NCI Office of Data Sharing (ODS) Data Jamboree (Abstract Submissions): Submission #23
Submission information
Submission Number: 23
Submission ID: 145131
Submission UUID: e916cb3f-64f9-46ec-ae3a-a3ed1c910306
Submission URI: /nci/ods-data-jamboree/abstractsubmissions
Submission Update: /nci/ods-data-jamboree/abstractsubmissions?token=6sw0Rr-ZDP-RmjVfsrAbxYf1N2ao6uzkj_XW3QVAurY
Created: Mon, 06/23/2025 - 10:14
Completed: Mon, 06/23/2025 - 10:19
Changed: Mon, 06/23/2025 - 10:19
Remote IP address: 10.208.28.103
Submitted by: Anonymous
Language: English
Is draft: No
Presenter Information
Elmer
Andrés
Fernández
Ph.D
Head of Health Data Science Laboratory and Biomedical Engineer
Fundacion para el Progreso de la Medicina
Córdoba, Argentina
daniorschanski@mi.unc.edu.ar
Abstract Information
Development or refinement of analysis pipelines or AI/ML algorithms
Gene Fusions, Knowledgebase, Pediatric Oncology
Fusion.AR-DB: A Collaborative Knowledgebase of Actionable Gene Fusions in Pediatric Cancers Leveraging Large-Scale RNA-Seq Data
Gene fusions are powerful oncogenic drivers and biomarkers in pediatric and AYA (adolescent and young adult) cancers, yet their clinical utility remains underexploited due to fragmented datasets, limited detection approaches, and the lack of centralized, clinically meaningful resources. We propose Fusion.AR-DB, the first open-access, comprehensive knowledge base of gene fusions in pediatric and AYA cancers.
The objective is to systematically detect, annotate, and organize gene fusions across publicly available RNA-Seq datasets, including NIH Kids First, TARGET, CCDI, and others. Fusion.AR-DB will catalog all detected fusions, not only known or actionable ones, and associate them with clinical data like tumor types, molecular subtypes, and expression profiles. Therapeutically actionable events will be linked to FDA/EMA-approved drugs and clinical trials. Each fusion will be annotated with functional and structural insights, including domain-level information, expression impact, and structure of the resulting chimeric proteins, enabling downstream modeling and docking.
Built with a validated, high-performance pipeline optimized for low-input pediatric samples, the platform integrates tools such as STAR and Arriba. All analyses will be run in a high-performance computing environment. The results will be delivered via a user-friendly, interactive interface, allowing users to explore fusion prevalence, co-occurrence, functional relevance, and therapeutic potential.
Preliminary analyses reveal:
- Undetected kinase fusions in high-risk pediatric cancers.
- Novel recurrent fusion actionable events.
- Actionable fusions in ~7% of FISH-negative tumors.
Impact: Fusion.AR-DB increases therapeutic eligibility up to 3-fold in pilot cohorts, reduces diagnostic costs by over $1.2M/year per institution, and directly supports NIH goals by transforming RNA-Seq data into clinically actionable, personalized insights.
Project Team:
- Ph.D. Elmer Fernández, Principal Investigator
- Guadalupe Nibeyro, Biochemist and PhD Candidate
- Daniela Orschanski, Biomedical Engineer and PhD Candidate
Affiliated with the Fundación para el Progreso de la Medicina and CONICET, Córdoba, Argentina.
The objective is to systematically detect, annotate, and organize gene fusions across publicly available RNA-Seq datasets, including NIH Kids First, TARGET, CCDI, and others. Fusion.AR-DB will catalog all detected fusions, not only known or actionable ones, and associate them with clinical data like tumor types, molecular subtypes, and expression profiles. Therapeutically actionable events will be linked to FDA/EMA-approved drugs and clinical trials. Each fusion will be annotated with functional and structural insights, including domain-level information, expression impact, and structure of the resulting chimeric proteins, enabling downstream modeling and docking.
Built with a validated, high-performance pipeline optimized for low-input pediatric samples, the platform integrates tools such as STAR and Arriba. All analyses will be run in a high-performance computing environment. The results will be delivered via a user-friendly, interactive interface, allowing users to explore fusion prevalence, co-occurrence, functional relevance, and therapeutic potential.
Preliminary analyses reveal:
- Undetected kinase fusions in high-risk pediatric cancers.
- Novel recurrent fusion actionable events.
- Actionable fusions in ~7% of FISH-negative tumors.
Impact: Fusion.AR-DB increases therapeutic eligibility up to 3-fold in pilot cohorts, reduces diagnostic costs by over $1.2M/year per institution, and directly supports NIH goals by transforming RNA-Seq data into clinically actionable, personalized insights.
Project Team:
- Ph.D. Elmer Fernández, Principal Investigator
- Guadalupe Nibeyro, Biochemist and PhD Candidate
- Daniela Orschanski, Biomedical Engineer and PhD Candidate
Affiliated with the Fundación para el Progreso de la Medicina and CONICET, Córdoba, Argentina.