A modular, Galaxy-based immunopeptidogenomic (iPepGen) analysis pipeline for discovery, verification, and prioritization of candidate cancer neoantigen peptides
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Background
Characterizing peptide antigens, processed from tumor-specific proteoforms, and bound to the major histocompatibility complex, is critical for immuno-oncology research. Next-generation sequencing predicts candidate neoantigen peptides derived from DNA mutations and/or RNA transcripts coding proteoform sequences that differ from the reference proteome. Mass spectrometry (MS)-based immunopeptidomics identifies predicted, MHC-bound neoantigen peptides and other tumor antigens. This “immunopeptidogenomic” approach requires multi-omic software integration, challenging researchers with limited bioinformatics expertise and resources. As a solution, we developed the immunopeptidogenomic (iPepGen) pipeline in the Galaxy ecosystem. iPepGen is composed of five core workflow modules, available via publicly accessible, scalable Galaxy instances, accompanied by training resources to empower community adoption.
Findings
Using representative multi-omic data from malignant peripheral nerve sheath tumors, we demonstrate the operation of iPepGen modules with these functions: 1) Predict neoantigen candidates from sequencing data and generate customized protein sequence databases, including reference and non-reference neoantigen candidate sequences; 2) Discover neoantigen peptide candidates by sequence database searching of tandem mass spectrometry (MS/MS) immunopeptidomics data; 3) Verify discovered peptide candidates through a secondary peptide-centric evaluation method against the MS/MS dataset; 4) Visualize and classify the nature of verified neoantigen peptides encoded by the genome and/or transcriptome; 5) Prioritize neoantigens for further exploration and empirical validation.
Conclusions
We demonstrate the effectiveness of the iPepGen pipeline for candidate neoantigen discovery and characterization. With tools, workflows, and training resources available in the open Galaxy ecosystem, iPepGen should provide cancer researchers with a flexible and accessible informatics resource tailored to accelerating immuno-oncology studies.
