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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.

摘要