Optimal Cancer Evasion in a Dynamic Immune Microenvironment

Abstract

The failure of cancer treatments, including immunotherapy, continues to be a major obstacle in preventing durable remission. This failure often results from tumor evolution, both genotypic and phenotypic, away from sensitive cell states. Here, we propose a mathematical framework for studying the dynamics of adaptive immune evasion that tracks the number of tumor-associated antigens available for immune targeting. We solve for the unique optimal cancer evasion strategy using stochastic dynamic programming and demonstrate that this policy results in increased cancer evasion rates when compared to a passive, fixed strategy. Our foundational model relates the likelihood and temporal dynamics of cancer evasion to features of the immune microenvironment, where tumor immunogenicity reflects a balance between cancer adaptation and host recognition. In contrast with a passive strategy, optimally adaptive evaders navigating varying selective environments result in substantially heterogeneous post-escape tumor antigenicity, giving rise to immunogenically hot and cold tumors.

Optimal Cellular Phenotypic Adaptation in Fluctuating Environments

“…In these more complicated cases, the number of stable phenotypic states, along with their corresponding fitness in fluctuating landscapes, are important to evaluate. Such scenarios may utilize similar modeling to characterize optimal decision-making [30] and will benefit from data-driven model extensions to define the phenotypic response to more complex environmental signatures. At present our model does not account for durable alteration mechanisms, such as genetic mutation, that may also contribute to improved adaptation over time.…”