Seq2Neo: a comprehensive pipeline for cancer neoantigen im-munogenicity prediction

doi:10.1101/2022.09.14.507872

	Seq2Neo: a comprehensive pipeline for cancer neoantigen im-munogenicity prediction
	Diao, K.1,3; Chen, J.3; Wu, T.1 ; Wang, X.1 ; Wang, G.1 ; Sun, X.1 ; Zhao, X.1 ; Wu, C.1 ; Wang, J.1 ; Yao, H.1; Gerarduzzi, C.4; Liu, X.-S.1
	2022-09-16
状态	已发表
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 shows clinical efficacy in original PD-1/PD-L1 blocker non-responders. However, not all somatic DNA mutations can result in immunogenicity in cancer cells, and efficient tools for predicting the immunogenicity of neoepitope are still urgently needed. Here we present the Seq2Neo pipeline, which provides a one-stop solution for neoepitope features prediction from raw sequencing data, and neoantigens derived from different types of genome DNA alterations, including point mutations, insertion deletions, and gene fusions are supported. Importantly a convolutional neural networks (CNN) based model has been trained to predict the immunogenicity of neoepitope. And this model shows improved performance compared with currently available tools in immunogenicity pre-diction in independent datasets. We anticipate that the Seq2Neo pipeline will become a useful tool in prediction of neoantigen immunogenicity and cancer immunotherapy. Seq2Neo is an open-source software under an academic free license (AFL) v3.0 and it is freely available at https://github.com/XSLiuLab/Seq2Neo.
关键词	immunogenicity immunotherapy bioinformatics pipelines deep learning
语种	英语
DOI	10.1101/2022.09.14.507872
相关网址	查看原文
出处	bioRxiv
收录类别	PPRN.PPRN
WOS记录号	PPRN:14761505
WOS类目	Computer Science, Interdisciplinary Applications
资助项目	Shanghai Science and Technology Commission["31771373","21ZR1442400"]
文献类型	预印本
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/348505
专题	生命科学与技术学院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_公共科研平台_高性能计算平台生命科学与技术学院_硕士生生命科学与技术学院_博士生生命科学与技术学院_本科生
通讯作者	Liu, X.-S.
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, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China 4.Univ Montreal, Fac Medecine, Dept Medecine, Montreal, PQ, Canada
推荐引用方式 GB/T 7714	Diao, K.,Chen, J.,Wu, T.,et al. Seq2Neo: a comprehensive pipeline for cancer neoantigen im-munogenicity prediction. 2022.