ABSTRACT
Elucidating a continuum of epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET) states in clinical samples promises new insights in cancer progression and drug response. Using mass cytometry time-course analysis, we resolve lung cancer EMT states through TGFβ-treatment and identify through TGFβ-withdrawal, an MET state previously unrealized. We demonstrate significant differences between EMT and MET trajectories using a novel computational tool (TRACER) for reconstructing trajectories between cell states. Additionally, we construct a lung cancer reference map of EMT and MET states referred to as the EMT-MET STAte MaP (STAMP). Using a neural net algorithm, we project clinical samples onto the EMT-MET STAMP to characterize their phenotypic profile with single-cell resolution in terms of our in vitro EMT-MET analysis. In summary, we provide a framework that can be extended to phenotypically characterize clinical samples in the context of in vitro studies showing differential EMT-MET traits related to metastasis and drug sensitivity.
Footnotes
↵* Co-senior author
Loukia G. Karacosta: loukia{at}stanford.edu, Benedict Anchang: anchang{at}stanford.edu, Nikolaos Ignatiadis: ignat{at}stanford.edu, Samuel C. Kimmey: skimmey{at}stanford.edu, Jalen A. Benson: jabenson{at}stanford.edu, Joseph B. Shrager: shrager{at}stanford.edu, Robert Tibshirani: tibs{at}stanford.edu, Sean C. Bendall: bendall{at}stanford.edu