Data Processing, Analysis and Modeling Unit

摘要-数据分析单元 我们建议建立一个数据分析股，为组学和多维空间（OMS）地图集服务。 OMS图谱将有助于发现个体患者中出现的耐药机制， 在用当前一代靶向治疗剂治疗期间转移性乳腺癌和前列腺癌 组合和免疫检查点抑制剂。治疗将这些疗法在转移性癌症是很少的 有效的延长一段时间，并了解这些癌症成为 抵抗治疗是OMS Atlas的主要目标。数据分析股将支持这一目标， 开发和部署数据管理、处理、分析和可视化方法和软件， 创建Atlas。OMS Atlas将收集两份活检标本，一份在治疗前，一份在治疗期间， 不同的癌症患者群体。数据分析单元的最终产品将是一个完整的肿瘤图谱 可通过交互式门户网站访问，使OMS Atlas，HTAN和更大的 研究界通过量化， 个体治疗前和治疗中/治疗后活检的纵向和空间分辨比较 患者使用从组学和成像测定生成的原始数据（第1层数据），数据分析单元 将生成三个额外的数据层：（第2层）通过处理数据获得的单基因/细胞测量值 来自单一数据平台;（第3层）通过结合单细胞和空间分辨生成的肿瘤图 组学和成像数据以及系统水平功能的量化，例如生物途径活性 以及包括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/治疗后活检， 可能与治疗耐药性相关的特征。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 可视化工具。将实施在该平台上运行的标准化和可复制的工作流程 来生成所有层次的数据。统计和机器学习方法将用于创建肿瘤地图， 连接不同测定中的镜像切片和细胞群。业务管理系统Atlas门户将 提供一个界面，可访问10种不同的肿瘤图可视化。肿瘤地图可以是 可视化并在单个患者内纵向比较或在患者之间横向比较。许多可视化 可以使用仪表板方法同时显示，其中可以逐步添加可视化 如所希望的，使得可以同时查看关于肿瘤图的许多不同类型的数据。 专业的动画方法和3D技术将用于可视化，以有效地显示 多维的、空间分辨的肿瘤图数据。

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.