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Seq2Neo: A Comprehensive Pipeline for Cancer Neoantigen Immunogenicity Prediction

Neoantigens derived from somatic DNA alterations are ideal cancer-specific targets. In recent years, the combination therapy of PD-1/PD-L1 blockers and neoantigen vaccines has shown clinical efficacy in original PD-1/PD-L1 blocker non-responders. However, not all somatic DNA mutations result in immu...

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Autores principales: Diao, Kaixuan, Chen, Jing, Wu, Tao, Wang, Xuan, Wang, Guangshuai, Sun, Xiaoqin, Zhao, Xiangyu, Wu, Chenxu, Wang, Jinyu, Yao, Huizi, Gerarduzzi, Casimiro, Liu, Xue-Song
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9569519/
https://www.ncbi.nlm.nih.gov/pubmed/36232923
http://dx.doi.org/10.3390/ijms231911624
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author Diao, Kaixuan
Chen, Jing
Wu, Tao
Wang, Xuan
Wang, Guangshuai
Sun, Xiaoqin
Zhao, Xiangyu
Wu, Chenxu
Wang, Jinyu
Yao, Huizi
Gerarduzzi, Casimiro
Liu, Xue-Song
author_facet Diao, Kaixuan
Chen, Jing
Wu, Tao
Wang, Xuan
Wang, Guangshuai
Sun, Xiaoqin
Zhao, Xiangyu
Wu, Chenxu
Wang, Jinyu
Yao, Huizi
Gerarduzzi, Casimiro
Liu, Xue-Song
author_sort Diao, Kaixuan
collection PubMed
description Neoantigens derived from somatic DNA alterations are ideal cancer-specific targets. In recent years, the combination therapy of PD-1/PD-L1 blockers and neoantigen vaccines has shown clinical efficacy in original PD-1/PD-L1 blocker non-responders. However, not all somatic DNA mutations result in immunogenicity among cancer cells and efficient tools to predict the immunogenicity of neoepitopes are still urgently needed. Here, we present the Seq2Neo pipeline, which provides a one-stop solution for neoepitope feature prediction using raw sequencing data. Neoantigens derived from different types of genome DNA alterations, including point mutations, insertion deletions and gene fusions, are all supported. Importantly, a convolutional neural network (CNN)-based model was trained to predict the immunogenicity of neoepitopes and this model showed an improved performance compared to the currently available tools in immunogenicity prediction using independent datasets. We anticipate that the Seq2Neo pipeline could become a useful tool in the prediction of neoantigen immunogenicity and cancer immunotherapy. Seq2Neo is open-source software under an academic free license (AFL) v3.0 and is freely available at Github.
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spelling pubmed-95695192022-10-17 Seq2Neo: A Comprehensive Pipeline for Cancer Neoantigen Immunogenicity Prediction Diao, Kaixuan Chen, Jing Wu, Tao Wang, Xuan Wang, Guangshuai Sun, Xiaoqin Zhao, Xiangyu Wu, Chenxu Wang, Jinyu Yao, Huizi Gerarduzzi, Casimiro Liu, Xue-Song Int J Mol Sci Article Neoantigens derived from somatic DNA alterations are ideal cancer-specific targets. In recent years, the combination therapy of PD-1/PD-L1 blockers and neoantigen vaccines has shown clinical efficacy in original PD-1/PD-L1 blocker non-responders. However, not all somatic DNA mutations result in immunogenicity among cancer cells and efficient tools to predict the immunogenicity of neoepitopes are still urgently needed. Here, we present the Seq2Neo pipeline, which provides a one-stop solution for neoepitope feature prediction using raw sequencing data. Neoantigens derived from different types of genome DNA alterations, including point mutations, insertion deletions and gene fusions, are all supported. Importantly, a convolutional neural network (CNN)-based model was trained to predict the immunogenicity of neoepitopes and this model showed an improved performance compared to the currently available tools in immunogenicity prediction using independent datasets. We anticipate that the Seq2Neo pipeline could become a useful tool in the prediction of neoantigen immunogenicity and cancer immunotherapy. Seq2Neo is open-source software under an academic free license (AFL) v3.0 and is freely available at Github. MDPI 2022-10-01 /pmc/articles/PMC9569519/ /pubmed/36232923 http://dx.doi.org/10.3390/ijms231911624 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Diao, Kaixuan
Chen, Jing
Wu, Tao
Wang, Xuan
Wang, Guangshuai
Sun, Xiaoqin
Zhao, Xiangyu
Wu, Chenxu
Wang, Jinyu
Yao, Huizi
Gerarduzzi, Casimiro
Liu, Xue-Song
Seq2Neo: A Comprehensive Pipeline for Cancer Neoantigen Immunogenicity Prediction
title Seq2Neo: A Comprehensive Pipeline for Cancer Neoantigen Immunogenicity Prediction
title_full Seq2Neo: A Comprehensive Pipeline for Cancer Neoantigen Immunogenicity Prediction
title_fullStr Seq2Neo: A Comprehensive Pipeline for Cancer Neoantigen Immunogenicity Prediction
title_full_unstemmed Seq2Neo: A Comprehensive Pipeline for Cancer Neoantigen Immunogenicity Prediction
title_short Seq2Neo: A Comprehensive Pipeline for Cancer Neoantigen Immunogenicity Prediction
title_sort seq2neo: a comprehensive pipeline for cancer neoantigen immunogenicity prediction
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9569519/
https://www.ncbi.nlm.nih.gov/pubmed/36232923
http://dx.doi.org/10.3390/ijms231911624
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