The immunoPeptidoGenomic (iPepGen) informatics resource forimmuno-oncology research

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.