Data Processing, Analysis and Modeling Unit

数据处理、分析和建模单元

基本信息

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
项目类别：
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

项目摘要

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（数据分析单元）转移性肿瘤是癌症死亡的主要原因，并且在生物学上难以治疗。潜在的细胞状态可塑性和控制适应的独特分子程序国外的微环境需要对复杂环境有更深入的了解我们的目标是通过建立时空模型来解决这一重大知识差距。三种异常致命的恶性肿瘤的转移转变图集——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深度注释的源自患者的原发性和转移性临床中的维度空间映射为了实现这一目标，我们的首要目标是开发实验设计方法来选择样品。用于创建这三个图集的患者、样本和实验参数。基于这样的基本原理：图集构建在实验中提出了新的统计挑战必须开发设计以最大限度地利用我们的地图集构建资源。第二个目标我们将大规模建设和实施数据分析单元的基础设施。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数据将指导使用图集进行搜索、比较和解释最后，我们将开发新的数据集成和计算方法。在这种情况下，我们假设数据来自不同的时空地图集。技术和平台通常包含冗余的生物信息信息，这些信息可以被提取以生成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。预测那些可能转移到的早期肺腺癌的案例最终，构建的地图集将提供对聚合事件和瓶颈的洞察。在转移转变中，提出了潜在的治疗靶点和机会干涉。