[HTML][HTML] Unraveling tumor specific neoantigen immunogenicity prediction: a comprehensive analysis
G Nibeyro, V Baronetto, JI Folco, P Pastore… - Frontiers in …, 2023 - frontiersin.org
G Nibeyro, V Baronetto, JI Folco, P Pastore, MR Girotti, L Prato, G Morón, HD Luján…
Frontiers in Immunology, 2023•frontiersin.orgIntroduction 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 …
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 …
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 presentation and positive/negative immune response (ITSNdb), sixteen metrics were evaluated as immunogenicity predictors. In addition, by using a dataset emulating patient derived TSNs and immunotherapy cohorts containing predicted TSNs for tumor neoantigen burden (TNB) with outcome association, the metrics were evaluated as TSNs prioritizers and as immunotherapy response biomarkers.
Results
Our results show high performance variability among methods, highlighting the need for substantial improvement. Deep learning predictors were top ranked on ITSNdb but show discrepancy on validation databases. In overall, current predicted TNB did not outperform existing biomarkers.
Conclusion
Recommendations for their clinical application and the ITSNdb are presented to promote development and comparison of computational TSNs immunogenicity predictors.
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