Mapping lung cancer epithelial-mesenchymal transition states and trajectories with single-cell resolution.
Nat Commun
; 10(1): 5587, 2019 12 06.
Article
em En
| MEDLINE
| ID: mdl-31811131
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Transição Epitelial-Mesenquimal
/
Neoplasias Pulmonares
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Nat Commun
Assunto da revista:
BIOLOGIA
/
CIENCIA
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Estados Unidos