NCI Office of Data Sharing (ODS) Data Jamboree (Abstract Submissions): Submission #22

Submission information
Submission Number: 22
Submission ID: 145130
Submission UUID: 12d39763-1dbb-4520-b9ca-5091c95bf3b9

Created: Mon, 06/23/2025 - 10:06
Completed: Mon, 06/23/2025 - 10:06
Changed: Mon, 06/23/2025 - 10:06

Remote IP address: 10.208.28.103
Submitted by: Anonymous
Language: English

Is draft: No
First Name Yanling
Middle Initial
Last Name Sun
Degree(s) Ph.D
Position/Title/Career Status Postdoc
Organization Bing Zhang lab/ Baylor Colledge of Medicine
Organization Address houston
Email yanling.sun@bcm.edu
Other (Please Specify) sunyanling312@gmail.com
Abstract Category Employment of statistical methods or existing computational, mathematical, or informatics tools
Abstract Keywords
Abstract Title Expanding LinkedOmics for comprehensive integration of pediatric cancer omics data
Abstract Summary Pediatric cancer remains a leading cause of disease-related mortality in children. Owing to its distinct molecular underpinnings compared to adult cancers, dedicated research is essential to improve diagnostic precision and uncover novel biomarkers and therapeutic targets. Meanwhile, comprehensive molecular landscapes derived from adult cancers can provide valuable guidance in identifying key molecular features and regulatory mechanisms in pediatric tumors. LinkedOmics is the first publicly accessible multi-omics web platform that integrates mass spectrometry (MS)-based proteomics with genomics, transcriptomics, metabolomics, and lipidomics data, all accompanied by comprehensive clinical annotations. The current release includes 60 adult cancer datasets (including four controlled-access datasets), comprising over 17,000 samples. The tool is openly and freely accessible at https://www.linkedomics.org, which has been cited in over 2000 publications. To extend its utility to pediatric oncology, we have integrated a pediatric brain tumor dataset comprising 223 samples as the initial entry point. The platform enables correlation analysis between clinical variables (e.g., age, sex, race, subtype, survival) and multi-omics features to identify clinically relevant molecular signatures. The association analysis between different omics data is also supported to reveal potential regulatory interactions, followed by functional interpretation through over-representation analysis (ORA) or gene set enrichment analysis (GSEA). Comparative analysis across pediatric and adult datasets further facilitates the exploration of shared and distinct biological elements. LinkedOmics is deployed on AWS cloud infrastructure, providing high scalability, robust data security, and responsive computational performance. The platform supports secure controlled access for unpublished pediatric datasets and offers seamless analysis of publicly available data, enabling flexible data sharing from early discovery to publication. At the jamboree, we will use the pediatric brain tumor dataset as an example to demonstrate feasibility through both predefined and participant-driven case studies, encourage adoption by the pediatric cancer research community, and identify pediatric-specific needs to guide future platform development.
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