Evaluation of Computational Neoantigen Prioritization Strategies for Personalized Cancer Vaccines.
- 1Bioinformatics and Big Data Analysis Laboratory, N.N. Blokhin National Medical Research Center for Oncology, Moscow 115478, Russia.
- 2Research Center for Artificial Intelligence in Healthcare, N.N. Blokhin National Medical Research Center for Oncology, Moscow 115522, Russia.
Abstract
Background: Identification of immunogenic neoantigens remains one of the major computational bottlenecks limiting the development of effective personalized cancer vaccines. Although various computational methods have been proposed for neoantigen discovery, there is little independent evidence regarding which approaches most effectively prioritize vaccine candidates using experimentally validated datasets. We focused on an independent multi-cohort benchmark of widely used neoantigen discovery pipelines and neoantigen scoring tools using publicly available datasets with experimentally validated immunogenic peptide-Major Histocompatibility complexes (MHC). Methods: In order to evaluate computational strategies used for selection of candidate vaccine peptides, five end-to-end neoantigen prediction pipelines and seven neoantigen scoring and prioritization tools were independently benchmarked using experimentally validated neoantigen datasets. End-to-end pipelines were evaluated on the TESLA cohort using raw WES and RNA-seq data from five patients, while individual prediction tools were additionally assessed on the TESLA, NCI, and HiTIDE validation cohorts comprising experimentally verified immunogenic and non-immunogenic peptide-MHC complexes. Results: NeoHunter achieved the highest overall recovery of experimentally validated neoantigens among the evaluated pipelines, while the scoring and prioritization tools NetMHCpan, MixMHCpred, and MHCFlurry generally showed the strongest ranking performance, although their relative performance varied substantially between datasets and patients. Precision-recall, average precision, and top K analyses demonstrated that no single predictor consistently dominated across cohorts and revealed a clear trade-off between candidate list size, precision, and recovery of validated neoantigens. Analysis of clinically relevant candidate selection thresholds demonstrated that restricting vaccine design to only the highest-ranked peptides substantially reduces sensitivity, whereas broader candidate selection improves recovery at the expense of increased experimental burden. Conclusions: These findings provide practical recommendations for computational selection of vaccine targets and highlight current limitations that must be addressed to improve the success of personalized neoantigen-based vaccines.
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BibTeX
@article{zyrin2026evaluation,
title = {{Evaluation of Computational Neoantigen Prioritization Strategies for Personalized Cancer Vaccines.}},
author = {Vladimir Zyrin and Evgeniy Mozheiko and Ivan Valiev and Alexey Lazarev and Tigran Gevorkyan and Matvey Murashko and Konstantin Okonechnikov},
journal = {Vaccines},
year = {2026},
month = sep,
doi = {10.3390/vaccines14100857},
url = {https://europepmc.org/article/MED/42931386},
note = {CC BY},
}
RIS
TY - JOUR
TI - Evaluation of Computational Neoantigen Prioritization Strategies for Personalized Cancer Vaccines.
AU - Vladimir Zyrin
AU - Evgeniy Mozheiko
AU - Ivan Valiev
AU - Alexey Lazarev
AU - Tigran Gevorkyan
AU - Matvey Murashko
AU - Konstantin Okonechnikov
PY - 2026
DA - 2026/09/28
JO - Vaccines
DO - 10.3390/vaccines14100857
UR - https://europepmc.org/article/MED/42931386
AB - Background: Identification of immunogenic neoantigens remains one of the major computational bottlenecks limiting the development of effective personalized cancer vaccines. Although various computational methods have been proposed for neoantigen discovery, there is little independent evidence regarding which approaches most effectively prioritize vaccine candidates using experimentally validated datasets. We focused on an independent multi-cohort benchmark of widely used neoantigen discovery pipelines and neoantigen scoring tools using publicly available datasets with experimentally validated immunogenic peptide-Major Histocompatibility complexes (MHC). Methods: In order to evaluate computational strategies used for selection of candidate vaccine peptides, five end-to-end neoantigen prediction pipelines and seven neoantigen scoring and prioritization tools were independently benchmarked using experimentally validated neoantigen datasets. End-to-end pipelines were evaluated on the TESLA cohort using raw WES and RNA-seq data from five patients, while individual prediction tools were additionally assessed on the TESLA, NCI, and HiTIDE validation cohorts comprising experimentally verified immunogenic and non-immunogenic peptide-MHC complexes. Results: NeoHunter achieved the highest overall recovery of experimentally validated neoantigens among the evaluated pipelines, while the scoring and prioritization tools NetMHCpan, MixMHCpred, and MHCFlurry generally showed the strongest ranking performance, although their relative performance varied substantially between datasets and patients. Precision-recall, average precision, and top K analyses demonstrated that no single predictor consistently dominated across cohorts and revealed a clear trade-off between candidate list size, precision, and recovery of validated neoantigens. Analysis of clinically relevant candidate selection thresholds demonstrated that restricting vaccine design to only the highest-ranked peptides substantially reduces sensitivity, whereas broader candidate selection improves recovery at the expense of increased experimental burden. Conclusions: These findings provide practical recommendations for computational selection of vaccine targets and highlight current limitations that must be addressed to improve the success of personalized neoantigen-based vaccines.
SN - 2076-393X
ER -