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Improvement of Neoantigen Identification Through Convolution Neural Network
Accurate prediction of neoantigens and the subsequent elicited protective anti-tumor response are particularly important for the development of cancer vaccine and adoptive T-cell therapy. However, current algorithms for predicting neoantigens are limited by in vitro binding affinity data and algorit...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8186784/ https://www.ncbi.nlm.nih.gov/pubmed/34113354 http://dx.doi.org/10.3389/fimmu.2021.682103 |
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author | Hao, Qing Wei, Ping Shu, Yang Zhang, Yi-Guan Xu, Heng Zhao, Jun-Ning |
author_facet | Hao, Qing Wei, Ping Shu, Yang Zhang, Yi-Guan Xu, Heng Zhao, Jun-Ning |
author_sort | Hao, Qing |
collection | PubMed |
description | Accurate prediction of neoantigens and the subsequent elicited protective anti-tumor response are particularly important for the development of cancer vaccine and adoptive T-cell therapy. However, current algorithms for predicting neoantigens are limited by in vitro binding affinity data and algorithmic constraints, inevitably resulting in high false positives. In this study, we proposed a deep convolutional neural network named APPM (antigen presentation prediction model) to predict antigen presentation in the context of human leukocyte antigen (HLA) class I alleles. APPM is trained on large mass spectrometry (MS) HLA-peptides datasets and evaluated with an independent MS benchmark. Results show that APPM outperforms the methods recommended by the immune epitope database (IEDB) in terms of positive predictive value (PPV) (0.40 vs. 0.22), which will further increase after combining these two approaches (PPV = 0.51). We further applied our model to the prediction of neoantigens from consensus driver mutations and identified 16,000 putative neoantigens with hallmarks of ‘drivers’. |
format | Online Article Text |
id | pubmed-8186784 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-81867842021-06-09 Improvement of Neoantigen Identification Through Convolution Neural Network Hao, Qing Wei, Ping Shu, Yang Zhang, Yi-Guan Xu, Heng Zhao, Jun-Ning Front Immunol Immunology Accurate prediction of neoantigens and the subsequent elicited protective anti-tumor response are particularly important for the development of cancer vaccine and adoptive T-cell therapy. However, current algorithms for predicting neoantigens are limited by in vitro binding affinity data and algorithmic constraints, inevitably resulting in high false positives. In this study, we proposed a deep convolutional neural network named APPM (antigen presentation prediction model) to predict antigen presentation in the context of human leukocyte antigen (HLA) class I alleles. APPM is trained on large mass spectrometry (MS) HLA-peptides datasets and evaluated with an independent MS benchmark. Results show that APPM outperforms the methods recommended by the immune epitope database (IEDB) in terms of positive predictive value (PPV) (0.40 vs. 0.22), which will further increase after combining these two approaches (PPV = 0.51). We further applied our model to the prediction of neoantigens from consensus driver mutations and identified 16,000 putative neoantigens with hallmarks of ‘drivers’. Frontiers Media S.A. 2021-05-25 /pmc/articles/PMC8186784/ /pubmed/34113354 http://dx.doi.org/10.3389/fimmu.2021.682103 Text en Copyright © 2021 Hao, Wei, Shu, Zhang, Xu and Zhao https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Immunology Hao, Qing Wei, Ping Shu, Yang Zhang, Yi-Guan Xu, Heng Zhao, Jun-Ning Improvement of Neoantigen Identification Through Convolution Neural Network |
title | Improvement of Neoantigen Identification Through Convolution Neural Network |
title_full | Improvement of Neoantigen Identification Through Convolution Neural Network |
title_fullStr | Improvement of Neoantigen Identification Through Convolution Neural Network |
title_full_unstemmed | Improvement of Neoantigen Identification Through Convolution Neural Network |
title_short | Improvement of Neoantigen Identification Through Convolution Neural Network |
title_sort | improvement of neoantigen identification through convolution neural network |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8186784/ https://www.ncbi.nlm.nih.gov/pubmed/34113354 http://dx.doi.org/10.3389/fimmu.2021.682103 |
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