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Systems analysis of cell-cell communication networks and immune activity in the melanoma tumor microenvironment

黑色素瘤肿瘤微环境中细胞间通讯网络和免疫活性的系统分析

基本信息

批准号：
10171565
负责人：
MARCUS W BOSENBERG
金额：
$ 68.89万
依托单位：
YALE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10171565
关键词：
Address Area Biology CTLA4 blockade Cancer Patient Cell Communication Cell model Cell secretion Cells Classification Clinical Communication Complex Computer Analysis Computer Models Computing Methodologies Data Fibroblasts Goals Human Immune Immune checkpoint inhibitor Immune response Immune system Immunologics Immunotherapy Laboratory Study Ligands Lymphocyte Malignant Neoplasms Maps Measurement Mediating Mission Modeling Mus Myeloid Cells Pathology Patients Pharmacology Population Property Public Health Publishing Receptor Cell Research Resistance Sampling Signal Transduction Stromal Neoplasm Systems Analysis Systems Biology T cell response T-Lymphocyte Testing Therapeutic Time Translating Tumor-infiltrating immune cells adaptive immunity anti-cancer anti-tumor immune response base cancer cell cancer immunotherapy cancer regression cancer therapy cell type checkpoint therapy chemokine combinatorial cytokine design functional plasticity improved in vivo innovation intercellular communication macrophage melanoma monocyte mouse model neoplastic cell next generation novel therapeutic intervention patient subsets predictive modeling programmed cell death protein 1 receptor receptor expression response single cell analysis single-cell RNA sequencing support network therapeutic target translational impact tumor tumor heterogeneity tumor microenvironment tumor progression tumor-immune system interactions

项目摘要

PROJECT SUMMARY Understanding the dynamic cell-cell communication networks that establish immunological activity in the tumor microenvironment (TME) would transform therapeutic strategies to help cancer patients that are unresponsive to checkpoint inhibitors (CPIs). To this end, the overall objective of this proposal is to determine networks of intercellular secreted signals that distinguish ineffective tumor-immune responses from effective ones in order to identify new targets to improve efficacy of cancer immunotherapy (CIT). To achieve this objective, extensive single-cell analysis will be performed on mouse models of melanoma and samples from human melanoma patients in response to CPI-targeting of T cells, and CITs targeting tumor associated monocytes and macrophages (TAMs). These data will be computationally analyzed to construct cell-cell interaction networks between stromal, tumor, and immune cells to identify interactions that maintain immunosuppressive TMEs, predict how to target them, and test these predictions experimentally. The central hypothesis tested in this proposal is that intercellular signaling networks between TAMs and other cells in the TME are central to suppressing immune activity, and that TAM-mediated networks are critical to reestablishing an effective TME immune response, especially in cases of CPI resistance due to inadequate T cell responses. The rationale for the proposed research is that identifying cell-cell interactions that distinguish immunosuppressive versus immunosupportive TMEs will serve as a roadmap of new targets to test in unresponsive tumors. Aim 1 will develop computational models that can distinguish between an ineffective versus effective anti-tumor immune response. These models will be developed by constructing intercellular networks of receptor-ligand interactions from single-cell RNA sequencing (scRNA-seq) and pathology data in growing and regressing murine and human melanoma tumors. The models will be used to identify targets mediating cell-cell interactions that will be validated experimentally. The objective of Aim 2 will be to determine how the functional plasticity of macrophages and other myeloid cells contributes to an immunosuppressive TME in mice and human melanomas. Interactome maps will be expanded to identify interactions between myeloid cell subsets and other cell types in the tumor over time and with treatment. The proposed research is innovative because, rather than focusing solely on isolated end points (e.g., T cell infiltration), it will identify the network of intercellular communication that stabilizes those endpoints. With respect to cancer systems biology, the proposed research is innovative because it will combine new computational methods–for defining cell subsets and network interactions from scRNA-seq data and constructing predictive classification models–with syngeneic mouse melanoma models and human patient samples that are ideally suited to evaluate CIT responses. The proposed research is significant because it will redefine the hallmarks of immune activity in the TME as emergent properties of multiple cell-cell interactions that can be pharmacologically targeted to design immunotherapies that will be effective on non-responding patients.

项目总结