NCI Office of Data Sharing (ODS) Data Jamboree (Abstract Submissions): Submission #17

Submission information
Submission Number: 17
Submission ID: 145078
Submission UUID: 0a264f46-7e43-4f9c-b16b-7f3347174b0a

Created: Fri, 06/20/2025 - 12:59
Completed: Fri, 06/20/2025 - 13:02
Changed: Fri, 06/20/2025 - 13:02

Remote IP address: 10.208.24.91
Submitted by: Anonymous
Language: English

Is draft: No
First Name Serghei
Middle Initial
Last Name Mangul
Degree(s) Ph.D.
Position/Title/Career Status Director | Challenges and Benchmarking
Organization Sage Bionetworks
Organization Address Seattle
Email serghei.mangul@sagebase.org
Other (Please Specify)
Abstract Category Employment of statistical methods or existing computational, mathematical, or informatics tools
Abstract Keywords RNA-Seq Immunophenotyping Childhood Cancer Immunology Ancestry-Inclusive Immune Profiling Bioinformatics Database Development Health Disparities Research
Abstract Title Developing reliable and scalable methods for deep immune phenotyping in public Childhood Cancer RNA-Seq Data repositories
Abstract Summary Recent advancements in RNA-Seq technologies have significantly improved our ability to analyze individual transcriptomes. However, current analyses in immunology, especially concerning childhood cancer, are limited, often overlooking crucial information like ancestry, cell type composition, HLA type, KIR expression, and T/B Cell Receptor (TCR/BCR) repertoires. This missing data is vital for understanding immune responses and disease susceptibility across diverse populations. Existing RNA-Seq and immunological databases are insufficient, either lacking essential immunological and ancestry data or missing key immunological phenotypes. This critical gap prevents comprehensive immunological studies that consider the variability in immune responses across different populations, particularly important for childhood cancer. To overcome these limitations, we propose developing advanced bioinformatics tools and a comprehensive database to infer and integrate critical immune phenotypes and ancestry information directly from RNA-Seq data. Our approach will enable more accurate and thorough analysis of immune-related diseases across diverse populations by leveraging public RNA-Seq samples. Methods will be rigorously benchmarked to ensure reliability, providing crucial insights into health disparities. We will develop robust methods for deep immune phenotyping, including an accurate HLA typing tool using a pan-genome reference to minimize ancestral bias. We'll also create tools to infer Adaptive Immune Receptor Repertoires (AIRR) alleles and enhance T and B cell receptor assembly for improved precision in V(D)J recombination and clonotype assembly. A consensus-based method will be introduced for more accurate cell type composition analysis. The insights gained will be disseminated through a novel, user-friendly database—the largest collection of individuals with detailed immunological phenotypes across diverse backgrounds and disease conditions. This platform will offer comprehensive functionalities, including normalization and meta-analysis, accessible via an R package, GUI, and API. We will prioritize ethical and security issues, promoting access and reuse of pediatric cancer data and fostering interdisciplinary collaborations.
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