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CIBERSORT analysis of TCGA and METABRIC identifies subgroups with better outcomes in triple negative breast cancer
Studies have shown that the presence of tumor infiltrating lymphocytes (TILs) in Triple Negative Breast Cancer (TNBC) is associated with better prognosis. However, the molecular mechanisms underlying these immune cell differences are not well delineated. In this study, analysis of hematoxylin and eo...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7907367/ https://www.ncbi.nlm.nih.gov/pubmed/33633150 http://dx.doi.org/10.1038/s41598-021-83913-7 |
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author | Craven, Kelly E. Gökmen-Polar, Yesim Badve, Sunil S. |
author_facet | Craven, Kelly E. Gökmen-Polar, Yesim Badve, Sunil S. |
author_sort | Craven, Kelly E. |
collection | PubMed |
description | Studies have shown that the presence of tumor infiltrating lymphocytes (TILs) in Triple Negative Breast Cancer (TNBC) is associated with better prognosis. However, the molecular mechanisms underlying these immune cell differences are not well delineated. In this study, analysis of hematoxylin and eosin images from The Cancer Genome Atlas (TCGA) breast cancer cohort failed to show a prognostic benefit of TILs in TNBC, whereas CIBERSORT analysis, which quantifies the proportion of each immune cell type, demonstrated improved overall survival in TCGA TNBC samples with increased CD8 T cells or CD8 plus CD4 memory activated T cells and in Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) TNBC samples with increased gamma delta T cells. Twenty-five genes showed mutational frequency differences between the TCGA high and low T cell groups, and many play important roles in inflammation or immune evasion (ATG2B, HIST1H2BC, PKD1, PIKFYVE, TLR3, NOTCH3, GOLGB1, CREBBP). Identification of these mutations suggests novel mechanisms by which the cancer cells attract immune cells and by which they evade or dampen the immune system during the cancer immunoediting process. This study suggests that integration of mutations with CIBERSORT analysis could provide better prediction of outcomes and novel therapeutic targets in TNBC cases. |
format | Online Article Text |
id | pubmed-7907367 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-79073672021-03-02 CIBERSORT analysis of TCGA and METABRIC identifies subgroups with better outcomes in triple negative breast cancer Craven, Kelly E. Gökmen-Polar, Yesim Badve, Sunil S. Sci Rep Article Studies have shown that the presence of tumor infiltrating lymphocytes (TILs) in Triple Negative Breast Cancer (TNBC) is associated with better prognosis. However, the molecular mechanisms underlying these immune cell differences are not well delineated. In this study, analysis of hematoxylin and eosin images from The Cancer Genome Atlas (TCGA) breast cancer cohort failed to show a prognostic benefit of TILs in TNBC, whereas CIBERSORT analysis, which quantifies the proportion of each immune cell type, demonstrated improved overall survival in TCGA TNBC samples with increased CD8 T cells or CD8 plus CD4 memory activated T cells and in Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) TNBC samples with increased gamma delta T cells. Twenty-five genes showed mutational frequency differences between the TCGA high and low T cell groups, and many play important roles in inflammation or immune evasion (ATG2B, HIST1H2BC, PKD1, PIKFYVE, TLR3, NOTCH3, GOLGB1, CREBBP). Identification of these mutations suggests novel mechanisms by which the cancer cells attract immune cells and by which they evade or dampen the immune system during the cancer immunoediting process. This study suggests that integration of mutations with CIBERSORT analysis could provide better prediction of outcomes and novel therapeutic targets in TNBC cases. Nature Publishing Group UK 2021-02-25 /pmc/articles/PMC7907367/ /pubmed/33633150 http://dx.doi.org/10.1038/s41598-021-83913-7 Text en © The Author(s) 2021 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/. |
spellingShingle | Article Craven, Kelly E. Gökmen-Polar, Yesim Badve, Sunil S. CIBERSORT analysis of TCGA and METABRIC identifies subgroups with better outcomes in triple negative breast cancer |
title | CIBERSORT analysis of TCGA and METABRIC identifies subgroups with better outcomes in triple negative breast cancer |
title_full | CIBERSORT analysis of TCGA and METABRIC identifies subgroups with better outcomes in triple negative breast cancer |
title_fullStr | CIBERSORT analysis of TCGA and METABRIC identifies subgroups with better outcomes in triple negative breast cancer |
title_full_unstemmed | CIBERSORT analysis of TCGA and METABRIC identifies subgroups with better outcomes in triple negative breast cancer |
title_short | CIBERSORT analysis of TCGA and METABRIC identifies subgroups with better outcomes in triple negative breast cancer |
title_sort | cibersort analysis of tcga and metabric identifies subgroups with better outcomes in triple negative breast cancer |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7907367/ https://www.ncbi.nlm.nih.gov/pubmed/33633150 http://dx.doi.org/10.1038/s41598-021-83913-7 |
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