pTuneos: prioritizing tumor neoantigens from next-generation sequencing data

Abstract Background Cancer neoantigens are expressed only in cancer cells and presented on the tumor cell surface in complex with major histocompatibility complex (MHC) class I proteins for recognition by cytotoxic T cells. Accurate and rapid identification of neoantigens play a pivotal role in canc...

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Main Authors: Chi Zhou, Zhiting Wei, Zhanbing Zhang, Biyu Zhang, Chenyu Zhu, Ke Chen, Guohui Chuai, Sheng Qu, Lu Xie, Yong Gao, Qi Liu
Format: Article
Language:English
Published: BMC 2019-10-01
Series:Genome Medicine
Subjects:
Online Access:http://link.springer.com/article/10.1186/s13073-019-0679-x
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spelling doaj-b1ad6210fb1d4cfa9619f2f1495693162020-11-25T03:36:54ZengBMCGenome Medicine1756-994X2019-10-0111111710.1186/s13073-019-0679-xpTuneos: prioritizing tumor neoantigens from next-generation sequencing dataChi Zhou0Zhiting Wei1Zhanbing Zhang2Biyu Zhang3Chenyu Zhu4Ke Chen5Guohui Chuai6Sheng Qu7Lu Xie8Yong Gao9Qi Liu10Department of Endocrinology & Metabolism, Shanghai Tenth People’s Hospital; Bioinformatics Department, School of Life Sciences and Technology, Tongji UniversityDepartment of Endocrinology & Metabolism, Shanghai Tenth People’s Hospital; Bioinformatics Department, School of Life Sciences and Technology, Tongji UniversityDepartment of Endocrinology & Metabolism, Shanghai Tenth People’s Hospital; Bioinformatics Department, School of Life Sciences and Technology, Tongji UniversityDepartment of Endocrinology & Metabolism, Shanghai Tenth People’s Hospital; Bioinformatics Department, School of Life Sciences and Technology, Tongji UniversityDepartment of Endocrinology & Metabolism, Shanghai Tenth People’s Hospital; Bioinformatics Department, School of Life Sciences and Technology, Tongji UniversityDepartment of Endocrinology & Metabolism, Shanghai Tenth People’s Hospital; Bioinformatics Department, School of Life Sciences and Technology, Tongji UniversityDepartment of Endocrinology & Metabolism, Shanghai Tenth People’s Hospital; Bioinformatics Department, School of Life Sciences and Technology, Tongji UniversityDepartment of Endocrinology & Metabolism, Shanghai Tenth People’s Hospital; Bioinformatics Department, School of Life Sciences and Technology, Tongji UniversityShanghai Center for Bioinformation TechnologyDepartment of Digestive Oncology, Shanghai East Hospital, Tongji UniversityDepartment of Endocrinology & Metabolism, Shanghai Tenth People’s Hospital; Bioinformatics Department, School of Life Sciences and Technology, Tongji UniversityAbstract Background Cancer neoantigens are expressed only in cancer cells and presented on the tumor cell surface in complex with major histocompatibility complex (MHC) class I proteins for recognition by cytotoxic T cells. Accurate and rapid identification of neoantigens play a pivotal role in cancer immunotherapy. Although several in silico tools for neoantigen prediction have been presented, limitations of these tools exist. Results We developed pTuneos, a computational pipeline for prioritizing tumor neoantigens from next-generation sequencing data. We tested the performance of pTuneos on the melanoma cancer vaccine cohort data and tumor-infiltrating lymphocyte (TIL)-recognized neopeptide data. pTuneos is able to predict the MHC presentation and T cell recognition ability of the candidate neoantigens, and the actual immunogenicity of single-nucleotide variant (SNV)-based neopeptides considering their natural processing and presentation, surpassing the existing tools with a comprehensive and quantitative benchmark of their neoantigen prioritization performance and running time. pTuneos was further tested on The Cancer Genome Atlas (TCGA) cohort data as well as the melanoma and non-small cell lung cancer (NSCLC) cohort data undergoing checkpoint blockade immunotherapy. The overall neoantigen immunogenicity score proposed by pTuneos is demonstrated to be a powerful and pan-cancer