Seq2Neo: A Comprehensive Pipeline for Cancer Neoantigen Immunogenicity Prediction

doi:10.3390/ijms231911624

	Seq2Neo: A Comprehensive Pipeline for Cancer Neoantigen Immunogenicity Prediction
	Diao, Kaixuan1,2,3 ; Chen, Jing1,2,3 ; Wu, Tao1 ; Wang, Xuan1 ; Wang, Guangshuai1 ; Sun, Xiaoqin1 ; Zhao, Xiangyu1 ; Wu, Chenxu1 ; Wang, Jinyu1 ; Yao, Huizi1 ; Gerarduzzi, Casimiro 4; Liu, Xue-Song1
	2022-10
发表期刊	INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES (IF:4.9[JCR-2023],5.6[5-Year])
EISSN	1422-0067
卷号	23 期号:19
发表状态	已发表
DOI	10.3390/ijms231911624
摘要	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.
关键词	immunogenicity immunotherapy bioinformatics pipeline deep learning
URL	查看原文
收录类别	SCI ; SCIE
语种	英语
资助项目	Shanghai Science and Technology Commission[21ZR1442400] ; National Natural Science Foundation of China[31771373]
WOS研究方向	Biochemistry & Molecular Biology ; Chemistry
WOS类目	Biochemistry & Molecular Biology ; Chemistry, Multidisciplinary
WOS记录号	WOS:000867763400001
出版者	MDPI
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/240481
专题	生命科学与技术学院_硕士生生命科学与技术学院_PI研究组_刘雪松组生命科学与技术学院_博士生
通讯作者	Liu, Xue-Song
作者单位	1.ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai 201203, Peoples R China 2.Chinese Acad Sci, Shanghai Inst Biochem & Cell Biol, Shanghai 200031, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Univ Montreal, Fac Med, Dept Med, Montreal, PQ H4T 1G2, Canada
第一作者单位	生命科学与技术学院
通讯作者单位	生命科学与技术学院
第一作者的第一单位	生命科学与技术学院
推荐引用方式 GB/T 7714	Diao, Kaixuan,Chen, Jing,Wu, Tao,et al. Seq2Neo: A Comprehensive Pipeline for Cancer Neoantigen Immunogenicity Prediction[J]. INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES,2022,23(19).
APA	Diao, Kaixuan.,Chen, Jing.,Wu, Tao.,Wang, Xuan.,Wang, Guangshuai.,...&Liu, Xue-Song.(2022).Seq2Neo: A Comprehensive Pipeline for Cancer Neoantigen Immunogenicity Prediction.INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES,23(19).
MLA	Diao, Kaixuan,et al."Seq2Neo: A Comprehensive Pipeline for Cancer Neoantigen Immunogenicity Prediction".INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES 23.19(2022).