NCI Office of Data Sharing (ODS) Data Jamboree (Abstract Submissions): Submission #33
Submission information
Submission Number: 33
Submission ID: 145192
Submission UUID: e84b8ed3-1a33-4918-bea4-b3d4a25cce88
Submission URI: /nci/ods-data-jamboree/abstractsubmissions
Submission Update: /nci/ods-data-jamboree/abstractsubmissions?token=vlN-Y1g5Tyjcy4cei0Fa1y5vIiEltK1M1iZPmuKKfVw
Created: Mon, 06/23/2025 - 18:10
Completed: Mon, 06/23/2025 - 18:17
Changed: Mon, 06/23/2025 - 18:17
Remote IP address: 10.208.24.253
Submitted by: Anonymous
Language: English
Is draft: No
Presenter Information
Azra
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Krek
Ph.D.
Senior scientist
Mount Sinai School of Medicine, Department of Genetics and Genomics, Francesca Petralia lab
New York
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Abstract Information
Employment of statistical methods or existing computational, mathematical, or informatics tools
pediatric brain tumors; tumor microenvironment; multi-omic data integration; cell-type deconvolution;
Characterizing the Tumor Microenvironment in Pediatric Brain Tumors
Pediatric brain tumors have been reported to have large cell composition heterogeneity in the tumor microenvironment (Petralia et al, Cell 2020, PMID: 33242424). To better characterize the immune landscape as well as tumor cell-unique biology among these tumors, we propose to perform comprehensive deconvolution analysis using proteogenomic data of ~400 pediatric tumors from an on-going pan-histology pediatric brain tumor study through the collaboration between the Kids First consortium and NCI-CPTAC. The histology covered in the study include Craniopharyngioma (n=64), Ependymoma (n=112), Medulloblastoma (n=128), and others.
Specifically, we will employ BayesDeBulk , a Bayesian framework that can jointly analyze gene expression and proteomics data, to systematically characterize the cellular composition of tumors, including immune, stromal, and vascular components, and infer tumor cell specific expression profiles from the bulk data. In parallel, we will analyze post-translational modification data, in particular the phosphorylation patterns, to estimate kinase activity and uncover active signaling pathways that may drive TME characteristics (i.e. cell composition vector). By linking these signaling profiles, dynamic cell compositions, tumor cell specific expression, and the clinical properties of each patient, we identify distinct microenvironmental and functional patterns across tumor types.
This integrated approach enables a deeper understanding of tumor biology from bulk data and could reveal potential therapeutic targets based on both microenvironmental context and dynamic signaling networks. Our findings demonstrate the value of combining multi-omic data with advanced computational modeling to inform more precise, biology-driven treatment strategies for pediatric brain tumors.
Specifically, we will employ BayesDeBulk , a Bayesian framework that can jointly analyze gene expression and proteomics data, to systematically characterize the cellular composition of tumors, including immune, stromal, and vascular components, and infer tumor cell specific expression profiles from the bulk data. In parallel, we will analyze post-translational modification data, in particular the phosphorylation patterns, to estimate kinase activity and uncover active signaling pathways that may drive TME characteristics (i.e. cell composition vector). By linking these signaling profiles, dynamic cell compositions, tumor cell specific expression, and the clinical properties of each patient, we identify distinct microenvironmental and functional patterns across tumor types.
This integrated approach enables a deeper understanding of tumor biology from bulk data and could reveal potential therapeutic targets based on both microenvironmental context and dynamic signaling networks. Our findings demonstrate the value of combining multi-omic data with advanced computational modeling to inform more precise, biology-driven treatment strategies for pediatric brain tumors.