Unraveling tumor specific neoantigen immunogenicity prediction: a comprehensive analysis
Guadalupe Nibeyro; Verónica M. Baronetto; Juan I. Folco; Pablo Pastore; María Romina Girotti; Laura Prato; Gabriel Morón; Hugo D. Luján · 2023 · Frontiers in Immunology
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Introduction: Identification of tumor specific neoantigen (TSN) immunogenicity is crucial to develop peptide/mRNA based anti-tumoral vaccines and/or adoptive T-cell immunotherapies; thus, accurate in-silico classification/prioritization proves critical for cost-effective clinical applications. Several methods were proposed as TSNs immunogenicity predictors; however, comprehensive performance comparison is still lacking due to the absence of well documented and adequate TSN databases. Methods: Here, by developing a new curated database having 199 TSNs with experimentally-validated MHC-I present
Abstract by Guadalupe Nibeyro; Verónica M. Baronetto; Juan I. Folco; Pablo Pastore; María Romina Girotti; Laura Prato; Gabriel Morón; Hugo D. Luján, Frontiers in Immunology (2023) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.3389/fimmu.2023.1094236