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Mapping lung cancer epithelial-mesenchymal transition states and trajectories with single-cell resolution

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...

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Autores principales: Karacosta, Loukia G., Anchang, Benedict, Ignatiadis, Nikolaos, Kimmey, Samuel C., Benson, Jalen A., Shrager, Joseph B., Tibshirani, Robert, Bendall, Sean C., Plevritis, Sylvia K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6898514/
https://www.ncbi.nlm.nih.gov/pubmed/31811131
http://dx.doi.org/10.1038/s41467-019-13441-6
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author Karacosta, Loukia G.
Anchang, Benedict
Ignatiadis, Nikolaos
Kimmey, Samuel C.
Benson, Jalen A.
Shrager, Joseph B.
Tibshirani, Robert
Bendall, Sean C.
Plevritis, Sylvia K.
author_facet Karacosta, Loukia G.
Anchang, Benedict
Ignatiadis, Nikolaos
Kimmey, Samuel C.
Benson, Jalen A.
Shrager, Joseph B.
Tibshirani, Robert
Bendall, Sean C.
Plevritis, Sylvia K.
author_sort Karacosta, Loukia G.
collection PubMed
description 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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spelling pubmed-68985142019-12-09 Mapping lung cancer epithelial-mesenchymal transition states and trajectories with single-cell resolution Karacosta, Loukia G. Anchang, Benedict Ignatiadis, Nikolaos Kimmey, Samuel C. Benson, Jalen A. Shrager, Joseph B. Tibshirani, Robert Bendall, Sean C. Plevritis, Sylvia K. Nat Commun Article 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. Nature Publishing Group UK 2019-12-06 /pmc/articles/PMC6898514/ /pubmed/31811131 http://dx.doi.org/10.1038/s41467-019-13441-6 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Karacosta, Loukia G.
Anchang, Benedict
Ignatiadis, Nikolaos
Kimmey, Samuel C.
Benson, Jalen A.
Shrager, Joseph B.
Tibshirani, Robert
Bendall, Sean C.
Plevritis, Sylvia K.
Mapping lung cancer epithelial-mesenchymal transition states and trajectories with single-cell resolution
title Mapping lung cancer epithelial-mesenchymal transition states and trajectories with single-cell resolution
title_full Mapping lung cancer epithelial-mesenchymal transition states and trajectories with single-cell resolution
title_fullStr Mapping lung cancer epithelial-mesenchymal transition states and trajectories with single-cell resolution
title_full_unstemmed Mapping lung cancer epithelial-mesenchymal transition states and trajectories with single-cell resolution
title_short Mapping lung cancer epithelial-mesenchymal transition states and trajectories with single-cell resolution
title_sort mapping lung cancer epithelial-mesenchymal transition states and trajectories with single-cell resolution
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6898514/
https://www.ncbi.nlm.nih.gov/pubmed/31811131
http://dx.doi.org/10.1038/s41467-019-13441-6
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