Mapping Lung Cancer Epithelial-mesenchymal Transition States and Trajectories with Single-cell Resolution

Overview

Journal Nat Commun

Specialty Biology

Date 2019 Dec 8

PMID 31811131

Citations 107

Authors

Loukia G Karacosta

Benedict Anchang

Nikolaos Ignatiadis

Samuel C Kimmey

Jalen A Benson

Joseph B Shrager

Robert Tibshirani

Sean C Bendall

Sylvia K Plevritis

Affiliations

Soon will be listed here.

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.

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