marker for survival prediction compared to traditional well-established biomarkers. Conclusions In summary, pTuneos provides the state-of-the-art one-stop and user-friendly solution for prioritizing SNV-based candidate neoepitopes, which could help to advance research on next-generation cancer immunotherapies and personalized cancer vaccines. pTuneos is available at https://github.com/bm2-lab/pTuneos, with a Docker version for quick deployment at https://cloud.docker.com/u/bm2lab/repository/docker/bm2lab/ptuneos.http://link.springer.com/article/10.1186/s13073-019-0679-xCancer neoantigenNext-generation sequencingImmune checkpoint blockadeBiomarkerImmunotherapy
collection DOAJ
language English
format Article
sources DOAJ
author Chi Zhou
Zhiting Wei
Zhanbing Zhang
Biyu Zhang
Chenyu Zhu
Ke Chen
Guohui Chuai
Sheng Qu
Lu Xie
Yong Gao
Qi Liu
spellingShingle Chi Zhou
Zhiting Wei
Zhanbing Zhang
Biyu Zhang
Chenyu Zhu
Ke Chen
Guohui Chuai
Sheng Qu
Lu Xie
Yong Gao
Qi Liu
pTuneos: prioritizing tumor neoantigens from next-generation sequencing data
Genome Medicine
Cancer neoantigen
Next-generation sequencing
Immune checkpoint blockade
Biomarker
Immunotherapy
author_facet Chi Zhou
Zhiting Wei
Zhanbing Zhang
Biyu Zhang
Chenyu Zhu
Ke Chen
Guohui Chuai
Sheng Qu
Lu Xie
Yong Gao
Qi Liu
author_sort Chi Zhou
title pTuneos: prioritizing tumor neoantigens from next-generation sequencing data
title_short pTuneos: prioritizing tumor neoantigens from next-generation sequencing data
title_full pTuneos: prioritizing tumor neoantigens from next-generation sequencing data
title_fullStr pTuneos: prioritizing tumor neoantigens from next-generation sequencing data
title_full_unstemmed pTuneos: prioritizing tumor neoantigens from next-generation sequencing data
title_sort ptuneos: prioritizing tumor neoantigens from next-generation sequencing data
publisher BMC
series Genome Medicine
issn 1756-994X
publishDate 2019-10-01
description Abstract Background Cancer neoantigens are expressed only in cancer cells and presented on the tumor cell surface in complex with major histocompatibility complex (MHC) class I proteins for recognition by cytotoxic T cells. Accurate and rapid identification of neoantigens play a pivotal role in cancer immunotherapy. Although several in silico tools for neoantigen prediction have been presented, limitations of these tools exist. Results We developed pTuneos, a computational pipeline for prioritizing tumor neoantigens from next-generation sequencing data. We tested the performance of pTuneos on the melanoma cancer vaccine cohort data and tumor-infiltrating lymphocyte (TIL)-recognized neopeptide data. pTuneos is able to predict the MHC presentation and T cell recognition ability of the candidate neoantigens, and the actual immunogenicity of single-nucleotide variant (SNV)-based neopeptides considering their natural processing and presentation, surpassing the existing tools with a comprehensive and quantitative benchmark of their neoantigen prioritization performance and running time. pTuneos was further tested on The Cancer Genome Atlas (TCGA) cohort data as well as the melanoma and non-small cell lung cancer (NSCLC) cohort data undergoing checkpoint blockade immunotherapy. The overall neoantigen immunogenicity score proposed by pTuneos is demonstrated to be a powerful and pan-cancer marker for survival prediction compared to traditional well-established biomarkers. Conclusions In summary, pTuneos provides the state-of-the-art one-stop and user-friendly solution for prioritizing SNV-based candidate neoepitopes, which could help to advance research on next-generation cancer immunotherapies and personalized cancer vaccines. pTuneos is available at https://github.com/bm2-lab/pTuneos, with a Docker version for quick deployment at https://cloud.docker.com/u/bm2lab/repository/docker/bm2lab/ptuneos.
topic Cancer neoantigen
Next-generation sequencing
Immune checkpoint blockade
Biomarker
Immunotherapy
url http://link.springer.com/article/10.1186/s13073-019-0679-x
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