Personalized neoantigen vaccination with synthetic long peptides
Year: 2016
Type: other
Abstract: Mutations in cancer may give rise to novel antigenic peptides, known as tumor neoantigens, that are promising targets for immunotherapy. However, due to the complexity and heterogeneity of most cancers, a single antigenic target is often insufficient for achieving a durable response or remission. Vaccines using single, short synthetic peptides are vulnerable to immune evasion or may even promote immune tolerance and/or anergy, leading to increased tumor growth. Cancer vaccination has moved in two promising directions: using more peptides and using longer peptides. Vaccines incorporating multiple peptides have been shown to be immunogenic and may reduce the risk of immune evasion. Separately, studies have demonstrated that vaccines which use synthetic long peptides do not appear to induce tolerance and may be more immunogenic than short peptide vaccines. We are developing an early-stage personalized cancer immunotherapy trial at Mount Sinai Hospital that combines these two research findings by treating each patient with up to twenty synthetic long peptides in a single vaccine. 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.
Institution: Icahn School of Medicine at Mount Sinai
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
OpenAlex ID: W2289543972