Abstract A104: A stochastic model of tumor-immune evasion predicts sustained coevolution and tumor antigen downregulation Academic Article uri icon

abstract

  • Abstract The dynamical interaction between a growing cancer population and the adaptive immune system generates diverse evolutionary trajectories that ultimately result in tumor clearance or immune escape. Here we create a simple mathematical model of T-cell recognition of tumor-associated antigens. We show that declines in T-cell turnover declines explain differences in early incidence data for almost all cancer types. Fitting the model to evolutionary data argues in favor of substantial and sustained immune pressure exerted on a developing tumor, suggesting that measured incidence is a small proportion of all cancer initiation events. Extending our model to account for the number of distinct tumor antigens, we solve for the optimal cancer evasion strategy and demonstrate that such a population-level strategy predicts that the accumulation rate of cancer mutations is a reflection of the tumor microenvironment and determined by the balance of cancer evasion costs and host recognition rate. We apply our framework to combination drug therapy and quantify the difference in predicted efficacy of combination therapy vs. sequential monotherapy. More generally, this framework has significant implications for understanding drug resistance in the face of an adaptive threat. Citation Format: Jason T. George, Herbert Levine. A stochastic model of tumor-immune evasion predicts sustained coevolution and tumor antigen downregulation [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2019 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2020;8(3 Suppl):Abstract nr A104.

published proceedings

  • Cancer Immunology Research

author list (cited authors)

  • George, J. T., & Levine, H.

citation count

  • 0

publication date

  • March 2020