Department of Bioengineering, University of Louisville, Lutz Hall 419, Louisville, KY, 40292, USA.
Institute for Experimental Cancer Research, Christian-Albrechts-University Kiel (CAU), Kiel, Germany.
Cancer Immunol Immunother. 2021 May;70(5):1475-1488. doi: 10.1007/s00262-020-02785-4. Epub 2020 Nov 12.
The dynamic interactions between macrophages and T-lymphocytes in the tumor microenvironment exert both antagonistic and synergistic functions affecting tumor growth. Extensive experimental effort has been expended to investigate immunotherapeutic strategies targeting macrophage polarization as well as T-cell activation with the goal to promote tumor cell killing and cancer elimination. However, these interactions remain poorly understood, and cancer immunotherapeutic strategies are often disappointing. The complex system encompassing innate and adaptive immune cell activity in response to tumor growth could benefit from a systems perspective built upon mathematical modeling. This study develops a modeling system to help evaluate the effects of macrophage and T-lymphocyte interactions on tumor growth. The system enables simulating the combined cytotoxic and tumor-promoting interactions of these two immune cell populations in a vascularized organ microenvironment, such as in liver metastases. A hypothetical immunotherapeutic strategy is simulated to increase the number of tumor-suppressive (M1-phenotype) vs. tumor-promoting (M2-phenotype) macrophages to gauge their effects on CD8 T-cells and CD4 T-helper cells, which in turn affect the macrophage functions. The results highlight the dynamic interactions between macrophages and T-lymphocytes in the tumor microenvironment and show that with the chosen set of parameter values, the overall cytotoxic effect from macrophages and T-lymphocytes obtained by driving the M1:M2 ratio higher could saturate and fail to achieve tumor regression. Further expansion of this modeling platform to include additional tumor-immune cell interactions, coupled with parameters representing particular tumor characteristics, could enable systematic evaluation of immunotherapeutic strategies tailored to patient-tumor specific conditions, including metastatic disease.
肿瘤微环境中巨噬细胞和 T 淋巴细胞之间的动态相互作用具有拮抗和协同作用，影响肿瘤的生长。人们已经付出了大量的实验努力来研究针对巨噬细胞极化和 T 细胞激活的免疫治疗策略，以期促进肿瘤细胞杀伤和癌症消除。然而，这些相互作用仍然知之甚少，癌症免疫治疗策略往往令人失望。包含固有和适应性免疫细胞对肿瘤生长反应的复杂系统可以从基于数学建模的系统角度中受益。本研究开发了一个建模系统，以帮助评估巨噬细胞和 T 淋巴细胞相互作用对肿瘤生长的影响。该系统能够模拟这两种免疫细胞群体在血管化器官微环境（如肝转移）中的联合细胞毒性和促进肿瘤生长的相互作用。模拟了一种假设的免疫治疗策略，以增加肿瘤抑制性（M1 表型）与促进肿瘤生长（M2 表型）的巨噬细胞数量，以评估它们对 CD8 T 细胞和 CD4 辅助性 T 细胞的影响，而这些细胞反过来又影响巨噬细胞的功能。结果突出了肿瘤微环境中巨噬细胞和 T 淋巴细胞之间的动态相互作用，并表明在选择的参数值下，通过提高 M1:M2 比值获得的巨噬细胞和 T 淋巴细胞的总体细胞毒性效应可能饱和并未能实现肿瘤消退。进一步扩展这个建模平台，纳入更多的肿瘤免疫细胞相互作用，并结合代表特定肿瘤特征的参数，可以系统地评估针对患者-肿瘤特定条件的免疫治疗策略，包括转移性疾病。
