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Intrinsic immune evasion patterns predict temozolomide sensitivity and immunotherapy response in lower-grade gliomas
BACKGROUND: Although intrinsic immune-evasion is important in cancer proliferation, metastasis and response to treatment, it is unclear whether intrinsic immune-evasion patterns of gliomas can aid in predicting clinical prognosis and determining treatment. METHODS: A total of 182 immune-evasion gene...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9465887/ https://www.ncbi.nlm.nih.gov/pubmed/36096781 http://dx.doi.org/10.1186/s12885-022-09984-5 |
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author | Tu, Zewei Ji, Qiankun Han, Qing Long, Xiaoyan Li, Jingying Wu, Lei Huang, Kai Zhu, Xingen |
author_facet | Tu, Zewei Ji, Qiankun Han, Qing Long, Xiaoyan Li, Jingying Wu, Lei Huang, Kai Zhu, Xingen |
author_sort | Tu, Zewei |
collection | PubMed |
description | BACKGROUND: Although intrinsic immune-evasion is important in cancer proliferation, metastasis and response to treatment, it is unclear whether intrinsic immune-evasion patterns of gliomas can aid in predicting clinical prognosis and determining treatment. METHODS: A total of 182 immune-evasion genes intrinsic to cancer were subjected to consensus clustering to identify immune-evasion patterns in 1421 patients with lower-grade glioma (LGG). The levels of each cancer hallmark were determined by the Gene Set Variant Analysis (GSVA) method, and immune cell infiltrations were quantified using two algorithms, the single-sample Gene Set Enrichment Analysis (ssGSEA) and the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) methods. IEVscore was determined by a method that combined univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO) regression and principal component analysis (PCA). RESULTS: Transcriptional and genomic analysis showed that most immune evasion genes (IEVGs) were upregulated in LGGs, with aberrant expression driven by alterations in copy number variants (CNV). Based on the mRNA expression profiles of cancer-intrinsic IEVGs could be divided into three LGG subgroups with distinct prognosis, clinicopathological features and immune infiltrations. A combined scoring scheme designed to assess the immune-evasion levels of LGGs divided these 1421 patients into two subgroups that differed in IEVscores. LGG patients with low-IEVscore had a better prognosis, would be more likely to benefit from immune check-point inhibitors and would be more susceptible to temozolomide (TMZ) chemotherapy. CONCLUSION: Intrinsic immune evasion in the tumor microenvironment (TME) has a crucial effect on glioma formation. Quantitatively assessing the IEV scores of individual LGG patients could enhance knowledge about the intra-glioma microenvironment and lead to the development of individualized therapeutic strategies for patients with LGG. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-022-09984-5. |
format | Online Article Text |
id | pubmed-9465887 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-94658872022-09-13 Intrinsic immune evasion patterns predict temozolomide sensitivity and immunotherapy response in lower-grade gliomas Tu, Zewei Ji, Qiankun Han, Qing Long, Xiaoyan Li, Jingying Wu, Lei Huang, Kai Zhu, Xingen BMC Cancer Research BACKGROUND: Although intrinsic immune-evasion is important in cancer proliferation, metastasis and response to treatment, it is unclear whether intrinsic immune-evasion patterns of gliomas can aid in predicting clinical prognosis and determining treatment. METHODS: A total of 182 immune-evasion genes intrinsic to cancer were subjected to consensus clustering to identify immune-evasion patterns in 1421 patients with lower-grade glioma (LGG). The levels of each cancer hallmark were determined by the Gene Set Variant Analysis (GSVA) method, and immune cell infiltrations were quantified using two algorithms, the single-sample Gene Set Enrichment Analysis (ssGSEA) and the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) methods. IEVscore was determined by a method that combined univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO) regression and principal component analysis (PCA). RESULTS: Transcriptional and genomic analysis showed that most immune evasion genes (IEVGs) were upregulated in LGGs, with aberrant expression driven by alterations in copy number variants (CNV). Based on the mRNA expression profiles of cancer-intrinsic IEVGs could be divided into three LGG subgroups with distinct prognosis, clinicopathological features and immune infiltrations. A combined scoring scheme designed to assess the immune-evasion levels of LGGs divided these 1421 patients into two subgroups that differed in IEVscores. LGG patients with low-IEVscore had a better prognosis, would be more likely to benefit from immune check-point inhibitors and would be more susceptible to temozolomide (TMZ) chemotherapy. CONCLUSION: Intrinsic immune evasion in the tumor microenvironment (TME) has a crucial effect on glioma formation. Quantitatively assessing the IEV scores of individual LGG patients could enhance knowledge about the intra-glioma microenvironment and lead to the development of individualized therapeutic strategies for patients with LGG. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-022-09984-5. BioMed Central 2022-09-12 /pmc/articles/PMC9465887/ /pubmed/36096781 http://dx.doi.org/10.1186/s12885-022-09984-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Tu, Zewei Ji, Qiankun Han, Qing Long, Xiaoyan Li, Jingying Wu, Lei Huang, Kai Zhu, Xingen Intrinsic immune evasion patterns predict temozolomide sensitivity and immunotherapy response in lower-grade gliomas |
title | Intrinsic immune evasion patterns predict temozolomide sensitivity and immunotherapy response in lower-grade gliomas |
title_full | Intrinsic immune evasion patterns predict temozolomide sensitivity and immunotherapy response in lower-grade gliomas |
title_fullStr | Intrinsic immune evasion patterns predict temozolomide sensitivity and immunotherapy response in lower-grade gliomas |
title_full_unstemmed | Intrinsic immune evasion patterns predict temozolomide sensitivity and immunotherapy response in lower-grade gliomas |
title_short | Intrinsic immune evasion patterns predict temozolomide sensitivity and immunotherapy response in lower-grade gliomas |
title_sort | intrinsic immune evasion patterns predict temozolomide sensitivity and immunotherapy response in lower-grade gliomas |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9465887/ https://www.ncbi.nlm.nih.gov/pubmed/36096781 http://dx.doi.org/10.1186/s12885-022-09984-5 |
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