Introduction
Relying on physically identified peptides identified by the COD-dipp pipeline and database simplifies vaccine design. We develop a simple web resource to let anyone leverage these physical detections of T-Cell antigens idenfied in a growing compendium of immunopeptidomics datasets. By aligning detected MHC Class I peptides to the genome, we give the ability to study any mutation of interest in terms of it's directly observed MHC Class I antigens. Patient-specific or cancer-specific mutations can be overlayed with the ‘focal regions’ in our resource and immediately be understood in terms of their propensity to be presented in the population, the expression level to which they would be presented, and which previously detected peptides overlap these mutations are expected to be produced. Even the physically templated mutant neoantigens are returned for further investigation. For the technically inclined bioinformatics and AI community, all data and code is openly available and uniquely mapped across the genome, transcriptome and proteome. This data can be found in the data availability in the presented paper.
Neoantigen analysis
Neoantigen prediction informed by experimentally detected antigens shows a significant improvement over the fully in-silico approach when predicting response to immuno-therapy. Here, either input a VCF file of mutations or enter specific mutations of interest. Mutations overlapping phsically identified peptides, the expected mutant neoantigen and additional relevant information will be presented.
Predict neoantigens templated from physically detected MHC Class I peptides.
GIC analysis
Focal public neoantigens are sets of mutations from cancer-relevant hotspots that intersect with directly observed highly immune-visible regions in the genome. We first catalogued immune hotspots at the genome level while pairing them with a novel MHC haplotype deconvolution strategy. If the mutations of interest (provided as VCF or entered manually) overlap these highly immune-visible regions, they will be shown alongside the details of the region.
Intersect your mutations with highly immune-visible regions of the genome (GICs).
Please cite us using "A comprehensive library of canonical and non-canonical MHC class I antigens for cancer vaccine development."