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A Machine Learning Model to Predict the Triple Negative Breast Cancer Immune Subtype
BACKGROUND: Immune checkpoint blockade (ICB) has been approved for the treatment of triple-negative breast cancer (TNBC), since it significantly improved the progression-free survival (PFS). However, only about 10% of TNBC patients could achieve the complete response (CR) to ICB because of the low r...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8484710/ https://www.ncbi.nlm.nih.gov/pubmed/34603338 http://dx.doi.org/10.3389/fimmu.2021.749459 |
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author | Chen, Zihao Wang, Maoli De Wilde, Rudy Leon Feng, Ruifa Su, Mingqiang Torres-de la Roche, Luz Angela Shi, Wenjie |
author_facet | Chen, Zihao Wang, Maoli De Wilde, Rudy Leon Feng, Ruifa Su, Mingqiang Torres-de la Roche, Luz Angela Shi, Wenjie |
author_sort | Chen, Zihao |
collection | PubMed |
description | BACKGROUND: Immune checkpoint blockade (ICB) has been approved for the treatment of triple-negative breast cancer (TNBC), since it significantly improved the progression-free survival (PFS). However, only about 10% of TNBC patients could achieve the complete response (CR) to ICB because of the low response rate and potential adverse reactions to ICB. METHODS: Open datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were downloaded to perform an unsupervised clustering analysis to identify the immune subtype according to the expression profiles. The prognosis, enriched pathways, and the ICB indicators were compared between immune subtypes. Afterward, samples from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset were used to validate the correlation of immune subtype with prognosis. Data from patients who received ICB were selected to validate the correlation of the immune subtype with ICB response. Machine learning models were used to build a visual web server to predict the immune subtype of TNBC patients requiring ICB. RESULTS: A total of eight open datasets including 931 TNBC samples were used for the unsupervised clustering. Two novel immune subtypes (referred to as S1 and S2) were identified among TNBC patients. Compared with S2, S1 was associated with higher immune scores, higher levels of immune cells, and a better prognosis for immunotherapy. In the validation dataset, subtype 1 samples had a better prognosis than sub type 2 samples, no matter in overall survival (OS) (p = 0.00036) or relapse-free survival (RFS) (p = 0.0022). Bioinformatics analysis identified 11 hub genes (LCK, IL2RG, CD3G, STAT1, CD247, IL2RB, CD3D, IRF1, OAS2, IRF4, and IFNG) related to the immune subtype. A robust machine learning model based on random forest algorithm was established by 11 hub genes, and it performed reasonably well with area Under the Curve of the receiver operating characteristic (AUC) values = 0.76. An open and free web server based on the random forest model, named as triple-negative breast cancer immune subtype (TNBCIS), was developed and is available from https://immunotypes.shinyapps.io/TNBCIS/. CONCLUSION: TNBC open datasets allowed us to stratify samples into distinct immunotherapy response subgroups according to gene expression profiles. Based on two novel subtypes, candidates for ICB with a higher response rate and better prognosis could be selected by using the free visual online web server that we designed. |
format | Online Article Text |
id | pubmed-8484710 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-84847102021-10-02 A Machine Learning Model to Predict the Triple Negative Breast Cancer Immune Subtype Chen, Zihao Wang, Maoli De Wilde, Rudy Leon Feng, Ruifa Su, Mingqiang Torres-de la Roche, Luz Angela Shi, Wenjie Front Immunol Immunology BACKGROUND: Immune checkpoint blockade (ICB) has been approved for the treatment of triple-negative breast cancer (TNBC), since it significantly improved the progression-free survival (PFS). However, only about 10% of TNBC patients could achieve the complete response (CR) to ICB because of the low response rate and potential adverse reactions to ICB. METHODS: Open datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were downloaded to perform an unsupervised clustering analysis to identify the immune subtype according to the expression profiles. The prognosis, enriched pathways, and the ICB indicators were compared between immune subtypes. Afterward, samples from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset were used to validate the correlation of immune subtype with prognosis. Data from patients who received ICB were selected to validate the correlation of the immune subtype with ICB response. Machine learning models were used to build a visual web server to predict the immune subtype of TNBC patients requiring ICB. RESULTS: A total of eight open datasets including 931 TNBC samples were used for the unsupervised clustering. Two novel immune subtypes (referred to as S1 and S2) were identified among TNBC patients. Compared with S2, S1 was associated with higher immune scores, higher levels of immune cells, and a better prognosis for immunotherapy. In the validation dataset, subtype 1 samples had a better prognosis than sub type 2 samples, no matter in overall survival (OS) (p = 0.00036) or relapse-free survival (RFS) (p = 0.0022). Bioinformatics analysis identified 11 hub genes (LCK, IL2RG, CD3G, STAT1, CD247, IL2RB, CD3D, IRF1, OAS2, IRF4, and IFNG) related to the immune subtype. A robust machine learning model based on random forest algorithm was established by 11 hub genes, and it performed reasonably well with area Under the Curve of the receiver operating characteristic (AUC) values = 0.76. An open and free web server based on the random forest model, named as triple-negative breast cancer immune subtype (TNBCIS), was developed and is available from https://immunotypes.shinyapps.io/TNBCIS/. CONCLUSION: TNBC open datasets allowed us to stratify samples into distinct immunotherapy response subgroups according to gene expression profiles. Based on two novel subtypes, candidates for ICB with a higher response rate and better prognosis could be selected by using the free visual online web server that we designed. Frontiers Media S.A. 2021-09-17 /pmc/articles/PMC8484710/ /pubmed/34603338 http://dx.doi.org/10.3389/fimmu.2021.749459 Text en Copyright © 2021 Chen, Wang, De Wilde, Feng, Su, Torres-de la Roche and Shi 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 | Immunology Chen, Zihao Wang, Maoli De Wilde, Rudy Leon Feng, Ruifa Su, Mingqiang Torres-de la Roche, Luz Angela Shi, Wenjie A Machine Learning Model to Predict the Triple Negative Breast Cancer Immune Subtype |
title | A Machine Learning Model to Predict the Triple Negative Breast Cancer Immune Subtype |
title_full | A Machine Learning Model to Predict the Triple Negative Breast Cancer Immune Subtype |
title_fullStr | A Machine Learning Model to Predict the Triple Negative Breast Cancer Immune Subtype |
title_full_unstemmed | A Machine Learning Model to Predict the Triple Negative Breast Cancer Immune Subtype |
title_short | A Machine Learning Model to Predict the Triple Negative Breast Cancer Immune Subtype |
title_sort | machine learning model to predict the triple negative breast cancer immune subtype |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8484710/ https://www.ncbi.nlm.nih.gov/pubmed/34603338 http://dx.doi.org/10.3389/fimmu.2021.749459 |
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