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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...

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Autores principales: Hao, Qing, Wei, Ping, Shu, Yang, Zhang, Yi-Guan, Xu, Heng, Zhao, Jun-Ning
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
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’.
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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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