Neoantigens are mutated peptides that are expressed by tumor cells, but not by normal tissue. As a result, they can be recognized as foreign antigens by cells of the immune system — making them promising targets for personalized cancer immunotherapies such as vaccines and adoptive cell therapies. However, because neoantigens can arise from somatic mutations occurring in any gene in the genome, neoantigen identification requires both exome-scale DNA and RNA sequencing.
NeoantigenID, a suite of advanced analytics available via ImmunoID NeXT, leverages Personalis’ augmented exome and transcriptome for accurate identification of tumor-specific somatic mutations. These mutations are a rich source of putative neoantigens. Current computational neoantigen prediction algorithms suffer from limited training datasets and poor performance. To overcome these challenges, Personalis has developed a Systematic HLA Epitope Ranking Pan Algorithm (SHERPA™) to improve neoantigen presentation prediction based on MHC Class I binding potential, level of gene expression, and other key parameters. SHERPA is integrated into the NeoantigenID analytics engine to deliver a comprehensive characterization of putative neoantigens.