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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Bibliographic Details
Published in:International journal of molecular sciences 2022-10, Vol.23 (19), p.11624
Main Authors: 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
Format: Article
Language:English
Subjects:
Algorithms
Antigens
bioinformatics pipeline
Cancer
Cancer immunotherapy
Coding standards
Datasets
deep learning
Deoxyribonucleic acid
DNA
Efficiency
Freeware
Gene expression
Genomes
Immunogenicity
Immunotherapy
Machine learning
Mutation
Neoantigens
Neural networks
PD-1 protein
PD-L1 protein
Peptides
Performance evaluation
Predictions
Vaccines
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Summary: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.
ISSN:1422-0067
1661-6596
1422-0067
DOI:10.3390/ijms231911624