COD-dipp

COD-dipp maps MHC Class I immunopeptidomic data to the genome to identify canonical and non-canonical (non-exonic) antigens and characterize immune-visible regions for cancer vaccine design and immunotherapy response prediction.

Key Features:

• Genomic mapping: Maps MHC Class I antigens, including canonical and non-canonical peptides, across a dataset of 26 cancer studies.
• Immune-visible region catalog: Identifies 140,966 highly immune-visible genomic regions annotated by expression and haplotype frequency and characterized by high conservation and frequent mutation in cancers.
• Enrichment of immunogenicity: Reports that identified immune-visible regions exhibit a 7.8-fold higher immunogenic potential compared to typical regions.
• Predictive intersection: Intersects pan-cancer mutations with immune-surveilled genomic regions to prioritize mutations overlapping physically identified antigens for immunotherapy response prediction.
• Public neoantigen–based vaccine framework: Provides a framework for leveraging public neoantigens to inform off-the-shelf multi-epitope cancer vaccine design.

Scientific Applications:

• Cancer immunotherapy research: Enables analysis of physically identified MHC Class I antigens to study tumor immune recognition across cancers.
• Cancer vaccine development: Supports selection and prioritization of antigenic regions and public neoantigens for multi-epitope vaccine constructs.
• Personalized immunotherapy prediction: Assists prediction of patient responses to immunotherapy by focusing on mutations overlapping immune-visible regions.
• Neoantigen prioritization: Facilitates ranking of tumor mutations by overlap with experimentally observed peptides and annotated immune visibility.

Methodology:

Integrates large-scale immunopeptidomic data with genomic information from multiple cancer studies to map MHC Class I antigens (canonical and non-canonical), annotates 140,966 immune-visible regions by expression and haplotype frequency, and intersects pan-cancer mutations with these regions for prioritization.