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Classification of triple-negative breast cancers based on Immunogenomic profiling
BACKGROUND: Abundant evidence shows that triple-negative breast cancer (TNBC) is heterogeneous, and many efforts have been devoted to identifying TNBC subtypes on the basis of genomic profiling. However, few studies have explored the classification of TNBC specifically based on immune signatures tha...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6310928/ https://www.ncbi.nlm.nih.gov/pubmed/30594216 http://dx.doi.org/10.1186/s13046-018-1002-1 |
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author | He, Yin Jiang, Zehang Chen, Cai Wang, Xiaosheng |
author_facet | He, Yin Jiang, Zehang Chen, Cai Wang, Xiaosheng |
author_sort | He, Yin |
collection | PubMed |
description | BACKGROUND: Abundant evidence shows that triple-negative breast cancer (TNBC) is heterogeneous, and many efforts have been devoted to identifying TNBC subtypes on the basis of genomic profiling. However, few studies have explored the classification of TNBC specifically based on immune signatures that may facilitate the optimal stratification of TNBC patients responsive to immunotherapy. METHODS: Using four publicly available TNBC genomics datasets, we classified TNBC on the basis of the immunogenomic profiling of 29 immune signatures. Unsupervised and supervised machine learning methods were used to perform the classification. RESULTS: We identified three TNBC subtypes that we named Immunity High (Immunity_H), Immunity Medium (Immunity_M), and Immunity Low (Immunity_L) and demonstrated that this classification was reliable and predictable by analyzing multiple different datasets. Immunity_H was characterized by greater immune cell infiltration and anti-tumor immune activities, as well as better survival prognosis compared to the other subtypes. Besides the immune signatures, some cancer-associated pathways were hyperactivated in Immunity_H, including apoptosis, calcium signaling, MAPK signaling, PI3K–Akt signaling, and RAS signaling. In contrast, Immunity_L presented depressed immune signatures and increased activation of cell cycle, Hippo signaling, DNA replication, mismatch repair, cell adhesion molecule binding, spliceosome, adherens junction function, pyrimidine metabolism, glycosylphosphatidylinositol (GPI)-anchor biosynthesis, and RNA polymerase pathways. Furthermore, we identified a gene co-expression subnetwork centered around five transcription factor (TF) genes (CORO1A, STAT4, BCL11B, ZNF831, and EOMES) specifically significant in the Immunity_H subtype and a subnetwork centered around two TF genes (IRF8 and SPI1) characteristic of the Immunity_L subtype. CONCLUSIONS: The identification of TNBC subtypes based on immune signatures has potential clinical implications for TNBC treatment. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13046-018-1002-1) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6310928 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-63109282019-01-07 Classification of triple-negative breast cancers based on Immunogenomic profiling He, Yin Jiang, Zehang Chen, Cai Wang, Xiaosheng J Exp Clin Cancer Res Research BACKGROUND: Abundant evidence shows that triple-negative breast cancer (TNBC) is heterogeneous, and many efforts have been devoted to identifying TNBC subtypes on the basis of genomic profiling. However, few studies have explored the classification of TNBC specifically based on immune signatures that may facilitate the optimal stratification of TNBC patients responsive to immunotherapy. METHODS: Using four publicly available TNBC genomics datasets, we classified TNBC on the basis of the immunogenomic profiling of 29 immune signatures. Unsupervised and supervised machine learning methods were used to perform the classification. RESULTS: We identified three TNBC subtypes that we named Immunity High (Immunity_H), Immunity Medium (Immunity_M), and Immunity Low (Immunity_L) and demonstrated that this classification was reliable and predictable by analyzing multiple different datasets. Immunity_H was characterized by greater immune cell infiltration and anti-tumor immune activities, as well as better survival prognosis compared to the other subtypes. Besides the immune signatures, some cancer-associated pathways were hyperactivated in Immunity_H, including apoptosis, calcium signaling, MAPK signaling, PI3K–Akt signaling, and RAS signaling. In contrast, Immunity_L presented depressed immune signatures and increased activation of cell cycle, Hippo signaling, DNA replication, mismatch repair, cell adhesion molecule binding, spliceosome, adherens junction function, pyrimidine metabolism, glycosylphosphatidylinositol (GPI)-anchor biosynthesis, and RNA polymerase pathways. Furthermore, we identified a gene co-expression subnetwork centered around five transcription factor (TF) genes (CORO1A, STAT4, BCL11B, ZNF831, and EOMES) specifically significant in the Immunity_H subtype and a subnetwork centered around two TF genes (IRF8 and SPI1) characteristic of the Immunity_L subtype. CONCLUSIONS: The identification of TNBC subtypes based on immune signatures has potential clinical implications for TNBC treatment. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13046-018-1002-1) contains supplementary material, which is available to authorized users. BioMed Central 2018-12-29 /pmc/articles/PMC6310928/ /pubmed/30594216 http://dx.doi.org/10.1186/s13046-018-1002-1 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research He, Yin Jiang, Zehang Chen, Cai Wang, Xiaosheng Classification of triple-negative breast cancers based on Immunogenomic profiling |
title | Classification of triple-negative breast cancers based on Immunogenomic profiling |
title_full | Classification of triple-negative breast cancers based on Immunogenomic profiling |
title_fullStr | Classification of triple-negative breast cancers based on Immunogenomic profiling |
title_full_unstemmed | Classification of triple-negative breast cancers based on Immunogenomic profiling |
title_short | Classification of triple-negative breast cancers based on Immunogenomic profiling |
title_sort | classification of triple-negative breast cancers based on immunogenomic profiling |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6310928/ https://www.ncbi.nlm.nih.gov/pubmed/30594216 http://dx.doi.org/10.1186/s13046-018-1002-1 |
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