Department of Biomedical Engineering, Texas A&M University, Houston, United States.
Engineering Medicine Program, Texas A&M University, Houston, United States.
Elife. 2023 Apr 25;12:e82786. doi: 10.7554/eLife.82786.
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 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.
癌症治疗（包括免疫疗法）的失败仍然是预防持久缓解的主要障碍。这种失败通常是由于肿瘤的进化，包括基因型和表型，远离敏感的细胞状态。在这里，我们提出了一个数学框架来研究适应性免疫逃逸的动态，该框架跟踪了可用于免疫靶向的肿瘤相关抗原的数量。我们使用随机动态规划求解了最优的癌症逃逸策略，并证明与被动的、固定的策略相比，这种策略导致了更高的癌症逃逸率。我们的基础模型将癌症逃逸的可能性和时间动态与免疫微环境的特征联系起来，其中肿瘤的免疫原性反映了癌症适应和宿主识别之间的平衡。与被动策略相反，在不同的选择环境中导航的最佳适应性逃逸者会导致肿瘤抗原性的显著异质性，从而产生免疫热肿瘤和冷肿瘤。
