2021 AACR: Accurate modeling of antigen processing and MHC peptide presentation using large-scale immunopeptidomes and a novel machine learning framework

Neoantigens, which are antigens specific to cancer cells, can be harnessed to develop personalized cancer vaccines and prognostic biomarkers for checkpoint blockade inhibition. Next generation sequencing technologies have enabled comprehensive profiling of putative neoantigens by interrogating the tumor exome and transcriptome, but accurate prediction of peptides presented by MHC complexes remains a significant challenge. Here, we present Systematic HLA Epitope Ranking Pan Algorithm (SHERPA™) that addresses this critical need. SHERPA comprises highly sensitive, accurate and pan-allelicMHC-peptide (MHCp) binding and presentation prediction models, that were built using a multi-pronged strategy.

Our immuno-oncology platform (ImmunoID NeXT) enables researchers to analyze both a tumor and its microenvironment from a single tumor sample. In-depth interrogation of tumor and normal samples and identification of tumor-specific genomic events allows us to comprehensively profile the landscape of potential neoantigens, a critical aspect of precision neoantigen discovery.