Data Processing, Analysis and Modeling Unit

数据处理、分析和建模单元

• 批准号: 10001477
• 负责人: Dana Pe'er
• 金额: $ 55.43万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-30 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10001477
• 关键词: ATAC-seq; Active Learning; Address; Adenocarcinoma; Algorithms; Architecture; Atlases; Automobile Driving; Biological; Biological Assay; Biology; Brain; Cancer Etiology; Cells; Cessation of life; Clinical; Complex; Computing Methodologies; DNA sequencing; Data; Data Analyses; Development; Environment; Epigenetic Process; Event; Experimental Designs; Genomics; Goals; Human; Image; Intervention; Knowledge; Learning; Lung Adenocarcinoma; Malignant Neoplasms; Malignant neoplasm of lung; Malignant neoplasm of pancreas; Maps; Memorial Sloan-Kettering Cancer Center; Metastatic Neoplasm to the Central Nervous System; Metastatic malignant neoplasm to brain; Metastatic to; Methods; Modality; Modeling; Molecular; Neoplasm Metastasis; Patient Selection; Patients; Pattern; Phenotype; Phylogeny; Primary Neoplasm; Quality Control; Resolution; Resource Allocation; Resources; Risk; Sampling; Techniques; Technology; Therapeutic; base; cell type; clinically actionable; computerized data processing; data analysis pipeline; data infrastructure; data integration; deep learning; digital pathology; insight; metastatic process; novel; programs; single-cell RNA sequencing; therapeutic target; tumor; tumor growth

PROJECT SUMMARY (Data Analysis Unit) Metastatic tumors are the leading cause of cancer deaths and are difficult to treat. The biology underlying cell state plasticity and the distinct molecular programs that govern adaptation to foreign microenvironments will require a much deeper understanding of the complex environment of tumor growth. We aim to address this significant knowledge gap by building a spatial-temporal atlas of the metastatic transition of three exceptionally lethal sets of malignancies – lung cancer, pancreatic cancer, and CNS metastases- by combining single-cell genomics with multi- dimensional spatial mapping in deeply annotated patient-derived primary and metastatic clinical samples. Towards this goal, our first aim will be to develop experimental design methods to select the patients, samples, and experimental parameters for creation of these three atlases. This aim is based on the rationale that atlas construction poses novel statistical challenges in experimental design that must be developed to maximally utilize resources towards atlas construction. In our second aim we will construct and implement the infrastructure of the data analysis unit at scale. The rationale for this aim is that quality control, and scalable quantification and annotation of the data at large scale will guide the use of the atlas for searching, comparing and interpreting samples. Finally, we will develop novel computational methods for data integration and interpretation towards a spatial-temporal atlas. In this instance we posit that data from different technologies and platforms often include redundant, biologically informative information that can be extracted for producing an annotated atlas. We will illustrate the impact of our atlas in the use case of predicting those early stage lung adenocarcinomas that are likely to metastasize to the brain. Ultimately the constructed atlases will provide insight on convergent events and bottlenecks in the metastatic transition, suggesting potential therapeutic targets and opportunities for intervention.

项目摘要（数据分析股） 转移性肿瘤是癌症死亡的主要原因，并且难以治疗。 潜在的细胞状态可塑性和不同的分子程序，管理适应， 外国的微环境将需要对复杂环境有更深入的了解 肿瘤的生长。我们的目标是通过建立一个时空 三种异常致命的恶性肿瘤转移转变图集--肺癌， 胰腺癌和中枢神经系统转移-通过将单细胞基因组学与多细胞基因组学相结合， 在深度注释的患者来源的原发性和转移性临床 样品为了实现这一目标，我们的第一个目标将是开发实验设计方法， 用于创建这三个图谱的患者、样本和实验参数。这一目标 是基于这样的基本原理，即图谱构建在实验中提出了新的统计挑战， 必须制定设计，以便最大限度地利用资源来建造atlas。在我们 第二个目标是大规模地建立和实施数据分析股的基础设施。 这一目标的基本原理是，质量控制，以及可扩展的量化和注释， 大规模的数据将指导地图集的使用，以进行搜索、比较和解释 样品最后，我们将开发新的数据集成计算方法， 对时空地图集的解释。在这种情况下，我们将数据从不同的 技术和平台通常包括冗余的生物信息信息， 被提取用于产生带注释的地图集。我们将说明我们的地图集在使用中的影响 预测那些早期肺腺癌有可能转移到 个脑袋最终，构建的地图集将提供对聚合事件和瓶颈的洞察力 在转移性转化中，提示潜在的治疗靶点和机会， 干预