NCI Cancer Research Data Commons: Cloud-Based Analytic Resources.

The NCI's Cloud Resources (CR) are the analytical components of the Cancer Research Data Commons (CRDC) ecosystem. This review describes how the three CRs (Broad Institute FireCloud, Institute for Systems Biology Cancer Gateway in the Cloud, and Seven Bridges Cancer Genomics Cloud) provide access and availability to large, cloud-hosted, multimodal cancer datasets, as well as offer tools and workspaces for performing data analysis where the data resides, without download or storage. In addition, users can upload their own data and tools into their workspaces, allowing researchers to create custom analysis workflows and integrate CRDC-hosted data with their own. See related articles by Brady et al., p. 1384, Wang et al., p. 1388, and Kim et al., p. 1404.

Pancreatic Cancer Action Network's SPARK: A Cloud-Based Patient Health Data and Analytics Platform for Pancreatic Cancer.

PURPOSE: Pancreatic cancer currently holds the position of third deadliest cancer in the United States and the 5-year survival rate is among the lowest for major cancers at just 12%. Thus, continued research efforts to better understand the clinical and molecular underpinnings of pancreatic cancer are critical to developing both early detection methodologies as well as improved therapeutic options. This study introduces Pancreatic Cancer Action Network's (PanCAN's) SPARK, a cloud-based data and analytics platform that integrates patient health data from the PanCAN's research initiatives and aims to accelerate pancreatic cancer research by making real-world patient health data and analysis tools easier to access and use. MATERIALS AND METHODS: The SPARK platform integrates clinical, molecular, multiomic, imaging, and patient-reported data generated from PanCAN's research initiatives. The platform is built on a cloud-based infrastructure powered by Velsera. Cohort exploration and browser capabilities are built using Velsera ARIA, a specialized product for leveraging clinicogenomic data to build cohorts, query variant information, and drive downstream association analyses. Data science and analytic capabilities are also built into the platform allowing researchers to perform simple to complex analysis. RESULTS: Version 1 of the SPARK platform was released to pilot users, who represented diverse end users, including molecular biologists, clinicians, and bioinformaticians. Included in the pilot release of SPARK are deidentified clinical (including treatment and outcomes data), molecular, multiomic, and whole-slide pathology images for over 600 patients enrolled in PanCAN's Know Your Tumor molecular profiling service. CONCLUSION: The pilot release of the SPARK platform introduces qualified researchers to PanCAN real-world patient health data and analytical resources in a centralized location.

Multi-omics Pathways Workflow (MOPAW): An Automated Multi-omics Workflow on the Cancer Genomics Cloud.

Introduction: In the era of big data, gene-set pathway analyses derived from multi-omics are exceptionally powerful. When preparing and analyzing high-dimensional multi-omics data, the installation process and programing skills required to use existing tools can be challenging. This is especially the case for those who are not familiar with coding. In addition, implementation with high performance computing solutions is required to run these tools efficiently. Methods: We introduce an automatic multi-omics pathway workflow, a point and click graphical user interface to Multivariate Single Sample Gene Set Analysis (MOGSA), hosted on the Cancer Genomics Cloud by Seven Bridges Genomics. This workflow leverages the combination of different tools to perform data preparation for each given data types, dimensionality reduction, and MOGSA pathway analysis. The Omics data includes copy number alteration, transcriptomics data, proteomics and phosphoproteomics data. We have also provided an additional workflow to help with downloading data from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium and preprocessing these data to be used for this multi-omics pathway workflow. Results: The main outputs of this workflow are the distinct pathways for subgroups of interest provided by users, which are displayed in heatmaps if identified. In addition to this, graphs and tables are provided to users for reviewing. Conclusion: Multi-omics Pathway Workflow requires no coding experience. Users can bring their own data or download and preprocess public datasets from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium using our additional workflow based on the samples of interest. Distinct overactivated or deactivated pathways for groups of interest can be found. This useful information is important in effective therapeutic targeting.

Retrotransposon insertions associated with risk of neurologic and psychiatric diseases.

Transposable elements (TEs) are active in neuronal cells raising the question whether TE insertions contribute to risk of neuropsychiatric disease. While genome-wide association studies (GWAS) serve as a tool to discover genetic loci associated with neuropsychiatric diseases, unfortunately GWAS do not directly detect structural variants such as TEs. To examine the role of TEs in psychiatric and neurologic disease, we evaluated 17,000 polymorphic TEs and find 76 are in linkage disequilibrium with disease haplotypes (P < 10-6 ) defined by GWAS. From these 76 polymorphic TEs, we identify potentially causal candidates based on having insertions in genomic regions of regulatory chromatin and on having associations with altered gene expression in brain tissues. We show that lead candidate insertions have regulatory effects on gene expression in human neural stem cells altering the activity of a minimal promoter. Taken together, we identify 10 polymorphic TE insertions that are potential candidates on par with other variants for having a causal role in neurologic and psychiatric disorders.