Integrated Immune Engineering for Poor Prognosis Cancers

综合免疫工程治疗预后不良的癌症

基本信息

批准号：
10700921
负责人：
DAVID ANDREW LARGAESPADA
金额：
$ 186.91万
依托单位：
UNIVERSITY OF MINNESOTA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-16 至 2026-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10700921
关键词：
Acceleration Accounting Antitumor Response Apoptosis Area Benefits and Risks Biomedical Engineering Biophysics Blood CAR T cell therapy Cancer Control Cancer Patient Cancer Prognosis Cells Chemicals Clinical Clinical Trials Collaborations Complex Computers Cytoskeleton Development Diagnosis Economics Effectiveness Engineering Environment Explosion Failure Genetic Genetic Engineering Genetically Engineered Mouse Genome engineering Glioblastoma Growth Health Benefit Image Immune Immune response Immune system Immunocompetent Immunologics Immunologist Immunooncology Immunotherapeutic agent Immunotherapy In Vitro Liquid substance Malignant Neoplasms Malignant neoplasm of brain Malignant neoplasm of pancreas Measures Mechanics Microfabrication Modeling Oncology Pancreatic Ductal Adenocarcinoma Patients Phase Physics Plants Process Prognosis Proliferating Risk Signal Transduction Solid Neoplasm Stream System T-Lymphocyte Technology Testing Therapy trial Time Tissues Translations Tumor-infiltrating immune cells anti-tumor immune response biophysical model cancer cell cancer therapy cell motility clinical risk computational platform cost cytotoxic engineered T cells exhaustion high resolution imaging high risk immune cell infiltrate immunoengineering immunotherapy clinical trials improved in vivo live cell microscopy model development models and simulation nanoscale optical imaging optimism patient population pharmacologic physical science pre-clinical assessment pre-clinical research preclinical development programs response success tumor tumor immunology tumor progression

Acceleration Accounting Antitumor Response Apoptosis Area Benefits and Risks Biomedical Engineering Biophysics Blood CAR T cell therapy Cancer Control Cancer Patient Cancer Prognosis Cells Chemicals Clinical Clinical Trials Collaborations Complex Computers Cytoskeleton Development Diagnosis Economics Effectiveness Engineering Environment Explosion Failure Genetic Genetic Engineering Genetically Engineered Mouse Genome engineering Glioblastoma Growth Health Benefit Image Immune Immune response Immune system Immunocompetent Immunologics Immunologist Immunooncology Immunotherapeutic agent Immunotherapy In Vitro Liquid substance Malignant Neoplasms Malignant neoplasm of brain Malignant neoplasm of pancreas Measures Mechanics Microfabrication Modeling Oncology Pancreatic Ductal Adenocarcinoma Patients Phase Physics Plants Process Prognosis Proliferating Risk Signal Transduction Solid Neoplasm Stream System T-Lymphocyte Technology Testing Therapy trial Time Tissues Translations Tumor-infiltrating immune cells anti-tumor immune response biophysical model cancer cell cancer therapy cell motility clinical risk computational platform cost cytotoxic engineered T cells exhaustion high resolution imaging high risk immune cell infiltrate immunoengineering immunotherapy clinical trials improved in vivo live cell microscopy model development models and simulation nanoscale optical imaging optimism patient population pharmacologic physical science pre-clinical assessment pre-clinical research preclinical development programs response success tumor tumor immunology tumor progression

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项目摘要

ABSTRACT A key limitation to effective immunotherapy is the physical access of immune cells to the cancer cells. We propose to develop a multiscale tumor simulator to predict tumor dynamics based on immune and cancer cell migration and net proliferation as measured quantitatively from live cell microscopy. The tumor simulator will be developed by biomedical engineers working in close collaboration with immunologists and genetic engineers who are developing immunotherapies for pancreatic ductal adenocarcinoma and glioblastoma. The tumor simulator will be a computational platform that will help guide immunotherapy development and so will evolve to become an immunotherapy simulator. In addition, we will integrate state- of-the-art genome engineering and microenvironmental engineering to bring a full suite of engineering approaches to bear on the simulator development. Together, the simulator will be used to make quantitative, testable predictions that are then tested experimentally using pharmacological and genetic perturbations. By iterative model development we will test our central hypothesis that immune cell proximity is a major determinant of effective anti-tumoral immune response, and limiting to effective immunotherapy of solid tumors. Altogether, our Program Project will develop a comprehensive biophysics-based simulator to predict tumor progression and accelerate immunotherapy development.

抽象的有效免疫疗法的关键局限性是免疫细胞进入癌细胞的物理访问。我们建议开发多盘肿瘤模拟器，以预测基于免疫和癌症的肿瘤动力学细胞迁移和净增殖，如活细胞显微镜定量测量。肿瘤模拟器将由与免疫学家密切合作的生物医学工程师开发为胰腺导管腺癌和胶质母细胞瘤。肿瘤模拟器将是一个计算平台，将有助于指导免疫疗法发育，因此将发展成为免疫疗法模拟器。此外，我们将整合国家 - 艺术基因组工程和微环境工程，带来一套完整的工程采用模拟器开发的方法。一起，模拟器将用于制作可定量的可检验预测，然后使用药理学和遗传进行实验测试扰动。通过迭代模型开发，我们将测试免疫细胞接近性的中心假设是有效抗肿瘤免疫反应的主要决定因素，并限制了有效免疫疗法实体瘤。总之，我们的程序项目将开发一个全面的基于生物物理的模拟器预测肿瘤进展并加速免疫疗法的发展。