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Classification of Estrogen Receptor-Positive Breast Cancer Based on Immunogenomic Profiling and Validation at Single-Cell Resolution

Background: The aim of this paper was to identify an immunotherapy-sensitive subtype for estrogen receptor-positive breast cancer (ER+ BC) patients by exploring the relationship between cancer genetic programs and antitumor immunity via multidimensional genome-scale analyses. Methods: Multidimension...

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Autores principales: Ma, Xianxiong, Chen, Hengyu, Yang, Ming, Ke, Zunxiang, Wang, Mengyi, Huang, Tao, Li, Lei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8490889/
https://www.ncbi.nlm.nih.gov/pubmed/34621742
http://dx.doi.org/10.3389/fcell.2021.722841
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author Ma, Xianxiong
Chen, Hengyu
Yang, Ming
Ke, Zunxiang
Wang, Mengyi
Huang, Tao
Li, Lei
author_facet Ma, Xianxiong
Chen, Hengyu
Yang, Ming
Ke, Zunxiang
Wang, Mengyi
Huang, Tao
Li, Lei
author_sort Ma, Xianxiong
collection PubMed
description Background: The aim of this paper was to identify an immunotherapy-sensitive subtype for estrogen receptor-positive breast cancer (ER+ BC) patients by exploring the relationship between cancer genetic programs and antitumor immunity via multidimensional genome-scale analyses. Methods: Multidimensional ER+ BC high-throughput data (raw count data) including gene expression profiles, copy number variation (CNV) data, single-nucleotide polymorphism mutation data, and relevant clinical information were downloaded from The Cancer Genome Atlas to explore an immune subtype sensitive to immunotherapy using the Consensus Cluster Plus algorithm based on multidimensional genome-scale analyses. One ArrayExpress dataset and eight Gene Expression Omnibus (GEO) datasets (GEO-meta dataset) as well as the Molecular Taxonomy of Breast Cancer International Consortium dataset were used as validation sets to confirm the findings regarding the immune profiles, mutational features, and survival outcomes of the three identified immune subtypes. Moreover, the development trajectory of ER+ BC patients from the single-cell resolution level was also explored. Results: Through comprehensive bioinformatics analysis, three immune subtypes of ER+ BC (C1, C2, and C3, designated the immune suppressive, activation, and neutral subtypes, respectively) were identified. C2 was associated with up-regulated immune cell signatures and immune checkpoint genes. Additionally, five tumor-related pathways (transforming growth factor, epithelial–mesenchymal transition, extracellular matrix, interferon-γ, and WNT signaling) tended to be more activated in C2 than in C1 and C3. Moreover, C2 was associated with a lower tumor mutation burden, a decreased neoantigen load, and fewer CNVs. Drug sensitivity analysis further showed that C2 may be more sensitive to immunosuppressive agents. Conclusion: C2 (the immune activation subtype) may be sensitive to immunotherapy, which provides new insights into effective treatment approaches for ER+ BC.
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spelling pubmed-84908892021-10-06 Classification of Estrogen Receptor-Positive Breast Cancer Based on Immunogenomic Profiling and Validation at Single-Cell Resolution Ma, Xianxiong Chen, Hengyu Yang, Ming Ke, Zunxiang Wang, Mengyi Huang, Tao Li, Lei Front Cell Dev Biol Cell and Developmental Biology Background: The aim of this paper was to identify an immunotherapy-sensitive subtype for estrogen receptor-positive breast cancer (ER+ BC) patients by exploring the relationship between cancer genetic programs and antitumor immunity via multidimensional genome-scale analyses. Methods: Multidimensional ER+ BC high-throughput data (raw count data) including gene expression profiles, copy number variation (CNV) data, single-nucleotide polymorphism mutation data, and relevant clinical information were downloaded from The Cancer Genome Atlas to explore an immune subtype sensitive to immunotherapy using the Consensus Cluster Plus algorithm based on multidimensional genome-scale analyses. One ArrayExpress dataset and eight Gene Expression Omnibus (GEO) datasets (GEO-meta dataset) as well as the Molecular Taxonomy of Breast Cancer International Consortium dataset were used as validation sets to confirm the findings regarding the immune profiles, mutational features, and survival outcomes of the three identified immune subtypes. Moreover, the development trajectory of ER+ BC patients from the single-cell resolution level was also explored. Results: Through comprehensive bioinformatics analysis, three immune subtypes of ER+ BC (C1, C2, and C3, designated the immune suppressive, activation, and neutral subtypes, respectively) were identified. C2 was associated with up-regulated immune cell signatures and immune checkpoint genes. Additionally, five tumor-related pathways (transforming growth factor, epithelial–mesenchymal transition, extracellular matrix, interferon-γ, and WNT signaling) tended to be more activated in C2 than in C1 and C3. Moreover, C2 was associated with a lower tumor mutation burden, a decreased neoantigen load, and fewer CNVs. Drug sensitivity analysis further showed that C2 may be more sensitive to immunosuppressive agents. Conclusion: C2 (the immune activation subtype) may be sensitive to immunotherapy, which provides new insights into effective treatment approaches for ER+ BC. Frontiers Media S.A. 2021-09-21 /pmc/articles/PMC8490889/ /pubmed/34621742 http://dx.doi.org/10.3389/fcell.2021.722841 Text en Copyright © 2021 Ma, Chen, Yang, Ke, Wang, Huang and Li. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cell and Developmental Biology
Ma, Xianxiong
Chen, Hengyu
Yang, Ming
Ke, Zunxiang
Wang, Mengyi
Huang, Tao
Li, Lei
Classification of Estrogen Receptor-Positive Breast Cancer Based on Immunogenomic Profiling and Validation at Single-Cell Resolution
title Classification of Estrogen Receptor-Positive Breast Cancer Based on Immunogenomic Profiling and Validation at Single-Cell Resolution
title_full Classification of Estrogen Receptor-Positive Breast Cancer Based on Immunogenomic Profiling and Validation at Single-Cell Resolution
title_fullStr Classification of Estrogen Receptor-Positive Breast Cancer Based on Immunogenomic Profiling and Validation at Single-Cell Resolution
title_full_unstemmed Classification of Estrogen Receptor-Positive Breast Cancer Based on Immunogenomic Profiling and Validation at Single-Cell Resolution
title_short Classification of Estrogen Receptor-Positive Breast Cancer Based on Immunogenomic Profiling and Validation at Single-Cell Resolution
title_sort classification of estrogen receptor-positive breast cancer based on immunogenomic profiling and validation at single-cell resolution
topic Cell and Developmental Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8490889/
https://www.ncbi.nlm.nih.gov/pubmed/34621742
http://dx.doi.org/10.3389/fcell.2021.722841
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