Data Processing, Analysis and Modeling Unit

摘要数据分析股 我们提议设立一个数据分析股，为OMICS和多维空间(OMS)地图集服务。 OMS图谱将有助于发现出现在个体患者身上的耐药机制 当代靶向治疗中的转移性乳腺癌和前列腺癌 联合用药和免疫检查点抑制剂。这些治疗方法在转移性癌症中很少见 在很长一段时间内有效，并了解这些癌症形成的机制 抵抗治疗是OMS地图集的主要目标。数据分析股将通过以下方式支持这一目标 开发和部署数据管理、处理、分析和可视化方法和软件，以 创建地图集。OMS Atlas将收集两份活组织检查，一份在治疗前，一份在治疗期间，共3份 不同的癌症患者队列。数据分析股的最终产品将是一份完整的肿瘤图谱 可通过交互式门户访问，使OMS Atlas、Htan和更大版本中的用例生物学家能够访问 研究社区通过量化、 个体治疗前和治疗后活检组织的纵向和空间分辨率比较 病人。使用组学和成像分析产生的主要数据(第1层数据)，数据分析股 将生成额外的三层数据：(第二层)通过处理数据获得的单基因/细胞测量 来自单一数据平台；(第3层)通过结合单细胞和空间分辨率生成的肿瘤地图 组学和成像数据以及系统级功能的量化，如生物途径活动 以及使用多个数据的综合分析来构成肿瘤及其周围组织的细胞 平台；(第4层)肿瘤图谱，可用于比较治疗前和治疗中/治疗后的活检组织并确定 这些特征可能与对治疗的耐药性有关。将使用强大的软件生成数据层 由数据管理系统、图像管理软件、工作流执行系统、 和可视化工具。将实施在该平台上运行的标准化和可重复使用的工作流 以生成所有层的数据。将使用统计和机器学习方法来创建肿瘤地图 通过不同的检测将镜像切片和细胞群连接起来。OMS Atlas门户网站将 提供单一界面，可访问10种不同的肿瘤地图可视化。肿瘤地图可以是 在单个患者内进行纵向可视化和比较，或在患者之间进行横向比较。许多可视化效果 可以使用仪表板方法同时显示，其中可以逐步添加可视化效果 根据需要，可以同时查看有关肿瘤地图的多种不同类型的数据。 将在可视化中使用专门的动画方法和3D技术，以有效地显示 多维、空间分辨率的肿瘤地图数据。

ABSTRACT – Data Analysis Unit We propose to create a Data Analysis Unit in service of the Omics and Multidimensional Spatial (OMS) Atlas. The OMS Atlas will enable discovery of mechanisms of resistance that arise in individual patients with metastatic breast and prostate cancer during treatment with current generation of targeted therapeutic combinations and immune checkpoint inhibitors. Treatment will these therapies in metastatic cancer is rarely effective for an extended period of time, and understanding the mechanisms by which these cancers become resistant to therapy is the primary goal of the OMS Atlas. The Data Analysis Unit will support this goal by developing and deploying data management, processing, analysis, and visualization methods and software to create the Atlas. The OMS Atlas will collect two biopsies, one before treatment and one during treatment for 3 different cohorts of cancer patients. The final product of the Data Analysis Unit will be a complete tumor atlas accessible via an interactive portal that enables use-case biologists in the OMS Atlas, the HTAN and the larger research community to develop hypotheses about tumor resistance mechanisms through quantified, longitudinal, and spatially-resolved comparisons of pre- and on/post-treatment biopsies from individual patients. Using primary data generated from omics and imaging assays (Tier 1 data), the Data Analysis Unit will generate three additional tiers of data: (Tier 2) single gene/cell measurements obtained by processing data from a single data platform; (Tier 3) tumor maps generated by combining single-cell and spatially-resolved omics and imaging data as well as quantification of systems-level functions such as biological pathway activity and the cells comprising the tumor and its surrounding tissue using integrative analyses of multiple data platforms; (Tier 4) a tumor atlas that can be used to compare pre- and on/post-treatment biopsies and identify features potentially correlated with resistance to treatment. Data tiers will be generated using a robust software pipeline consisting of a data management system, image management software, a workflow execution system, and visualization tools. Standardized and reproducible workflows that run on this platform will be implemented to generate all tiers of data. Statistical and machine learning approaches will be used to create tumor maps by connecting mirror image sections and cell populations across different assays. The OMS Atlas portal will provide a single interface with access to 10 different visualizations of tumor maps. Tumor maps can be visualized and compared longitudinally within a single patient or laterally across patients. Many visualizations can be displayed simultaneously using a dashboard approach where visualizations can be progressively added as desired, making it possible to view many different types of data about tumor maps simultaneously. Specialized animation approaches and 3D techniques will be used in visualizations to effectively display multidimensional, spatially resolved tumor map data.