Pharmacologic and Genomic Imaging Core

摘要： 胶质母细胞瘤是一种复杂的疾病，其中肿瘤成分和药物均存在异质性 为了理解治疗反应，需要考虑分布。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 （直流）。此外，微环境和脉管系统的病理生理障碍，例如血液 脑屏障（BBB），产生DC的异质分布，事实上暴露异质恶性 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 对于精确剖析胶质母细胞瘤对任何 DC 的反应至关重要。许多靶向治疗试验已 未能纳入此类测量结果，妨碍了我们解释结果的能力。药理和 哈佛/斯坦福 U19 项目的基因组成像核心 (PGIC) 将提供最先进的技术 用于表征患者肿瘤中的药物浓度和胶质母细胞瘤反应的专业知识和资源 样本和全面表征的原位小鼠患者来源的异种移植物 (PDX) 神经胶质瘤 模型。 PGIC 汇集了从微观分析到宏观体内器官的多尺度技术 具有微观和宏观尺度配准的成像。 PGIC提供了一个集成平台来融合详细信息 组织病理学、药物分布、代谢组学和单细胞转录组学，并注册到体内 可用时进行成像。具体来说，PGIC 将：（目标 1）对 DC 和 从临床试验和实验模型中采集的脑组织标本的代谢途径； （目标 2) 对冷冻的神经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； （目标 3）执行动态空间分析 使用空间转录组学和多重分析对胶质母细胞瘤患者样本和模型的治疗效果 数字成像。我们开发了创新高效的临床管线和多中心临床试验 获取样本进行 PGIC 分析并在医院环境中建立系统工作流程的基础设施。 我们的分析将在冷冻样本上进行，以促进采集的样本在多个样本之间的流动 哈佛/斯坦福 GTN 中的站点。 PGIC将提供一致的工作流程，标准化采集 样本和数据，提供专家人员，促进项目内的知识共享，以及 维护为所有项目生成数据所需的先进设备，这对数据至关重要 跨项目的可重复性和数据共享。 PGIC 将由 Mario Suvà 博士、Nathalie Agar 博士和 Keith Ligon 将贡献他们在单细胞技术、质谱成像和 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.