"
Over the past two decades it has become clear that cancers are complex and evolving diseases. Genetic and non-genetic processes generate diverse subpopulations of tumor cells which can thrive under a variety of conditions and stressors. This provides a rich pool of variation that by means of natural selection and continued evolution enables adaptation to even the most modern treatments, especially in advanced cancers. Based on this novel understanding, so-called “evolutionary therapy” or “evolution-informed treatment strategies” have emerged which try to leverage, and potentially even steer, tumour evolution by strategically and dynamically sequencing and combining existing therapies and adjusting dose levels. In particular, adaptive therapy, which dynamically changes treatment levels to maintain drug-sensitive cells in order to competitively suppress emerging drug resistance, has produced a number of promising theoretical, preclinical and also clinical results. However, unlike for new drugs for which there are clear established frameworks for translation from bench to bedside, the design of preclinical protocols to ensure efficacy and safety of evolutionary therapies is an open question.
In this study, we use mathematical modeling to develop and interpret in vitro experimental protocols and apply them towards the development of an adaptive therapy for Osimertinib for the treatment of Non-Small Cell Lung Cancer. In the first step, we consider the question of how to measure ecological interactions between tumor subpopulations. To do so, we build on the “Game Assay” previously developed by our group, in which cells are co-cultured at different frequencies to measure how a population’s fitness depends on its frequency in the environment. Using an agent-based model of our in vitro experiments, we study how different aspects of the design (number of replicates, number of proportions) and analysis (regression technique, regression window) impact the accuracy and precision of the assay. Subsequently, we use our optimized protocol to quantify the frequency-dependent interactions between Osimertinib sensitive and resistant PC9 cells under different drug levels. In the next step, we use our model to explore whether and how so-obtained fitness differences translate to the ability to steer the composition of the tumor in long-term in vitro experiments, in which cells are co-cultured and passaged at regular intervals. In particular, we explore the role of the population size, passaging frequency, and passage fraction (proportion of cells carried forward to next passage). To conclude, we will present preliminary data in which we use this assay to trial a potential adaptive Osimertinib therapy protocol in vitro. Overall, we demonstrate how mathematical models can help to understand and improve experimental assays, and we contribute towards the important discussion as to how to translate evolutionary therapies from the blackboard to the bedside.
Additional authors: Dagim Tadele; Rowan Barker-Clarke; Mina Dinh; Jeffrey Maltas; Jacob Scott