Pharmacologic and Genomic Imaging Core

摘要： 胶质母细胞瘤是一种复杂疾病，在这种疾病中，肿瘤成分和药物的异质性 为了了解治疗反应，需要考虑分布情况。遗传、表观遗传和 恶性细胞之间不同的发育程序会影响对任何候选药物的反应 (DC)。此外，微环境和血管系统的病理生理障碍，如血液- 脑屏障(BBB)，产生异质性分布的DC，事实上暴露了异质性恶性 细胞及其微环境对不同药物浓度的影响。评估这些可变参数是 最重要的是要准确地剖析胶质母细胞瘤对任何DC的反应。许多靶向治疗试验都有 没有纳入这样的测量，阻碍了我们解释结果的能力。药理和药理作用 哈佛/斯坦福U19计划的基因组成像核心(PGIC)将提供最先进的技术 研究肿瘤患者的药物浓度和胶质母细胞瘤反应的专业知识和资源 小鼠原位患者来源的异种移植(PDX)胶质瘤的标本和全面特征 模特们。PGIC结合了从微观分析到体内大尺度器官的多尺度技术 具有微观和宏观尺度配准的成像。PGIC提供集成平台，融合详细信息 注册到体内的组织病理学、药物分布、代谢组学和单细胞转录组学 成像(如果可用)。具体地说，PGIC将：(Aim1)执行DC和DC的空间分辨率测量 从临床试验和实验模型中采集的脑组织样本中的代谢途径； 2)对冻存的胶质瘤和微环境细胞进行单核rna测序分析。 胶质母细胞瘤的临床样本和临床前模型；(AIM 3)进行动态空间分析 应用空间转录和多重分析技术对胶质母细胞瘤患者样本和模型的治疗效果 数字成像。我们开发了创新和高效的临床流水线和多地点临床试验 基础设施，以获取用于PGIC分析的样本，并在医院环境中建立系统的工作流程。 我们的分析将在冷冻样本上进行，以促进获取的样本在多个 位于哈佛/斯坦福GTN的网站。PGIC将提供一致的工作流程，标准化收购 样本和数据，提供专家人员，促进方案内的知识共享，以及 维护为所有项目生成数据所需的复杂设备，这对数据至关重要 可重复性和跨项目的数据共享。PGIC将由Mario SUVà博士、Nathalie Agar博士和Dr Nathalie Agar博士领导。 Keith Ligon将贡献他们在单细胞技术、质谱学成像和 神经病理学。

Summary: Glioblastoma represents a complex disease in which heterogeneity in both tumor composition and drug distribution needs to be considered for understanding treatment response. Genetic, epigenetic and developmental programs that differ between malignant cells influence the response to any drug candidates (DC). Additionally, pathophysiological barriers of the microenvironment and vasculature, such as the blood- brain barrier (BBB), produce heterogeneous distribution of DC, de facto exposing heterogeneous malignant cells and their microenvironment to variable drug concentrations. Assessing these variable parameters is paramount to precisely dissect the response of glioblastoma to any DC. Many targeted therapy trials have failed to incorporate such measurements, hampering our ability to interpret results. The Pharmacological and Genomic Imaging Core (PGIC) of the Harvard/Stanford U19 Program will provide state-of-art technical expertise and resources for characterizing drug concentration and glioblastoma response in patient tumor samples and in comprehensively characterized orthotopic mouse patient-derived xenografts (PDX) glioma models. PGIC brings together multiscale technologies from microscale analysis to macroscale organ in vivo imaging with micro- and macro-scale registration. PGIC provides an integrated platform to fuse detailed histopathology, drug distribution, metabolomics, and single-cell transcriptomics with registration to in vivo imaging when available. Specifically, PGIC will: (Aim1) perform spatially-resolved measurements of DC and metabolic pathways in brain tissue specimens sampled from both clinical trials and experimental models; (Aim 2) perform single-nucleus RNA-sequencing analysis of glioma and microenvironmental cells from frozen clinical samples and pre-clinical models of glioblastoma; (Aim 3) perform spatial analysis of dynamic therapeutic effects in glioblastoma patient samples and models using spatial transcriptomics and multiplex digital imaging. We have developed innovative and efficient clinical pipelines and multi-site clinical trials infrastructure to obtain samples for PGIC analysis and put in place systematic workflows in the hospital setting. Our analyses will be performed on frozen samples, facilitating the flow of acquired specimen across multiple sites in the Harvard/Stanford GTN. PGIC will provide consistent workflows, standardize the acquisition of samples and data, provide expert personnel, facilitate the sharing of knowledge within the program, and maintain the sophisticated equipment necessary to generate data for all Projects which is critical for data reproducibility and data sharing across projects. PGIC will be led Dr. Mario Suvà, Dr Nathalie Agar and Dr. Keith Ligon who will contribute their expertise in single-cell technologies, mass spectrometry imaging and neuropathology.