Systems Biology of Antigen and T-Cell Transport in Cancer Immunotherapy

摘要 癌细胞经常利用免疫检查点分子来抑制和逃避免疫反应； 通过抑制这一过程，免疫检查点阻滞剂(ICBS)改变了癌症治疗。 不幸的是，ICB治疗只使20%的患者受益，而且没有强有力的生物标志物。 预测任何个体患者的反应。最近的数据证实，有效的ICB应对措施需要 淋巴组织中新T细胞的激活。特定T细胞上的T细胞受体是特定的 抗原表位，只有一小部分幼稚T细胞可以识别肿瘤抗原。激活 初始T细胞启动抗肿瘤反应需要与抗原提呈物进行物理相互作用 显示特定T细胞所识别的正确的、特定的同源抗原的细胞(APC)。联席- 淋巴系统促进APC、幼稚T细胞和同源抗原的定位，淋巴系统 可在淋巴结内浓缩APC和抗原。然而，由于抗原的丰富多样性， 由于T细胞群的反应性，对任何单一抗原都有有限数量的T细胞。 这一事实，加上T细胞循环的随机性和对许多淋巴结的采样， 提示T细胞的激活依赖于随机过程。从逻辑上讲，存在更多 抗原，或更多的淋巴结中的抗原应该会增加T细胞激活的可能性。 此外，幼稚T细胞和APC的贩运和相互作用可能会被 在淋巴结中转移的癌细胞，这会损害抗癌免疫反应和 对ICB的回应。这项拟议的工作分析了淋巴转移如何损害肿瘤的生成。 抗癌免疫反应。我们假设机械和生理上的干扰会导致 通过转移可以干扰淋巴流动，抗原运输和T细胞在淋巴结内的运输， 并因此通过降低淋巴细胞活化的几率直接影响抗肿瘤免疫。 在这个项目中，我们将使用机械的、多尺度的计算建模来解决这一假设 找出未能启动抗肿瘤免疫的具体原因。我们的计算模型将是 由我们独特的、最先进的动物模型支持，以测量免疫细胞交易、淋巴 功能和肿瘤在各种条件下的生长。一旦得到验证，计算模型将提供 一种机械框架，用于选择额外的治疗方法来提高淋巴细胞的活性，从而 免疫治疗的应答率。我们已经组建了一支由领先的计算建模人员组成的团队， 淋巴生物学家、免疫学家和癌症生物学家。这个多学科团队得到了 生物统计学、淋巴结计算模型和淋巴测量方面的专家合作者 在体内流动。R01团队将共同获得对免疫检查点故障模式的重要见解 阻止并开发可测试的解决方案，为更多患者释放抗肿瘤免疫反应 得了癌症。

ABSTRACT Cancer cells often exploit immune checkpoint molecules to suppress and evade immune responses; by inhibiting this process, immune checkpoint blockers (ICBs) have transformed cancer treatment. Unfortunately, ICB therapy only benefits <20% of patients, and there are no robust biomarkers for predicting response in any individual patient. Recent data confirm that effective ICB responses require activation of new T cells in lymph nodes. The T cell receptors on a given T cell are specific for certain antigen epitopes, and only a small subset of naive T cells can recognize tumor antigens. Activation of naïve T cells to initiate the anti-tumor response requires physical interaction with an antigen presenting cell (APC) displaying the correct, specific cognate antigen recognized by that specific T cell. The co- localization of the APC, naïve T-cell and cognate antigen is facilitated by the lymphatic system, which can concentrate APCs and antigen in lymph nodes. However, due to the rich diversity of antigen reactivity of the T cell population, there are limited numbers of T cells specific for any single antigen. This fact, along with the random nature of T cell circulation and sampling of the many lymph nodes, suggests that T cell activation depends on stochastic processes. Logically, the presence of more antigen, or antigen in more lymph nodes should increase the probability of T-cell activation. Furthermore, the trafficking and interaction of naïve T-cells and APCs can be disrupted by the presence of metastatic cancer cells in the lymph node, which impairs anti-cancer immune responses and the response to ICB. The proposed work analyzes how lymph node metastases impair the generation of anti-cancer immune responses. We hypothesize that mechanical and physiological disruptions caused by metastases can interfere with lymph flow, antigen transport and T cell trafficking in the lymph node, and therefore directly affect anti-tumor immunity by reducing the probability of lymphocyte activation. In this project, we will address this hypothesis using mechanistic, multiscale computational modeling to identify specific reasons for the failure to initiate anti-tumor immunity. Our computational model will be supported by our unique, state-of-the-art animal models to measure immune cell trafficking, lymphatic function and tumor growth in various conditions. Once validated, the computational model will provide a mechanistic framework for selecting additional treatments to increase lymphocyte activation and thus the response rate to immunotherapy. We have assembled a team of leading computational modelers, lymphatic biologists, immunologists and cancer biologists. This multidisciplinary team is supported by expert collaborators in biostatistics, computational models of lymph nodes and measurements of lymph flow in vivo. Together, the R01 team will gain critical insights into modes of failure of immune checkpoint blockade and develop testable solutions to unleash anti-tumor immune responses for more patients with cancer.