# The immunoPeptidoGenomic (iPepGen) informatics resource forimmuno-oncology research

> **NIH NIH U01** · UNIVERSITY OF MINNESOTA · 2024 · $400,530

## Abstract

SUMMARY
Immuno-oncology studies continue to grow which seek new therapies leveraging immunogenic, non-normal
peptide sequences (neoantigens) arising from tumor-specific alterations at the genomic, transcriptomic or
proteomic level. Non-normal DNA and RNA sequences that may encode neoantigens can be identified from
next-generation sequencing (NGS) data, and further prioritized by their predicted binding to the class I or II major
histocompatibility complex (MHC) as an indicator of immunogenicity. Immunopeptidomic enrichment of the
peptide-MHC complex coupled with liquid chromatography tandem mass spectrometry (LC-MS/MS) can confirm
the existence of predicted neoantigens as well as other tumor-associated antigens (TAAs) derived from normal
protein sequences, including those with post-translational modifications (PTMs). This powerful approach
requires `immunopeptidogenomic' informatics tools that integrate NGS and MS data analysis. Despite steadily
growing numbers of cancer researchers pursuing these studies, they lack a centralized informatics resource
tailored to these informatics requirements. As a solution, we will develop the immunopeptidogenomic (iPepGen)
informatics resource for immuno-oncology research. iPepGen will leverage the Galaxy bioinformatics
ecosystem, offering cancer researchers accessible workflows to predict neoantigens from NGS data and confirm
their presence from MS-based immunopeptidomics data, including training resources housed in the Galaxy
Training Network to promote community adoption. We will achieve our goals through these Specific Aims: Aim
1: Optimize and harden modular workflows for identifying, prioritizing and curating neoantigen candidates
detected from genomic and/or transcriptomic alterations; Aim 2: Optimize and harden state-of-the-art MS-based
immunopeptidomic analysis modules for identifying and verifying MHC-bound neoantigen and TAA peptides;
Aim 3: Disseminate tested and optimized workflows and engage in training activities to promote community
adoption of the iPepGen resource. Our team brings complementary, world-class expertise necessary for
success in developing the iPepGen resource. PIs Griffin and Jagtap have developed widely used Galaxy-based
multi-omic tools and training materials for cancer research. PI Nesvizhskii is a world-leader in development of
computational tools for quantitative, MS-based proteomic and peptidomic analysis. Development, testing and
optimization of tools, workflows and training materials will be guided by collaboration with cancer researchers
conducting Driving Immuno-oncology Projects (DIPs). The iPepGen resource will offer a critically needed
resource to advance game-changing immunotherapy studies impacting a wide-variety of cancer types.

## Key facts

- **NIH application ID:** 10864686
- **Project number:** 1U01CA288888-01
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** TIMOTHY J. GRIFFIN
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $400,530
- **Award type:** 1
- **Project period:** 2024-06-01 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10864686

## Citation

> US National Institutes of Health, RePORTER application 10864686, The immunoPeptidoGenomic (iPepGen) informatics resource forimmuno-oncology research (1U01CA288888-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10864686. Licensed CC0.

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