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. 2022 May 12:10:819583.
doi: 10.3389/fbioe.2022.819583. eCollection 2022.

CAD v1.0: Cancer Antigens Database Platform for Cancer Antigen Algorithm Development and Information Exploration

Jijun Yu  1   2 , Luoxuan Wang  3 , Xiangya Kong  4 , Yang Cao  5 , Mengmeng Zhang  1   6 , Zhaolin Sun  6 , Yang Liu  5 , Jing Wang  1   2 , Beifen Shen  1   2 , Xiaochen Bo  7 , Jiannan Feng  1   2
Affiliations

CAD v1.0: Cancer Antigens Database Platform for Cancer Antigen Algorithm Development and Information Exploration

Jijun Yu et al. Front Bioeng Biotechnol. .

Abstract

Cancer vaccines have gradually attracted attention for their tremendous preclinical and clinical performance. With the development of next-generation sequencing technologies and related algorithms, pipelines based on sequencing and machine learning methods have become mainstream in cancer antigen prediction; of particular focus are neoantigens, mutation peptides that only exist in tumor cells that lack central tolerance and have fewer side effects. The rapid prediction and filtering of neoantigen peptides are crucial to the development of neoantigen-based cancer vaccines. However, due to the lack of verified neoantigen datasets and insufficient research on the properties of neoantigens, neoantigen prediction algorithms still need to be improved. Here, we recruited verified cancer antigen peptides and collected as much relevant peptide information as possible. Then, we discussed the role of each dataset for algorithm improvement in cancer antigen research, especially neoantigen prediction. A platform, Cancer Antigens Database (CAD, http://cad.bio-it.cn/), was designed to facilitate users to perform a complete exploration of cancer antigens online.

Keywords: cancer antigen; neoantigen; prediction model; tumor-associated antigens (TAAs); tumor-specific antigens (TSAs).

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Conflict of interest statement

Author XK is employed by Beijing Geneworks Technology Co. and Author MZ is employed Beijing Capital Agribusiness Future Biotechnology Co. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Schematic diagram of data preprocessing and website architecture.
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References

    1. Ahn E., Kim H., Han K. T., Sin J.-I. (2015). A Loss of Antitumor Therapeutic Activity of CEA DNA Vaccines Is Associated with the Lack of Tumor Cells' Antigen Presentation to Ag-specific CTLs in a colon Cancer Model. Cancer Lett. 356, 676–685. 10.1016/j.canlet.2014.10.019 - DOI - PubMed
    1. Almeida L. G., Sakabe N. J., deOliveira A. R., Silva M. C. C., Mundstein A. S., Cohen T., et al. (2009). CTdatabase: a Knowledge-Base of High-Throughput and Curated Data on Cancer-Testis Antigens. Nucleic Acids Res. 37, D816–D819. 10.1093/nar/gkn673 - DOI - PMC - PubMed
    1. Andreatta M., Karosiene E., Rasmussen M., Stryhn A., Buus S., Nielsen M. (2015). Accurate Pan-specific Prediction of Peptide-MHC Class II Binding Affinity with Improved Binding Core Identification. Immunogenetics 67, 641–650. 10.1007/s00251-015-0873-y - DOI - PMC - PubMed
    1. Bagaev D. V., Vroomans R. M. A., Samir J., Stervbo U., Rius C., Dolton G., et al. (2020). VDJdb in 2019: Database Extension, New Analysis Infrastructure and a T-Cell Receptor Motif Compendium. Nucleic Acids Res. 48, D1057–D1062. 10.1093/nar/gkz874 - DOI - PMC - PubMed
    1. Balachandran V. P., Łuksza M., Łuksza M., Zhao J. N., Makarov V., Moral J. A., et al. (2017). Identification of Unique Neoantigen Qualities in Long-Term Survivors of Pancreatic Cancer. Nature 551, 512–516. 10.1038/nature24462 - DOI - PMC - PubMed