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Mapping lung cancer epithelial-mesenchymal transition states and trajectories with single-cell resolution. Nature communications Karacosta, L. G., Anchang, B. n., Ignatiadis, N. n., Kimmey, S. C., Benson, J. A., Shrager, J. B., Tibshirani, R. n., Bendall, S. C., Plevritis, S. K. 2019; 10 (1): 5587

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 TGFß-treatment and identify, through TGFß-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.

View details for DOI 10.1038/s41467-019-13441-6

View details for PubMedID 31811131