1. Mapping lung cancer epithelial-mesenchymal transition states and trajectories with single-cell resolution.
- Author
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Karacosta, Loukia G, Anchang, Benedict, Ignatiadis, Nikolaos, Kimmey, Samuel C, Benson, Jalen A, Shrager, Joseph B, Tibshirani, Robert, Bendall, Sean C, and Plevritis, Sylvia K
- Subjects
Cell Line ,Tumor ,Epithelial Cells ,Humans ,Lung Neoplasms ,Transforming Growth Factor beta ,Cytophotometry ,Computational Biology ,Systems Biology ,Phenotype ,Algorithms ,Epithelial-Mesenchymal Transition ,Cell Line ,Tumor - 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.
- Published
- 2019