Abstract:
The identification of tumor-specific neoantigen (TSN) immunogenicity is crucial to develop peptide/mRNA based antitumoral vaccines and/or adoptive T cell immunotherapies. In silico immunogenicity prediction of candidate peptides is crucial to speed up the prioritization of such peptides for experimental validation. Up to now, several methods were proposed as TSN immunogenicity predictors, but there are still several drawbacks in both performance and comprehensive performance evaluation, mainly due to the absence of well documented and adequate TSN databases.The Immunogenic Tumor-Specific Neoantigen database (ITSNdb) is a tool developed to fairly benchmark immunogenicity predictors intended to be used over tumoral neopeptides. The proposed ITSNdb enables the analysis of immunogenicity without the interference of other variables such as binding affinity or peptide processing, as they were considered into the inclusion criteria for the curation of neoantigens. ITSNdb, together with a dataset emulating a true patient neoantigens scenario, as a validation strategy for prioritization, and a list of neopeptides predicted to bind to major histocompatibility complex I (MHC-I) from immune checkpoint blockade immunotherapy (ICB) cohorts, along with their associated patient outcomes, is available to evaluate tumor neoantigen burden as a biomarker for ICB response (accessible at https://github.com/elmerfer/ITSNdb).
