Personalized Neoantigen Vaccination with Synthetic Long Peptides

In order to select the contents of this highly multiplexed personalized vaccine, we have developed a computational pipeline to identify and prioritize candidate vaccine peptides. Our computational pipeline takes as input a patient’s tumor sequence data and HLA type, generating a set of therapeutic vaccine peptides. The first step in the pipeline is the processing of whole-exome DNA sequence data to determine somatic coding mutations. These mutations lead to candidate antigenic peptides that are filtered by tumor expression level using RNA sequence data. Candidate peptides are then ranked based on a scoring system that incorporates in-silico peptide-MHC binding affinity prediction and a novel immunogenicity predictor. This predictor estimates T cell recognition by computing the similarity of a candidate peptide to peptides in the self-ligandome of each HLA allele.