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

基本信息

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
项目类别：
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

项目摘要

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.

抽象的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) 改变了癌症治疗。不幸的是，ICB 疗法仅使 <20% 的患者受益，并且没有可靠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 反应需要。淋巴结中新 T 细胞的激活特定 T 细胞上的 T 细胞受体对某些特定的 T 细胞具有特异性。抗原表位，并且只有一小部分幼稚 T 细胞可以识别肿瘤抗原的激活。初始 T 细胞启动抗肿瘤反应需要与呈递的抗原进行物理相互作用细胞 (APC) 显示出由该特定 T 细胞识别的正确的、特定的同源抗原。淋巴系统促进 APC、初始 T 细胞和同源抗原的定位，该系统可以将APC和抗原集中在淋巴结中，但是由于抗原的丰富多样性。由于 T 细胞群的反应性，对任何单一抗原具有特异性的 T 细胞数量有限。这一事实，加上 T 细胞循环和许多淋巴结采样的随机性，表明 T 细胞的激活取决于随机过程，从逻辑上讲，更多的存在。抗原，或者更多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 的运输和相互作用可能会因 T 细胞和 APC 的存在而受到干扰。淋巴结中的转移性癌细胞，会损害抗癌免疫反应和所提出的工作引擎对 ICB 的反应如何影响淋巴结转移的产生。我们捕获了机械和生理破坏引起的抗癌免疫反应。转移瘤会干扰淋巴结中的淋巴液流动、抗原转运和 T 细胞运输，从而通过降低淋巴细胞活化的概率来直接影响抗肿瘤免疫。在这个项目中，我们将使用机械、多尺度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。由我们独特、最先进的动物模型支持，用于测量免疫细胞运输、淋巴一旦经过验证，计算模型将提供各种条件下的功能和肿瘤生长。选择额外治疗以增加淋巴细胞活化的机制框架我们组建了一支领先的计算建模团队，这个多学科团队由淋巴生物学家、免疫学家和癌症生物学家提供支持。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 R01 团队将共同获得对免疫检查点失效模式的重要见解。封锁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患有癌症。