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

摘要--项目2 我们CSBC MIT/DFCI胶质母细胞瘤系统生物学中心的主要前提是 系统级测量技术，适用于各种尺度(分子-细胞-组织) 单元类型的特定分辨率和空间分辨率，并与数据集成、数据反卷积和 计算机建模将使识别调节肿瘤的关键途径和网络成为可能 进展和治疗耐药，同时也提供反映肿瘤状态和疗效的生物标志物 改进治疗策略。该项目旨在将同样的前提应用于肿瘤免疫界面， 确定免疫抑制状态动态演变的分子途径和网络 基底膜肿瘤。为此，我们精心设计了一个多层次的项目，以此为基础 在体外共培养模型系统中肿瘤-免疫界面的受控协同进化，与时间 对特定细胞类型中的分子节点的系统级多组学分析，由实验和 计算反卷积。该数据与定量表型数据耦合的计算建模将 对与免疫和肿瘤状态改变相关的节点、通路和网络进行预测 通过实验得到验证。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 查询体内肿瘤-免疫界面的模型，在不同的空间和时间系统水平上进行分析 肿瘤发生的时间点和对治疗的反应。我们也用这个目的来审问 不同免疫细胞在工程化小鼠肿瘤-免疫界面进化中的作用 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 分子网络在更复杂的活体环境中具有动态演化。最后，在第三个目标中 在这个项目中，我们通过以下系统将这些分析扩展到人类GBM肿瘤的不同地理区域- 空间引导的核心活检的水平分析。这些人类肿瘤标本为 我们的计算模型，并使定量模型的开发预测治疗的影响 不同的治疗策略。总而言之，这个项目将产生前所未有的系统级别的分子洞察 肿瘤-免疫界面，使得能够识别新的治疗靶点以消除 基底膜肿瘤的免疫抑制性质。

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.