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.
Highlights
Elucidating the spectrum of epithelial-mesenchymal transition (EMT) and mesenchymalepithelial transition (MET) states in clinical samples promises insights on cancer progression and drug resistance
We show that our EMT–MET PHENOSTAMP can be utilized to score and interpret clinical specimen data towards EMT and MET state heterogeneity and that similar approaches can be extended to phenotyping a range of cellular processes that involve cell state transitions
We provide an integrated experimental–computational framework to define discrete lung cancer EMT and MET states and assess their clinical relevance
Summary
Elucidating the spectrum of epithelial-mesenchymal transition (EMT) and mesenchymalepithelial transition (MET) states in clinical samples promises insights on cancer progression and drug resistance. EMT is a dynamic process and under specific conditions is reversible (mesenchymal–epithelial transition, MET), highlighting a phenotypic plasticity that has been observed in both normal and malignant cells[3]. Adding to the complexity in understanding the clinical significance of EMT is the recognition that EMT is not a binary process (strictly defined by epithelial and mesenchymal states), but instead a spectrum of states where transitioning cells exhibit partial EMT phenotypes with both epithelial and mesenchymal features. Gonzalez et al.[13] identified partial EMT states in ovarian cancer specimens with mass cytometry While these studies provide important insights, they did not directly relate their findings to EMT states that have been well characterized in preclinical in vitro reference models. Mass cytometry was used to study drug perturbations on tumor growth factor-β (TGFβ)-induced EMT in mouse epithelial cancer cells[14], and sc-RNAseq was used to study TGFβ-induced EMT in human breast epithelial cells[15]; neither of these studies provided a means to assess the clinical relevance of their respective findings
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