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Nat Commun. 2019 Dec 6;10(1):5587. doi: 10.1038/s41467-019-13441-6.

Mapping lung cancer epithelial-mesenchymal transition states and trajectories with single-cell resolution.

Author information

1
Department of Biomedical Data Science, Stanford University, Stanford, USA.
2
Department of Radiology, Stanford University, Stanford, USA.
3
Department of Statistics, Stanford University, Stanford, USA.
4
Department of Pathology, Stanford University, Stanford, USA.
5
Department of Cardiothoracic Surgery, Stanford University, Stanford, USA.
6
Department of Biomedical Data Science, Stanford University, Stanford, USA. sylvia.plevritis@stanford.edu.
7
Department of Radiology, Stanford University, Stanford, USA. sylvia.plevritis@stanford.edu.

Abstract

Elucidating the spectrum of epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET) states in clinical samples promises insights on cancer progression and drug resistance. Using mass cytometry time-course analysis, we resolve lung cancer EMT states through TGFβ-treatment and identify, through TGFβ-withdrawal, a distinct MET state. We demonstrate significant differences between EMT and MET trajectories using a computational tool (TRACER) for reconstructing trajectories between cell states. In addition, we construct a lung cancer reference map of EMT and MET states referred to as the EMT-MET PHENOtypic STAte MaP (PHENOSTAMP). Using a neural net algorithm, we project clinical samples onto the EMT-MET PHENOSTAMP to characterize their phenotypic profile with single-cell resolution in terms of our in vitro EMT-MET analysis. In summary, we provide a framework to phenotypically characterize clinical samples in the context of in vitro EMT-MET findings which could help assess clinical relevance of EMT in cancer in future studies.

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