Performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures

Khoa A. Tran, Venkateswar Addala, Rebecca L. Johnston, David Lovell, Andrew Bradley, Lambros T. Koufariotis, Scott Wood, Sunny Z. Wu, Daniel Roden, Ghamdan Al-Eryani, Alexander Swarbrick, Elizabeth D. Williams, John V. Pearson, Olga Kondrashova and Nicola Waddell ()
Additional contact information
Khoa A. Tran: QIMR Berghofer Medical Research Institute
Venkateswar Addala: QIMR Berghofer Medical Research Institute
Rebecca L. Johnston: QIMR Berghofer Medical Research Institute
David Lovell: Queensland University of Technology
Andrew Bradley: Queensland University of Technology
Lambros T. Koufariotis: QIMR Berghofer Medical Research Institute
Scott Wood: QIMR Berghofer Medical Research Institute
Sunny Z. Wu: Garvan Institute of Medical Research
Daniel Roden: Garvan Institute of Medical Research
Ghamdan Al-Eryani: Garvan Institute of Medical Research
Alexander Swarbrick: Garvan Institute of Medical Research
Elizabeth D. Williams: Queensland University of Technology (QUT)
John V. Pearson: QIMR Berghofer Medical Research Institute
Olga Kondrashova: QIMR Berghofer Medical Research Institute
Nicola Waddell: QIMR Berghofer Medical Research Institute

Nature Communications, 2023, vol. 14, issue 1, 1-17

Abstract: Abstract 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.

Date: 2023
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DOI: 10.1038/s41467-023-41385-5

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