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Project 2: Deciphering the Dynamic Evolution of the Tumor-Immune Interface

项目2：破译肿瘤免疫界面的动态演化

基本信息

批准号：
10729276
负责人：
Forest M White
金额：
$ 45.9万
依托单位：
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
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：
2023-09-15 至 2028-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10729276
关键词：
Address Antigen Presentation Antigens B lymphoid malignancy Biological Biological Markers Biological Models Cell Communication Cells Chemicals Clinical Trials Coculture Techniques Combination immunotherapy Communication Complex Computer Analysis Computer Models Core Biopsy Coupled Critical Pathways Cross Presentation Cytotoxic T-Lymphocytes Data Dendritic Cells Development Disease Engineering Environment Evolution Failure Foundations Genetic Genomics Geography Glioblastoma Goals Human Immune Immune checkpoint inhibitor Immune response Immunologics Immunosuppression Immunotherapy In Vitro Interphase Knowledge Lung Maps Measurement Mediating Modeling Molecular Molecular Analysis Molecular Target Mus Mutation Myeloid Cells Nature Neurons Pathway Analysis Pathway interactions Patient Care Phenotype Phosphoproteins Play Proteins Repression Resolution Role Signal Transduction Specimen System Systems Biology T-Lymphocyte Techniques Therapeutic Time Tissues Transcript Translating Tumor Cell Invasion Tumor Promotion Tumor Tissue Tumor stage biological systems cell growth cell type cytokine data integration design draining lymph node dynamical evolution exhaust improved in vivo insight melanoma mouse model multiple omics neoplastic cell new therapeutic target phenotypic data predictive modeling programmed cell death ligand 1 reconstruction recruit success therapy outcome therapy resistant translational goal treatment response treatment strategy tumor tumor growth tumor microenvironment tumor progression tumor-immune system interactions tumorigenic

项目摘要

ABSTRACT – PROJECT 2 The central premise of our CSBC MIT/DFCI Center for Systems Biology in Glioblastoma is that high-content systems-level measurement techniques, employed across a variety of scales (molecular-cellular-tissue) with cell-type specific and spatial resolution, and combined with data integration, data deconvolution, and computational modeling, will enable the identification of critical pathways and networks regulating tumor progression and therapeutic resistance, while also providing biomarkers reflecting tumor state and efficacy of improved therapeutic strategies. This project aims to apply this same premise to the tumor-immune interface, defining the molecular pathways and networks underlying the dynamic evolution of the immunosuppressive state of GBM tumors. To this end, we have designed a multi-tiered project, with the foundation based on carefully controlled co-evolution of the tumor-immune interface in co-culture model systems in vitro, with temporal systems-level multi-omic analysis of molecular nodes in specific cell types provided by experimental and computational deconvolution. Computational modeling of this data coupled to quantitative phenotypic data will yield predictions as to nodes, pathways, and networks associated with altered immune and tumor states that will be experimentally verified. In the second Aim, we extend these studies to multiple GEMM and syngeneic murine models to query the tumor-immune interface in vivo, with spatial and temporal systems-level analysis at different time points of tumor development and in response to therapy. We also use this Aim to interrogate the role of different immune cells in mediating the evolution of the tumor-immune interface by engineering mice lacking various immune cell types and repeating the above studies. Computational modeling of these data connect molecular networks with dynamic evolution in the more complex in vivo environment. Finally, in the third Aim of this project, we extend these analyses to geographically distinct regions of human GBM tumors through systems- level analysis of spatially-guided core biopsies. These human tumor specimens provide ‘ground truth’ data for our computational models and enable development of quantitative models predicting the therapeutic impact of different treatment strategies. Together, this project will yield unprecedented systems-level molecular insight into the tumor-immune interface, enabling identification of novel therapeutic targets to abrogate the immunosuppressive nature of GBM tumors.

摘要-项目2