Speaker
Description
The current cancer treatment paradigm of administering a ‘maximum tolerated dose’ to patients fails to account for the eco-evolutionary dynamics that drive disease relapse. Adaptive therapy has shown clinically encouraging results in controlling tumors by modeling patient-specific evolutionary dynamics, based on the premise that resistance comes at a cost and that sensitive cells can suppress resistant cell growth through competition. In this study, we have constructed agent-based and ordinary differential equation models that recapitulate in vitro experiments of doxorubicin-sensitive and resistant MCF7 cell lines. We show the cost of resistance depends on the microenvironmental pH that can be manipulated via acid-normalizing agents (such as buffer therapy). We find that drug-resistant cells are more dependent on anaerobic glycolysis and consequently produce more acid, acting as a 'public bad' that inhibits the growth of the drug-sensitive population more than the drug-resistant subpopulation. Model simulations showed the impact of buffer therapy on the cost of resistance and on cell competition dynamics, allowing us to infer optimal treatment schedules of buffer combined with chemotherapy that rescue the drug-sensitive population. Overall, this study showed the importance of treating both the tumor and its microenvironment to develop model-informed evolutionary therapies, extending the traditional modeling paradigm of adaptive therapy beyond pure cell competition.
