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The expression profiles of signature genes from CD103(+)LAG3(+) tumour-infiltrating lymphocyte subsets predict breast cancer survival
BACKGROUND: Tumour-infiltrating lymphocytes (TILs), including T and B cells, have been demonstrated to be associated with tumour progression. However, the different subpopulations of TILs and their roles in breast cancer remain poorly understood. Large-scale analysis using multiomics data could unco...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10367329/ https://www.ncbi.nlm.nih.gov/pubmed/37488535 http://dx.doi.org/10.1186/s12916-023-02960-1 |
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author | Xia, Zi-An Lu, Can Pan, Can Li, Jia Li, Jun Mao, Yitao Sun, Lunquan He, Jiang |
author_facet | Xia, Zi-An Lu, Can Pan, Can Li, Jia Li, Jun Mao, Yitao Sun, Lunquan He, Jiang |
author_sort | Xia, Zi-An |
collection | PubMed |
description | BACKGROUND: Tumour-infiltrating lymphocytes (TILs), including T and B cells, have been demonstrated to be associated with tumour progression. However, the different subpopulations of TILs and their roles in breast cancer remain poorly understood. Large-scale analysis using multiomics data could uncover potential mechanisms and provide promising biomarkers for predicting immunotherapy response. METHODS: Single-cell transcriptome data for breast cancer samples were analysed to identify unique TIL subsets. Based on the expression profiles of marker genes in these subsets, a TIL-related prognostic model was developed by univariate and multivariate Cox analyses and LASSO regression for the TCGA training cohort containing 1089 breast cancer patients. Multiplex immunohistochemistry was used to confirm the presence of TIL subsets in breast cancer samples. The model was validated with a large-scale transcriptomic dataset for 3619 breast cancer patients, including the METABRIC cohort, six chemotherapy transcriptomic cohorts, and two immunotherapy transcriptomic cohorts. RESULTS: We identified two TIL subsets with high expression of CD103 and LAG3 (CD103(+)LAG3(+)), including a CD8(+) T-cell subset and a B-cell subset. Based on the expression profiles of marker genes in these two subpopulations, we further developed a CD103(+)LAG3(+) TIL-related prognostic model (CLTRP) based on CXCL13 and BIRC3 genes for predicting the prognosis of breast cancer patients. CLTRP-low patients had a better prognosis than CLTRP-high patients. The comprehensive results showed that a low CLTRP score was associated with a high TP53 mutation rate, high infiltration of CD8 T cells, helper T cells, and CD4 T cells, high sensitivity to chemotherapeutic drugs, and a good response to immunotherapy. In contrast, a high CLTRP score was correlated with a low TP53 mutation rate, high infiltration of M0 and M2 macrophages, low sensitivity to chemotherapeutic drugs, and a poor response to immunotherapy. CONCLUSIONS: Our present study showed that the CLTRP score is a promising biomarker for distinguishing prognosis, drug sensitivity, molecular and immune characteristics, and immunotherapy outcomes in breast cancer patients. The CLTRP could serve as a valuable tool for clinical decision making regarding immunotherapy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-023-02960-1. |
format | Online Article Text |
id | pubmed-10367329 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-103673292023-07-26 The expression profiles of signature genes from CD103(+)LAG3(+) tumour-infiltrating lymphocyte subsets predict breast cancer survival Xia, Zi-An Lu, Can Pan, Can Li, Jia Li, Jun Mao, Yitao Sun, Lunquan He, Jiang BMC Med Research Article BACKGROUND: Tumour-infiltrating lymphocytes (TILs), including T and B cells, have been demonstrated to be associated with tumour progression. However, the different subpopulations of TILs and their roles in breast cancer remain poorly understood. Large-scale analysis using multiomics data could uncover potential mechanisms and provide promising biomarkers for predicting immunotherapy response. METHODS: Single-cell transcriptome data for breast cancer samples were analysed to identify unique TIL subsets. Based on the expression profiles of marker genes in these subsets, a TIL-related prognostic model was developed by univariate and multivariate Cox analyses and LASSO regression for the TCGA training cohort containing 1089 breast cancer patients. Multiplex immunohistochemistry was used to confirm the presence of TIL subsets in breast cancer samples. The model was validated with a large-scale transcriptomic dataset for 3619 breast cancer patients, including the METABRIC cohort, six chemotherapy transcriptomic cohorts, and two immunotherapy transcriptomic cohorts. RESULTS: We identified two TIL subsets with high expression of CD103 and LAG3 (CD103(+)LAG3(+)), including a CD8(+) T-cell subset and a B-cell subset. Based on the expression profiles of marker genes in these two subpopulations, we further developed a CD103(+)LAG3(+) TIL-related prognostic model (CLTRP) based on CXCL13 and BIRC3 genes for predicting the prognosis of breast cancer patients. CLTRP-low patients had a better prognosis than CLTRP-high patients. The comprehensive results showed that a low CLTRP score was associated with a high TP53 mutation rate, high infiltration of CD8 T cells, helper T cells, and CD4 T cells, high sensitivity to chemotherapeutic drugs, and a good response to immunotherapy. In contrast, a high CLTRP score was correlated with a low TP53 mutation rate, high infiltration of M0 and M2 macrophages, low sensitivity to chemotherapeutic drugs, and a poor response to immunotherapy. CONCLUSIONS: Our present study showed that the CLTRP score is a promising biomarker for distinguishing prognosis, drug sensitivity, molecular and immune characteristics, and immunotherapy outcomes in breast cancer patients. The CLTRP could serve as a valuable tool for clinical decision making regarding immunotherapy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-023-02960-1. BioMed Central 2023-07-24 /pmc/articles/PMC10367329/ /pubmed/37488535 http://dx.doi.org/10.1186/s12916-023-02960-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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 Article Xia, Zi-An Lu, Can Pan, Can Li, Jia Li, Jun Mao, Yitao Sun, Lunquan He, Jiang The expression profiles of signature genes from CD103(+)LAG3(+) tumour-infiltrating lymphocyte subsets predict breast cancer survival |
title | The expression profiles of signature genes from CD103(+)LAG3(+) tumour-infiltrating lymphocyte subsets predict breast cancer survival |
title_full | The expression profiles of signature genes from CD103(+)LAG3(+) tumour-infiltrating lymphocyte subsets predict breast cancer survival |
title_fullStr | The expression profiles of signature genes from CD103(+)LAG3(+) tumour-infiltrating lymphocyte subsets predict breast cancer survival |
title_full_unstemmed | The expression profiles of signature genes from CD103(+)LAG3(+) tumour-infiltrating lymphocyte subsets predict breast cancer survival |
title_short | The expression profiles of signature genes from CD103(+)LAG3(+) tumour-infiltrating lymphocyte subsets predict breast cancer survival |
title_sort | expression profiles of signature genes from cd103(+)lag3(+) tumour-infiltrating lymphocyte subsets predict breast cancer survival |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10367329/ https://www.ncbi.nlm.nih.gov/pubmed/37488535 http://dx.doi.org/10.1186/s12916-023-02960-1 |
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