TOPIC 428 "CLOUD-BASED LIQUID-BIOPSY PLATFORM FOR THE CANCER RESEARCH DATA COMMONS"

用于多组学和成像分析的基于云的资源和技术的最新进展为使用定量方法探索组织学、分子事件和临床结果之间的关系创造了新的机会。然而，多组学和成像数据的前所未有的规模和复杂性已经提出了关键的计算瓶颈，需要新的概念和使能工具。该提案的目的是通过创新的基于云的数据分析管道来解决癌症基因组图谱（TCGA），临床蛋白质组肿瘤分析联盟（CPTAC）和癌症成像档案（TCIA）的多组学和成像数据综合分析的计算挑战，以充分释放癌症研究数据共享（CRDC）的潜力。这将通过构建一个计算框架来实现，该框架将新的大数据分析算法集成到基于云的管道中，以揭示组织病理学图像，多组学和表型结果之间的复杂关系。该项目不仅促进了新的大数据分析技术的开发，还通过基于云的数据分析管道解决了癌症研究中出现的科学问题，该管道包括用于多组学和成像分析的创新计算方法，并与CRDC接口。预计提出的计算方法和管道将影响癌症研究，并使研究人员能够有效地测试他们的科学假设。

Recent advances in cloud-based resources and technologies for multi-omics and imaging analysis have created new opportunities for exploring relationships between histology, molecular events, and clinical outcomes using quantitative methods. However, the unprecedented scale and complexity of multi-omics and imaging data have presented critical computational bottlenecks requiring new concepts and enabling tools. The objective of this proposal is to address the computational challenges in integrative analysis of multi-omics and imaging data from The Cancer Genome Atlas (TCGA), Clinical Proteomic Tumor Analysis Consortium (CPTAC), and The Cancer Imaging Archive (TCIA) via an innovative cloud-based data analytics pipeline to fully unlock the potential of the Cancer Research Data Commons (CRDC). This will be accomplished by building a computational framework that integrates novel big data analysis algorithms into a cloud-based pipeline for revealing complex relationships between histopathology images, multi-omics, and phenotypic outcomes. This project not only facilitates the development of new big data analysis techniques, but also addresses emerging scientific questions in cancer research via a cloud-based data analytics pipeline that consists of innovative computational methods for multi-omics and imaging analysis and interfaced with CRDC. The proposed computational methods and pipeline are expected to impact cancer research and enable investigators to effectively test their scientific hypothesis.