Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
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
_version_ 1785106856822177792
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
work_keys_str_mv AT trankhoaa performanceoftumourmicroenvironmentdeconvolutionmethodsinbreastcancerusingsinglecellsimulatedbulkmixtures
AT addalavenkateswar performanceoftumourmicroenvironmentdeconvolutionmethodsinbreastcancerusingsinglecellsimulatedbulkmixtures
AT johnstonrebeccal performanceoftumourmicroenvironmentdeconvolutionmethodsinbreastcancerusingsinglecellsimulatedbulkmixtures
AT lovelldavid performanceoftumourmicroenvironmentdeconvolutionmethodsinbreastcancerusingsinglecellsimulatedbulkmixtures
AT bradleyandrew performanceoftumourmicroenvironmentdeconvolutionmethodsinbreastcancerusingsinglecellsimulatedbulkmixtures
AT koufariotislambrost performanceoftumourmicroenvironmentdeconvolutionmethodsinbreastcancerusingsinglecellsimulatedbulkmixtures
AT woodscott performanceoftumourmicroenvironmentdeconvolutionmethodsinbreastcancerusingsinglecellsimulatedbulkmixtures
AT wusunnyz performanceoftumourmicroenvironmentdeconvolutionmethodsinbreastcancerusingsinglecellsimulatedbulkmixtures
AT rodendaniel performanceoftumourmicroenvironmentdeconvolutionmethodsinbreastcancerusingsinglecellsimulatedbulkmixtures
AT aleryanighamdan performanceoftumourmicroenvironmentdeconvolutionmethodsinbreastcancerusingsinglecellsimulatedbulkmixtures
AT swarbrickalexander performanceoftumourmicroenvironmentdeconvolutionmethodsinbreastcancerusingsinglecellsimulatedbulkmixtures
AT williamselizabethd performanceoftumourmicroenvironmentdeconvolutionmethodsinbreastcancerusingsinglecellsimulatedbulkmixtures
AT pearsonjohnv performanceoftumourmicroenvironmentdeconvolutionmethodsinbreastcancerusingsinglecellsimulatedbulkmixtures
AT kondrashovaolga performanceoftumourmicroenvironmentdeconvolutionmethodsinbreastcancerusingsinglecellsimulatedbulkmixtures
AT waddellnicola performanceoftumourmicroenvironmentdeconvolutionmethodsinbreastcancerusingsinglecellsimulatedbulkmixtures