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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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/23/19/11624 |
| _version_ | 1797478946928852992 |
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| author | Kaixuan Diao Jing Chen Tao Wu Xuan Wang Guangshuai Wang Xiaoqin Sun Xiangyu Zhao Chenxu Wu Jinyu Wang Huizi Yao Casimiro Gerarduzzi Xue-Song Liu |
| author_facet | Kaixuan Diao Jing Chen Tao Wu Xuan Wang Guangshuai Wang Xiaoqin Sun Xiangyu Zhao Chenxu Wu Jinyu Wang Huizi Yao Casimiro Gerarduzzi Xue-Song Liu |
| author_sort | Kaixuan Diao |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T21:38:51Z |
| format | Article |
| id | doaj.art-0ca85d6c3550415a96015c3c2c04218c |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-09T21:38:51Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-0ca85d6c3550415a96015c3c2c04218c2023-11-23T20:36:28ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672022-10-0123191162410.3390/ijms231911624Seq2Neo: A Comprehensive Pipeline for Cancer Neoantigen Immunogenicity PredictionKaixuan Diao0Jing Chen1Tao Wu2Xuan Wang3Guangshuai Wang4Xiaoqin Sun5Xiangyu Zhao6Chenxu Wu7Jinyu Wang8Huizi Yao9Casimiro Gerarduzzi10Xue-Song Liu11School of Life Science and Technology, ShanghaiTech University, Shanghai 201203, ChinaSchool of Life Science and Technology, ShanghaiTech University, Shanghai 201203, ChinaSchool of Life Science and Technology, ShanghaiTech University, Shanghai 201203, ChinaSchool of Life Science and Technology, ShanghaiTech University, Shanghai 201203, ChinaSchool of Life Science and Technology, ShanghaiTech University, Shanghai 201203, ChinaSchool of Life Science and Technology, ShanghaiTech University, Shanghai 201203, ChinaSchool of Life Science and Technology, ShanghaiTech University, Shanghai 201203, ChinaSchool of Life Science and Technology, ShanghaiTech University, Shanghai 201203, ChinaSchool of Life Science and Technology, ShanghaiTech University, Shanghai 201203, ChinaSchool of Life Science and Technology, ShanghaiTech University, Shanghai 201203, ChinaDépartement de Médecine, Faculté de Médecine, Université de Montréal, Montréal, QC H4T 1G2, CanadaSchool of Life Science and Technology, ShanghaiTech University, Shanghai 201203, ChinaNeoantigens 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.https://www.mdpi.com/1422-0067/23/19/11624immunogenicityimmunotherapybioinformatics pipelinedeep learning |
| spellingShingle | Kaixuan Diao Jing Chen Tao Wu Xuan Wang Guangshuai Wang Xiaoqin Sun Xiangyu Zhao Chenxu Wu Jinyu Wang Huizi Yao Casimiro Gerarduzzi Xue-Song Liu Seq2Neo: A Comprehensive Pipeline for Cancer Neoantigen Immunogenicity Prediction International Journal of Molecular Sciences immunogenicity immunotherapy bioinformatics pipeline deep learning |
| 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 | immunogenicity immunotherapy bioinformatics pipeline deep learning |
| url | https://www.mdpi.com/1422-0067/23/19/11624 |
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