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Increased expression of peptides from non-coding genes in cancer proteomics datasets suggests potential tumor neoantigens
Neoantigen-based immunotherapy has yielded promising results in clinical trials. However, it is limited to tumor-specific mutations, and is often tailored to individual patients. Identifying suitable tumor-specific antigens is still a major challenge. Previous proteogenomics studies have identified...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8062694/ https://www.ncbi.nlm.nih.gov/pubmed/33888849 http://dx.doi.org/10.1038/s42003-021-02007-2 |
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author | Xiang, Rong Ma, Leyao Yang, Mingyu Zheng, Zetian Chen, Xiaofang Jia, Fujian Xie, Fanfan Zhou, Yiming Li, Fuqiang Wu, Kui Zhu, Yafeng |
author_facet | Xiang, Rong Ma, Leyao Yang, Mingyu Zheng, Zetian Chen, Xiaofang Jia, Fujian Xie, Fanfan Zhou, Yiming Li, Fuqiang Wu, Kui Zhu, Yafeng |
author_sort | Xiang, Rong |
collection | PubMed |
description | Neoantigen-based immunotherapy has yielded promising results in clinical trials. However, it is limited to tumor-specific mutations, and is often tailored to individual patients. Identifying suitable tumor-specific antigens is still a major challenge. Previous proteogenomics studies have identified peptides encoded by predicted non-coding sequences in human genome. To investigate whether tumors express specific peptides encoded by non-coding genes, we analyzed published proteomics data from five cancer types including 933 tumor samples and 275 matched normal samples and compared these to data from 31 different healthy human tissues. Our results reveal that many predicted non-coding genes such as DGCR9 and RHOXF1P3 encode peptides that are overexpressed in tumors compared to normal controls. Furthermore, from the non-coding genes-encoded peptides specifically detected in cancers, we predict a large number of “dark antigens” (neoantigens from non-coding genomic regions), which may provide an alternative source of neoantigens beyond standard tumor specific mutations. |
format | Online Article Text |
id | pubmed-8062694 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-80626942021-05-05 Increased expression of peptides from non-coding genes in cancer proteomics datasets suggests potential tumor neoantigens Xiang, Rong Ma, Leyao Yang, Mingyu Zheng, Zetian Chen, Xiaofang Jia, Fujian Xie, Fanfan Zhou, Yiming Li, Fuqiang Wu, Kui Zhu, Yafeng Commun Biol Article Neoantigen-based immunotherapy has yielded promising results in clinical trials. However, it is limited to tumor-specific mutations, and is often tailored to individual patients. Identifying suitable tumor-specific antigens is still a major challenge. Previous proteogenomics studies have identified peptides encoded by predicted non-coding sequences in human genome. To investigate whether tumors express specific peptides encoded by non-coding genes, we analyzed published proteomics data from five cancer types including 933 tumor samples and 275 matched normal samples and compared these to data from 31 different healthy human tissues. Our results reveal that many predicted non-coding genes such as DGCR9 and RHOXF1P3 encode peptides that are overexpressed in tumors compared to normal controls. Furthermore, from the non-coding genes-encoded peptides specifically detected in cancers, we predict a large number of “dark antigens” (neoantigens from non-coding genomic regions), which may provide an alternative source of neoantigens beyond standard tumor specific mutations. Nature Publishing Group UK 2021-04-22 /pmc/articles/PMC8062694/ /pubmed/33888849 http://dx.doi.org/10.1038/s42003-021-02007-2 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Xiang, Rong Ma, Leyao Yang, Mingyu Zheng, Zetian Chen, Xiaofang Jia, Fujian Xie, Fanfan Zhou, Yiming Li, Fuqiang Wu, Kui Zhu, Yafeng Increased expression of peptides from non-coding genes in cancer proteomics datasets suggests potential tumor neoantigens |
title | Increased expression of peptides from non-coding genes in cancer proteomics datasets suggests potential tumor neoantigens |
title_full | Increased expression of peptides from non-coding genes in cancer proteomics datasets suggests potential tumor neoantigens |
title_fullStr | Increased expression of peptides from non-coding genes in cancer proteomics datasets suggests potential tumor neoantigens |
title_full_unstemmed | Increased expression of peptides from non-coding genes in cancer proteomics datasets suggests potential tumor neoantigens |
title_short | Increased expression of peptides from non-coding genes in cancer proteomics datasets suggests potential tumor neoantigens |
title_sort | increased expression of peptides from non-coding genes in cancer proteomics datasets suggests potential tumor neoantigens |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8062694/ https://www.ncbi.nlm.nih.gov/pubmed/33888849 http://dx.doi.org/10.1038/s42003-021-02007-2 |
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