The tumor ecosystem in cancer progression and immunotherapeutic response

癌症进展和免疫治疗反应中的肿瘤生态系统

基本信息

批准号：
9980809
负责人：
Alexander Y Rudensky
金额：
$ 63.87万
依托单位：
SLOAN-KETTERING INST CAN RESEARCH
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-08-26 至 2022-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9980809
关键词：
Adaptive Immune System Antigen-Presenting Cells Biological Biopsy Blood CD8-Positive T-Lymphocytes CTLA4 blockade Cancer Model Cancer Patient Cell Communication Cell model Cells Client Clinical Communication Computer Models Data Data Set Dendritic Cells Ecology Ecosystem Endothelial Cells Epithelial Cells Experimental Models Fibroblasts Gene Expression Gene Expression Profiling Goals Hepatocyte Human Immune Immunology Immunotherapeutic agent In Vitro Lymphoid Cell Malignant Neoplasms Malignant neoplasm of lung Mediator of activation protein Memorial Sloan-Kettering Cancer Center Modality Modeling Modeling of Functional Interactions Molecular Mus Myeloid Cells NCI Center for Cancer Research Natural Killer Cells Organ Organism Outcome PD-1 blockade Patients Physiological Population Regulatory Element Regulatory T-Lymphocyte Role Skin Cancer Smooth Muscle Myocytes Stress Structure System Systems Analysis Therapeutic Tissues Tumor Immunity Validation alveolar epithelium base cancer immunotherapeutics cell type eosinophil high throughput analysis in silico in vitro Model macrophage mathematical model melanocyte melanoma mimetics mouse model neoplastic cell neutrophil novel predictive modeling protein expression protein metabolite response tumor tumor microenvironment tumor progression

项目摘要

Project II. The tumor ecosystem in cancer progression and immunotherapeutic response PROJECT SUMMARY The goal of this project is to identify the ecological interactions between cancer and immune cells that govern cancer dynamics and response to therapy. We start from the idea that a tumor can be considered an ecosystem or organ, where multiple accessory cell types are interconnected and communicate with each other and with tumor cells, which serve as their clients. Through systems analysis and modeling of functional interactions in the tumor ecosystem on different scales including cellular, protein, and gene expression dynamics at a population and single cell levels this proposal will seek to identify key cellular and molecular regulatory elements in the tumor microenvironment and potential means of their manipulation for therapeutic benefit. We will explore features of, and relationships between, multiple accessory cell types and tumor cells, in experimental models of skin and lung cancer in mice and in human cancer patients (Aim 1). The accessory cells include myeloid cells, dendritic cells, innate lymphoid cells (ILC), neutrophils, eosinophils, endothelial cells, fibroblasts, and regulatory T (Treg) cells. We will also investigate features of mediators of anti-tumor immunity including NK cells, CD4 and CD8 T cells and their specialized subsets. We will use perturbation of the tumor ecology impacting its progression in mice and human patients by established and novel immunotherapeutic modalities including PD1 and CTLA4 blockade and Treg cell depletion. The impact of these perturbations will be assessed through comprehensive analysis of cellular dynamics and states in relation to biological and clinical outcomes to generate predictive models from the data (Aim 2). We will then validate key interaction components in the tumor ecosystem by modeling cell-cell interactions in vitro using tissue mimetic systems and in silico using agent-based models (Aim 3). 1

项目II。癌症进展和免疫治疗反应中的肿瘤生态系统项目摘要该项目的目的是确定癌症与免疫细胞之间的生态相互作用癌症动力和对治疗的反应。我们从可以将肿瘤视为的想法开始生态系统或器官，其中多种附件类型相互联系并相互通信并与肿瘤细胞一起充当客户。通过系统分析和功能建模肿瘤生态系统的相互作用在不同尺度上的相互作用，包括细胞，蛋白质和基因表达人群和单细胞水平的动力学该提案将寻求识别关键细胞和分子肿瘤微环境中的调节元素及其操纵的潜在手段好处。我们将探索多种附属细胞类型和肿瘤细胞之间的特征以及之间的关系小鼠和人类癌症患者皮肤和肺癌的实验模型（AIM 1）。配件细胞包括髓样细胞，树突状细胞，先天淋巴样细胞（ILC），中性粒细胞，嗜酸性粒细胞，内皮细胞，成纤维细胞和调节t（Treg）细胞。我们还将调查抗肿瘤介体的特征包括NK细胞，CD4和CD8 T细胞及其专门子集的免疫力。我们将使用肿瘤生态学通过既定而新颖免疫治疗方式，包括PD1和CTLA4阻断和Treg细胞耗竭。的影响这些扰动将通过对细胞动力学和状态的全面分析来评估与生物学和临床结果的关系，以从数据中产生预测模型（AIM 2）。然后我们会通过使用使用细胞细胞相互作用在体外对肿瘤生态系统中的关键相互作用组件进行验证组织模拟系统以及使用基于代理的模型在硅中（AIM 3）。 1