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.