Data Processing, Analysis and Modeling Unit

数据处理、分析和建模单元

• 批准号: 10902925
• 负责人: Jeremy Goecks
• 金额: $ 92.33万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-04 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10902925
• 关键词: 3-Dimensional; ATAC-seq; Aftercare; Atlases; Biological; Biological Assay; Biological Process; Biopsy; CDK4 gene; Cancer Patient; Cells; Characteristics; Communities; Computer software; Data; Data Analyses; Data Management Resources; Data Storage and Retrieval; Dimensions; Disseminated Malignant Neoplasm; Electron Microscopy; Generations; Genes; Goals; Image; Image Analysis; Immune checkpoint inhibitor; Immunofluorescence Immunologic; Immunohistochemistry; Immunomodulators; Individual; Malignant Neoplasms; Malignant neoplasm of prostate; Maps; Measurement; Metastatic breast cancer; Methods; Modeling; Mutation; Neoplasm Metastasis; Pathway interactions; Patients; Periodicity; Poly(ADP-ribose) Polymerase Inhibitor; Population; Primary Neoplasm; Reproducibility; Research; Resistance; Resistance development; Running; Sampling; Services; Standardization; Statistical Data Interpretation; Structure; System; Techniques; Time; Tissues; Visualization; Visualization software; animation; blood vessel development; castration resistant prostate cancer; cohort; computerized data processing; dashboard; data management; enzalutamide; epigenomics; hormone receptor-positive; hormone therapy; immune cell infiltrate; immune checkpoint; immune modulating agents; indexing; individual patient; inhibitor; malignant breast neoplasm; molecular imaging; molecular targeted therapies; multimodal data; multimodality; novel; prospective; refractory cancer; resistance mechanism; single cell sequencing; statistical and machine learning; targeted treatment; temporal measurement; therapy resistant; triple-negative invasive breast carcinoma; tumor

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.

摘要 – 数据分析单元 我们建议创建一个数据分析单元，为组学和多维空间（OMS）图集服务。 OMS Atlas 将能够发现个体患者中出现的耐药机制 当前一代靶向治疗药物治疗期间的转移性乳腺癌和前列腺癌 这些疗法与免疫检查点抑制剂的联合治疗很少用于治疗转移性癌症。 长期有效，并了解这些癌症发生的机制 抵抗治疗是 OMS Atlas 的主要目标，数据分析部门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 创建 Atlas。OMS Atlas 将收集两份活检样本，一份在治疗前，一份在治疗期间，持续 3 年。 数据分析单元的最终产品将是一个完整的肿瘤图谱。 通过交互式门户进行访问，该门户使 OMS Atlas、HTAN 和更大的用例生物学家能够使用 研究界通过量化提出有关肿瘤抵抗机制的假设， 个体治疗前和治疗后/治疗后活检的纵向和空间分辨比较 数据分析单元使用组学和成像分析（第 1 级数据）生成的原始数据。 将生成另外三个数据层：（第 2 层）通过处理数据获得的单基因/细胞测量值 （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级功能（例如生物途径活动）的量化 使用多种数据的综合分析来分析肿瘤及其周围组织的细胞 （第 4 层）肿瘤图谱，可用于比较治疗前和治疗中/治疗后活检并识别 与治疗耐药性潜在相关的特征将使用强大的软件生成。 管道由数据管理系统、图像管理软件、工作流程执行系统组成， 将实施在此平台上运行的标准化且可重复的工作流程。 生成所有层的数据将用于创建肿瘤图。 OMS Atlas 门户将连接不同检测中的镜像切片和细胞群。 提供可访问 10 种不同的肿瘤图可视化效果的单一界面。 对单个患者进行纵向可视化或跨患者进行横向比较。 可以使用仪表板方法同时显示，其中可以逐步添加可视化 根据需要，可以同时查看有关肿瘤图谱的许多不同类型的数据。 专门的动画方法和3D技术将用于可视化以有效地展示 多维、空间解析的肿瘤图数据。