期刊
NATURE COMMUNICATIONS
卷 10, 期 -, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41467-019-13441-6
关键词
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资金
- NIH/NCI [R25CA180993]
- Tobacco-Related Disease Research Program [T29FT0569]
- Chan Zuckerberg Initiative DAF
- Damon Runyon Cancer Research Foundation Fellowship [DRG-2017-09]
- NIH [1DP2OD022550-01, 1R01AG056287-01, 1R01AG057915-01, 1-R00-GM104148-01, 1U24CA224309-01, 5U19AI116484-02, U54CA209971]
Elucidating the spectrum of epithelial-mesenchymal transition (EMT) and mesenchymalepithelial 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 TGF beta-treatment and identify, through TGF beta-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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