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 CSBC MIT/DFCI 胶质母细胞瘤系统生物学中心的核心前提是高内涵 系统级测量技术，应用于各种尺度（分子-细胞-组织） 细胞类型特异性和空间分辨率，并结合数据集成、数据反卷积和 计算模型将能够识别调节肿瘤的关键途径和网络 治疗耐药性，同时还提供反映肿瘤状态和疗效的进展生物标志物 该项目旨在将相同的前提应用于肿瘤免疫界面， 定义免疫抑制状态动态演化的分子途径和网络 为此，我们设计了一个多层次的项目，并在此基础上进行了仔细的研究。 体外共培养模型系统中肿瘤-免疫界面的受控共同进化，随着时间的推移 由实验和实验提供的特定细胞类型中分子节点的系统级多组学分析 将该数据与定量表型数据相结合的计算模型。 产生与免疫和肿瘤状态相关的节点、通路和网络的预测 在第二个目标中，我们将这些研究扩展到多个 GEMM 和同基因小鼠。 模型来查询体内肿瘤免疫界面，并在不同的时间进行空间和时间系统级分析 我们还使用这个目标来探究肿瘤发展的时间点和对治疗的反应。 不同免疫细胞通过缺乏基因的工程小鼠介导肿瘤免疫界面的进化 各种免疫细胞类型并重复这些数据的计算模型。 最后，在更复杂的体内环境中具有动态进化的分子网络。 在这个项目中，我们通过系统将这些分析扩展到人类 GBM 肿瘤的不同地理区域- 这些人类肿瘤标本为空间引导核心活检提供了“地面真实”数据。 我们的计算模型并能够开发预测治疗影响的定量模型 总之，该项目将产生前所未有的系统级分子洞察力。 肿瘤免疫界面，能够识别新的治疗靶点以消除 GBM 肿瘤的免疫抑制性质。