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

Loukia G. Karacosta, Benedict Anchang, Nikolaos Ignatiadis, Samuel C. Kimmey, Jalen A. Benson, Joseph B. Shrager, Robert Tibshirani, Sean C. Bendall and Sylvia K. Plevritis ()
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Loukia G. Karacosta: Stanford University
Benedict Anchang: Stanford University
Nikolaos Ignatiadis: Stanford University
Samuel C. Kimmey: Stanford University
Jalen A. Benson: Stanford University
Joseph B. Shrager: Stanford University
Robert Tibshirani: Stanford University
Sean C. Bendall: Stanford University
Sylvia K. Plevritis: Stanford University

Nature Communications, 2019, vol. 10, issue 1, 1-15

Abstract: 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.

Date: 2019
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DOI: 10.1038/s41467-019-13441-6

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