Performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
16 09 2023
16 09 2023
Historique:
received:
31
07
2022
accepted:
01
09
2023
medline:
18
9
2023
pubmed:
17
9
2023
entrez:
16
9
2023
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 37717006
doi: 10.1038/s41467-023-41385-5
pii: 10.1038/s41467-023-41385-5
pmc: PMC10505141
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
5758Informations de copyright
© 2023. Springer Nature Limited.
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