Abstract 2740: Stochastic modeling uncovers the interplay between early cancer progression and adaptive immune-specific cancer escape

Abstract

Abstract The lack of durable therapeutic interventions continues to limit cancer treatment efficacy. While breakthroughs in immunotherapy have improved patient survival for some disease subtypes, immune escape is an unfortunate and common hallmark of cancer evasion. Understanding the dynamic interplay between an evolving and heterogeneous malignant population and the adaptive immune system, consisting of millions of unique T cells, is therefore required for designing optimized treatment strategies. We begin by describing the implications of cancer escape via durable evasion events, such as major histocompatibility antigen presentation down-regulation. Durable escape is then applied to relate observed cancer age-specific incidence to physiologic impairments in T cell turnover and diversity. We then discuss a more generalized stochastic process that enables transient escape and recurrent recognition via distinct T cell subclones. This co-evolutionary model predicts repeated immune recognition and cancer evasion prior to disease manifestation. Using this model, we find that T cell turnover and cancer mutation rates explain differences in early age incidence data across nearly all cancer types. Lastly, we introduce a modeling approach for understanding the extent of optimality occurring in cancer evasion via tumor-associated antigen downregulation, emphasizing the utility of foundational modeling frameworks for optimizing cancer T cell immunotherapy. Citation Format: Jason T. George. Stochastic modeling uncovers the interplay between early cancer progression and adaptive immune-specific cancer escape [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2740.