NeoAtlas-Tumor and NeoBert: A Database and A Predictive Model for Canonical and Noncanonical Tumor Neoantigens

Neoantigens are classified into canonical and noncanonical types. Noncanonical neoantigens include those derived from noncoding regions, transposable elements (TE), and intron retention events, and they have recently gained considerable attention in cancer immunity. In this study, we focused on neoantigens presented by HLA class I molecules, which are central to CD8+ T cell-mediated immune responses. We curated 39,347 non-redundant neoantigen-HLA pairs from 14 immunopeptidomes studies, by analyzing unique features and differences across various sources of neoantigens. This knowledge enabled us to develop machine learning models for the prediction of different types of neoantigens. Our data and models are available at a public portal (https://ngdc.cncb.ac.cn/neoatlas) to facilitate broad access and future research. This resource offers advanced functionalities, including integration with epigenome browsers which allow easy navigation of epigenomic datasets to support and confirm the expression of neoantigens. We further demonstrate that combining our database with mass spectrometry analysis can identify noncanonical neoantigens. The resource we constructed holds significant value and promise for the development of neoantigen-based vaccines. All data, machine learning models, and analytical tools are freely available at the NeoAtlas-Tumor portal (https://ngdc.cncb.ac.cn/neoatlas).

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Publication Details

Journal
Genomics Proteomics & Bioinformatics
Published
2025-11-12
DOI
https://doi.org/10.1093/gpbjnl/qzaf105
Primary Topic
vaccines and immunoinformatics approaches
Type
article
Field-Weighted Citation Impact
0.00
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article

NeoAtlas-Tumor and NeoBert: A Database and A Predictive Model for Canonical and Noncanonical Tumor Neoantigens

Haobin Chen, Yizhuo Li, Gang Jin, Yīmíng Bào et al.
Genomics Proteomics & Bioinformatics
vaccines and immunoinformatics approaches
article

NeoAtlas-Tumor and NeoBert: A Database and A Predictive Model for Canonical and Noncanonical Tumor Neoantigens

Haobin Chen, Yizhuo Li, Gang Jin, Yīmíng Bào, Meilong Shi, Jing Li, Lingyun Xu, Yan Ren, Chunman Zuo, Fei Yang, Wei Zhao, Zhihui Yan, Fuxin Han, Qianyi Yan, Chuanqi Teng
article en

Abstract

Neoantigens are classified into canonical and noncanonical types. Noncanonical neoantigens include those derived from noncoding regions, transposable elements (TE), and intron retention events, and they have recently gained considerable attention in cancer immunity. In this study, we focused on neoantigens presented by HLA class I molecules, which are central to CD8+ T cell-mediated immune responses. We curated 39,347 non-redundant neoantigen-HLA pairs from 14 immunopeptidomes studies, by analyzing unique features and differences across various sources of neoantigens. This knowledge enabled us to develop machine learning models for the prediction of different types of neoantigens. Our data and models are available at a public portal (https://ngdc.cncb.ac.cn/neoatlas) to facilitate broad access and future research. This resource offers advanced functionalities, including integration with epigenome browsers which allow easy navigation of epigenomic datasets to support and confirm the expression of neoantigens. We further demonstrate that combining our database with mass spectrometry analysis can identify noncanonical neoantigens. The resource we constructed holds significant value and promise for the development of neoantigen-based vaccines. All data, machine learning models, and analytical tools are freely available at the NeoAtlas-Tumor portal (https://ngdc.cncb.ac.cn/neoatlas).

Genomics Proteomics & Bioinformatics
Sun Yat-sen University (CN), Second Military Medical University (CN), Donghua University (CN), Jilin University (CN), Chinese Academy of Sciences (CN), Wenzhou Medical University (CN), Beijing Institute of Genomics (CN), Shanghai University of Traditional Chinese Medicine (CN), Ministry of Education (IR), Zhejiang Academy of Social Sciences (CN), Changhai Hospital (CN), ZheJiang Academy of Agricultural Sciences (CN), Institute of Immunology (HR), PLA Navy General Hospital (CN), Institut de Biosciences et Biotechnologies (FR), Shanghai Cell Therapy Research Institute (CN), Shanghai Center For Bioinformation Technology (CN), University of Chinese Academy of Sciences (CN)
Openalex Percentile: Top 70%
vaccines and immunoinformatics approaches
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