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

癌症免疫治疗中抗原和 T 细胞运输的系统生物学

• 批准号: 10751192
• 负责人: LANCE L MUNN
• 金额: $ 50.5万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10751192
• 关键词: Affect; Anatomy; Animal Experiments; Animal Model; Antibodies; Antigen-Presenting Cells; Antigens; Antitumor Response; Biology; Biometry; Blood Circulation; Breast Cancer Model; Cancer Etiology; Cells; Circulation; Clinical; Complement; Computer Models; Data; Data Set; Disseminated Malignant Neoplasm; Drug Kinetics; Effectiveness; Epitopes; Excision; Failure; Generations; Immune; Immune Evasion; Immune response; Immunologist; Immunosuppression; Immunotherapy; Impairment; Lymphatic; Lymphatic System; Lymphatic function; Lymphocyte; Lymphocyte Activation; Malignant Neoplasms; Measurement; Measures; Mechanics; Metastatic Neoplasm to Lymph Nodes; Modeling; Mus; Nature; Neoplasm Metastasis; Non-Small-Cell Lung Carcinoma; Patients; Pattern; Physiological; Population; Probability; Process; Prognosis; Sampling; Stochastic Processes; Systems Biology; T memory cell; T-Cell Activation; T-Cell Receptor; T-Lymphocyte; Testing; Tumor Antigens; Tumor Immunity; Work; anti-CTLA-4 therapy; anti-PD-1; anti-PD1 therapy; anti-cancer; anti-tumor immune response; antigen-specific T cells; cancer cell; cancer immunotherapy; cancer therapy; chemokine; cohort; draining lymph node; experimental study; fluid flow; immune activation; immune checkpoint; immune checkpoint blockade; immune checkpoint blockers; immune function; immunoreaction; immunoregulation; improved; improved outcome; in vivo; individual patient; insight; lymph flow; lymph nodes; mathematical model; melanoma; mouse model; multi-scale modeling; multidisciplinary; pharmacodynamic model; predicting response; predictive marker; prevent; residence; response; stem; trafficking; tumor; tumor growth

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.

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，免疫检查点阻断剂（ICB）已经改变了癌症治疗。 不幸的是，ICB治疗仅使<20%的患者受益，并且没有强有力的生物标志物用于 预测任何个体患者的反应。最近的数据证实，有效的国际竞争力对策需要 淋巴结中新T细胞的活化。给定T细胞上的T细胞受体对某些特定的 抗原表位，并且只有一小部分初始T细胞可以识别肿瘤抗原。激活 初始T细胞启动抗肿瘤应答需要与抗原呈递的物理相互作用 细胞（APC）展示由该特异性T细胞识别的正确的特异性同源抗原。该公司- APC、初始T细胞和同源抗原的定位由淋巴系统促进， 能使APC和抗原在淋巴结内聚集。然而，由于抗原的丰富多样性， 由于T细胞群体的反应性，对任何单一抗原具有特异性的T细胞的数量有限。 这一事实，沿着T细胞循环的随机性和许多淋巴结的取样， 表明T细胞活化依赖于随机过程。从逻辑上讲，更多的存在 抗原，或抗原在更多的淋巴结应该增加的概率T细胞活化。 此外，幼稚T细胞和APC的运输和相互作用可以被 转移性癌细胞在淋巴结中的转移，这会损害抗癌免疫反应， 对ICB的回应拟议的工作分析了淋巴结转移如何损害产生的 抗癌免疫反应。我们假设机械和生理上的破坏 通过转移可以干扰淋巴结中的淋巴流动、抗原转运和T细胞运输， 因此通过降低淋巴细胞活化的可能性直接影响抗肿瘤免疫。 在这个项目中，我们将使用机械的，多尺度的计算模型来解决这个假设， 确定未能启动抗肿瘤免疫的具体原因。我们的计算模型将是 在我们独特的、最先进的动物模型的支持下， 功能和肿瘤生长的各种条件。一旦验证，计算模型将提供 选择额外治疗以增加淋巴细胞活化的机制框架， 免疫治疗的反应率我们召集了一个由顶尖的计算模型专家组成的团队， 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以下方面的支持： 生物统计学、淋巴结计算模型和淋巴结测量方面的专家合作者 体内流动。R 01团队将共同获得对免疫检查点失败模式的重要见解 阻断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