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

• 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
• 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

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 我们的CSBC MIT/DFCI系统生物学中心的中心前提是高含量 系统级测量技术，在各种尺度（分子 - 细胞组织）上使用 细胞类型特异性和空间分辨率，并与数据整合，数据反卷积和 计算建模，将使关键途径和网络结合肿瘤的识别 进展和热阻力，同时还提供反映肿瘤状态和效率的生物标志物 改进的治疗策略。该项目旨在将同样的前提应用于肿瘤免疫接口， 定义免疫抑制状态动态演化的分子途径和网络 GBM肿瘤。为此，我们设计了一个多层项目，基础基于仔细 在体外共培养模型系统中肿瘤免疫接口的控制共同进化，临时 实验和实验性细胞类型中分子淋巴结的系统级多词分析 计算反卷积。该数据与定量表型数据的计算建模将 产生有关与免疫和肿瘤状态改变的节点，途径和网络的预测 经过实验验证。在第二个目的中，我们将这些研究扩展到多种宝石和同性鼠 在体内查询肿瘤 - 免疫接口的模型，并在不同的系统级别分析不同 肿瘤发育的时间点和对治疗的反应。我们还使用此目标来审问 通过缺乏工程小鼠，不同的免疫细胞介导肿瘤免疫接口的演变 各种免疫细胞类型并重复上述研究。这些数据连接的计算建模 在更复杂的体内环境中具有动态演化的分子网络。最后，第三个目标 这个项目，我们通过系统将这些分析扩展到人类GBM肿瘤的地理不同区域 - 空间引导的核心活检的水平分析。这些人类肿瘤标本提供了“地面真相”数据 我们的计算模型并能够开发定量模型，以预测 不同的治疗策略。该项目一起将产生前所未有的系统级分子洞察力 肿瘤免疫接口，使新的治疗靶标识别以消除 GBM肿瘤的免疫抑制性质。