Since the introduction of the first immune checkpoint inhibitor (ICI), immunotherapy has changed the landscape of molecular therapeutics for cancers. However, ICIs do not work equally well on all cancers and for all patients. There has been a growing interest in using mathematical and computational models to optimize clinical responses. Ordinary differential equations (ODEs) have been widely used for mechanistic modeling in immuno-oncology and immunotherapy. They allow rapid simulations of temporal changes in the cellular and molecular populations involved. Nonetheless, ODEs cannot describe the spatial structure in the tumor microenvironment or quantify the influence of spatially-dependent characteristics of tumor-immune dynamics. For these reasons, agent-based models (ABMs) have gained popularity because they can model more detailed phenotypic and spatial heterogeneity that better reflect the complexity seen in vivo. In the context of anti-PD-1 ICIs, we compare treatment outcomes simulated from an ODE model and an ABM to show the importance of including spatial components in computational models of cancer immunotherapy. We consider tumor cells of high and low antigenicity and two distinct cytotoxic T lymphocyte (CTL) killing mechanisms. The preferred mechanism differs based on the antigenicity of tumor cells. Our ABM reveals varied phenotypic shifts within the tumor and spatial organization of tumor and CTLs despite similarities in key immune parameters, initial simulation conditions, and early temporal trajectories of the cell populations.
自首个免疫检查点抑制剂问世以来，免疫疗法已彻底改变了癌症分子治疗的格局。然而，免疫检查点抑制剂并非对所有癌症类型及所有患者均具有同等疗效。利用数学与计算模型优化临床反应的研究日益受到关注。在免疫肿瘤学及免疫治疗领域，常微分方程已被广泛应用于机制建模，能够快速模拟相关细胞与分子群体的时序变化。但常微分方程无法描述肿瘤微环境的空间结构，亦难以量化空间依赖性特征对肿瘤-免疫动力学的影响。因此，基于智能体的模型因其能模拟更详尽的表型与空间异质性，更精准地反映体内真实复杂情况而备受青睐。本研究以抗PD-1免疫检查点抑制剂为例，通过对比常微分方程模型与基于智能体模型的治疗模拟结果，揭示了空间要素在癌症免疫治疗计算模型中的重要性。我们考察了高抗原性与低抗原性肿瘤细胞，以及两种不同的细胞毒性T淋巴细胞杀伤机制。肿瘤细胞的抗原性差异决定了优势杀伤机制的选择。研究显示，尽管关键免疫参数、初始模拟条件及细胞群体早期时序轨迹相似，基于智能体模型仍能揭示肿瘤内部的表型异变以及肿瘤与细胞毒性T淋巴细胞的空间组织结构差异。
肿瘤(癌症)患者之家