Department of Biomedical Engineering, University of Southern California, Los Angeles, California, United States of America.
Department of Quantitative and Biological Sciences, University of Southern California, Los Angeles, California, United States of America.
PLoS Comput Biol. 2020 Dec 23;16(12):e1008519. doi: 10.1371/journal.pcbi.1008519. eCollection 2020 Dec.
Within the tumor microenvironment, macrophages exist in an immunosuppressive state, preventing T cells from eliminating the tumor. Due to this, research is focusing on immunotherapies that specifically target macrophages in order to reduce their immunosuppressive capabilities and promote T cell function. In this study, we develop an agent-based model consisting of the interactions between macrophages, T cells, and tumor cells to determine how the immune response changes due to three macrophage-based immunotherapeutic strategies: macrophage depletion, recruitment inhibition, and macrophage reeducation. We find that reeducation, which converts the macrophages into an immune-promoting phenotype, is the most effective strategy and that the macrophage recruitment rate and tumor proliferation rate (tumor-specific properties) have large impacts on therapy efficacy. We also employ a novel method of using a neural network to reduce the computational complexity of an intracellular signaling mechanistic model.
在肿瘤微环境中，巨噬细胞处于免疫抑制状态，阻止 T 细胞消除肿瘤。因此，研究人员专注于免疫疗法，专门针对巨噬细胞，以降低其免疫抑制能力并促进 T 细胞功能。在这项研究中，我们开发了一个基于代理的模型，其中包括巨噬细胞、T 细胞和肿瘤细胞之间的相互作用，以确定由于三种基于巨噬细胞的免疫治疗策略（巨噬细胞耗竭、募集抑制和巨噬细胞再教育），免疫反应如何变化。我们发现，再教育将巨噬细胞转化为促进免疫的表型是最有效的策略，并且巨噬细胞募集率和肿瘤增殖率（肿瘤特异性特性）对治疗效果有很大影响。我们还采用了一种使用神经网络的新方法来降低细胞内信号转导机制模型的计算复杂性。
