免疫检查点阻断通过扩增效应 CD8⁺ T 细胞克隆增强淋巴细胞清除性化疗诱导的抗肿瘤免疫
Immune Checkpoint Blockade Augments Lymphodepleting Chemotherapy-Induced Antitumor Immunity by Expanding Effector CD8+ T-cell Clones.
CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
英文原题:Agent-based Modeling of Tumor and Immune System Interactions in Combinational Therapy with Low-dose 5-fluorouracil and Dendritic Cell Vaccine in Melanoma B16F10.
Agent-based Modeling of Tumor and Immune System Interactions in Combinational Therapy with Low-dose 5-fluorouracil and Dendritic Cell Vaccine in Melanoma B16F10.
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本研究旨在提出一个基于个体的模型(ABM),用于模拟黑色素瘤模型中肿瘤细胞与免疫系统之间的相互作用。该模型分别将髓源性抑制细胞(MDSCs)和树突状细胞(DCs)视为免疫抑制因子和抗原呈递因子。动物实验在68只B16F10黑色素瘤荷瘤C57BL/6雌性小鼠上进行,以收集用于ABM实施和验证的动态数据。动物分为4组;第1组为对照组(不治疗),第2组和第3组分别接受DC疫苗和低剂量5-氟尿嘧啶(5-FU)治疗,第4组接受DC疫苗和低剂量5-FU联合治疗。评估每组的肿瘤生长速率、MDSC数量以及肿瘤微环境中CD8+/CD107a+ T细胞的存在情况。首先,将肿瘤细胞、效应免疫细胞、DCs和MDSCs作为ABM模型的个体,其相互作用方式从文献中提取并在模型中实现。然后,利用动物实验收集的动态数据估计模型参数。为验证ABM模型,将模拟结果与真实数据进行比较。
结果表明,模型个体的动力学能够模拟所考虑的免疫系统组分之间的关系,并产生与真实数据相符的涌现结果。所提出模型的简洁性有助于理解联合治疗的结果,并使该模型成为研究不同情景和评估联合结果的有用工具。确定每个组分的作用有助于发现肿瘤进展过程中的关键时间点,并改变肿瘤与免疫系统的平衡,使其有利于免疫系统。
This study is designed to present an agent-based model (ABM) to simulate the interactions between tumor cells and the immune system in the melanoma model. The Myeloid-derived Suppressor Cells (MDSCs) and dendritic cells (DCs) are considered in this model as immunosuppressive and antigen-presenting agents respectively. The animal experiment was performed on 68 B16F10 melanoma tumor-bearing C57BL/6 female mice to collect dynamic data for ABM implementation and validation. Animals were divided into 4 groups; group 1 was control (no treatment) while groups 2 and 3 were treated with DC vaccine and low-dose 5- fluorouracil (5-FU) respectively and group 4 was treated with both DC Vaccine and low-dose of 5-FU. The tumor growth rate, number of MDSC, and presence of CD8+/CD107a+ T cells in the tumor microenvironment were evaluated in each group.
Firstly, the tumor cells, the effector immune cells, DCs, and the MDSCs have been considered as the agents of the ABM model and their interaction methods have been extracted from the literature and implemented in the model. Then, the model parameters were estimated by the dynamic data collected from animal experiments. To validate the ABM model, the simulation results were compared with the real data.
The results show that the dynamics of the model agents can mimic the relations among considered immune system components to an emergent outcome compatible with real data. The simplicity of the proposed model can help to understand the results of the combinational therapy and make this model a useful tool for studying different scenarios and assessing the combinational results. Determining the role of each component helps to find critical times during tumor progression and change the tumor and immune system balance in favor of the immune system.
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