An Analysis of Natural T Cell Responses to Predicted Tumor Neoepitopes
Barra, Carolina M.
Hadrup, Sine Reker
Eklund, Aron Charles
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CitationBjerregaard, Anne-Mette, Morten Nielsen, Vanessa Jurtz, Carolina M. Barra, Sine Reker Hadrup, Zoltan Szallasi, and Aron Charles Eklund. 2017. “An Analysis of Natural T Cell Responses to Predicted Tumor Neoepitopes.” Frontiers in Immunology 8 (1): 1566. doi:10.3389/fimmu.2017.01566. http://dx.doi.org/10.3389/fimmu.2017.01566.
AbstractPersonalization of cancer immunotherapies such as therapeutic vaccines and adoptive T-cell therapy may benefit from efficient identification and targeting of patient-specific neoepitopes. However, current neoepitope prediction methods based on sequencing and predictions of epitope processing and presentation result in a low rate of validation, suggesting that the determinants of peptide immunogenicity are not well understood. We gathered published data on human neopeptides originating from single amino acid substitutions for which T cell reactivity had been experimentally tested, including both immunogenic and non-immunogenic neopeptides. Out of 1,948 neopeptide-HLA (human leukocyte antigen) combinations from 13 publications, 53 were reported to elicit a T cell response. From these data, we found an enrichment for responses among peptides of length 9. Even though the peptides had been pre-selected based on presumed likelihood of being immunogenic, we found using NetMHCpan-4.0 that immunogenic neopeptides were predicted to bind significantly more strongly to HLA compared to non-immunogenic peptides. Investigation of the HLA binding strength of the immunogenic peptides revealed that the vast majority (96%) shared very strong predicted binding to HLA and that the binding strength was comparable to that observed for pathogen-derived epitopes. Finally, we found that neopeptide dissimilarity to self is a predictor of immunogenicity in situations where neo- and normal peptides share comparable predicted binding strength. In conclusion, these results suggest new strategies for prioritization of mutated peptides, but new data will be needed to confirm their value.
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