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Performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures
Cells within the tumour microenvironment (TME) can impact tumour development and influence treatment response. Computational approaches have been developed to deconvolve the TME from bulk RNA-seq. Using scRNA-seq profiling from breast tumours we simulate thousands of bulk mixtures, representing tumo...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10505141/ https://www.ncbi.nlm.nih.gov/pubmed/37717006 http://dx.doi.org/10.1038/s41467-023-41385-5 |
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author | Tran, Khoa A. Addala, Venkateswar Johnston, Rebecca L. Lovell, David Bradley, Andrew Koufariotis, Lambros T. Wood, Scott Wu, Sunny Z. Roden, Daniel Al-Eryani, Ghamdan Swarbrick, Alexander Williams, Elizabeth D. Pearson, John V. Kondrashova, Olga Waddell, Nicola |
author_facet | Tran, Khoa A. Addala, Venkateswar Johnston, Rebecca L. Lovell, David Bradley, Andrew Koufariotis, Lambros T. Wood, Scott Wu, Sunny Z. Roden, Daniel Al-Eryani, Ghamdan Swarbrick, Alexander Williams, Elizabeth D. Pearson, John V. Kondrashova, Olga Waddell, Nicola |
author_sort | Tran, Khoa A. |
collection | PubMed |
description | Cells within the tumour microenvironment (TME) can impact tumour development and influence treatment response. Computational approaches have been developed to deconvolve the TME from bulk RNA-seq. Using scRNA-seq profiling from breast tumours we simulate thousands of bulk mixtures, representing tumour purities and cell lineages, to compare the performance of nine TME deconvolution methods (BayesPrism, Scaden, CIBERSORTx, MuSiC, DWLS, hspe, CPM, Bisque, and EPIC). Some methods are more robust in deconvolving mixtures with high tumour purity levels. Most methods tend to mis-predict normal epithelial for cancer epithelial as tumour purity increases, a finding that is validated in two independent datasets. The breast cancer molecular subtype influences this mis-prediction. BayesPrism and DWLS have the lowest combined numbers of false positives and false negatives, and have the best performance when deconvolving granular immune lineages. Our findings highlight the need for more single-cell characterisation of rarer cell types, and suggest that tumour cell compositions should be considered when deconvolving the TME. |
format | Online Article Text |
id | pubmed-10505141 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-105051412023-09-18 Performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures Tran, Khoa A. Addala, Venkateswar Johnston, Rebecca L. Lovell, David Bradley, Andrew Koufariotis, Lambros T. Wood, Scott Wu, Sunny Z. Roden, Daniel Al-Eryani, Ghamdan Swarbrick, Alexander Williams, Elizabeth D. Pearson, John V. Kondrashova, Olga Waddell, Nicola Nat Commun Article Cells within the tumour microenvironment (TME) can impact tumour development and influence treatment response. Computational approaches have been developed to deconvolve the TME from bulk RNA-seq. Using scRNA-seq profiling from breast tumours we simulate thousands of bulk mixtures, representing tumour purities and cell lineages, to compare the performance of nine TME deconvolution methods (BayesPrism, Scaden, CIBERSORTx, MuSiC, DWLS, hspe, CPM, Bisque, and EPIC). Some methods are more robust in deconvolving mixtures with high tumour purity levels. Most methods tend to mis-predict normal epithelial for cancer epithelial as tumour purity increases, a finding that is validated in two independent datasets. The breast cancer molecular subtype influences this mis-prediction. BayesPrism and DWLS have the lowest combined numbers of false positives and false negatives, and have the best performance when deconvolving granular immune lineages. Our findings highlight the need for more single-cell characterisation of rarer cell types, and suggest that tumour cell compositions should be considered when deconvolving the TME. Nature Publishing Group UK 2023-09-16 /pmc/articles/PMC10505141/ /pubmed/37717006 http://dx.doi.org/10.1038/s41467-023-41385-5 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/) . |
spellingShingle | Article Tran, Khoa A. Addala, Venkateswar Johnston, Rebecca L. Lovell, David Bradley, Andrew Koufariotis, Lambros T. Wood, Scott Wu, Sunny Z. Roden, Daniel Al-Eryani, Ghamdan Swarbrick, Alexander Williams, Elizabeth D. Pearson, John V. Kondrashova, Olga Waddell, Nicola Performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures |
title | Performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures |
title_full | Performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures |
title_fullStr | Performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures |
title_full_unstemmed | Performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures |
title_short | Performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures |
title_sort | performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10505141/ https://www.ncbi.nlm.nih.gov/pubmed/37717006 http://dx.doi.org/10.1038/s41467-023-41385-5 |
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