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

摘要-项目2 我们的CSBC MIT/DFCI胶质母细胞瘤系统生物学中心的中心前提是， 系统级测量技术，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，并结合数据整合，数据反卷积， 计算机建模，将能够识别调控肿瘤的关键途径和网络 进展和治疗抗性，同时还提供反映肿瘤状态的生物标志物和化疗的功效。 改善治疗策略。该项目旨在将同样的前提应用于肿瘤免疫界面， 定义免疫抑制状态动态演变的分子途径和网络 GBM肿瘤。为此，我们设计了一个多层次的项目，以精心的基础 体外共培养模型系统中肿瘤-免疫界面的受控共进化， 系统水平的多组学分析的分子节点在特定的细胞类型提供的实验和 计算反褶积将这些数据与定量表型数据相结合的计算建模将 产生与改变的免疫和肿瘤状态相关的节点、通路和网络的预测， 进行实验验证。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。 模型来查询体内的肿瘤-免疫界面，在不同的时间和空间上进行系统级分析。 肿瘤发展的时间点和对治疗的反应。我们也用这个目标来询问 不同的免疫细胞介导肿瘤免疫界面的进化， 各种免疫细胞类型并重复上述研究。这些数据的计算建模 在更复杂的体内环境中具有动态进化的分子网络。最后，在第三个目标 在这个项目中，我们通过系统将这些分析扩展到人类GBM肿瘤的地理上不同的区域， 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“地面实况”数据， 我们的计算模型，并使定量模型的发展预测的治疗效果， 不同的治疗策略。总之，这个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，以消除肿瘤免疫。 GBM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.