Mapping Lung Cancer Epithelial-Mesenchymal Transition States and Trajectories with Single-Cell Resolution
Elucidating a continuum of epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET) states in clinical samples promises new insights in cancer progression and drug response. Using mass cytometry time-course analysis, we resolve lung cancer EMT states through TGFβ-treatment...
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| Published in | bioRxiv |
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| Main Authors | , , , , , , , , |
| Format | Paper |
| Language | English |
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Cold Spring Harbor
Cold Spring Harbor Laboratory Press
07.03.2019
Cold Spring Harbor Laboratory |
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| ISSN | 2692-8205 2692-8205 |
| DOI | 10.1101/570341 |
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| Abstract | Elucidating a continuum of epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET) states in clinical samples promises new insights in cancer progression and drug response. Using mass cytometry time-course analysis, we resolve lung cancer EMT states through TGFβ-treatment and identify through TGFβ-withdrawal, an MET state previously unrealized. We demonstrate significant differences between EMT and MET trajectories using a novel computational tool (TRACER) for reconstructing trajectories between cell states. Additionally, we construct a lung cancer reference map of EMT and MET states referred to as the EMT-MET STAte MaP (STAMP). Using a neural net algorithm, we project clinical samples onto the EMT-MET STAMP to characterize their phenotypic profile with single-cell resolution in terms of our in vitro EMT-MET analysis. In summary, we provide a framework that can be extended to phenotypically characterize clinical samples in the context of in vitro studies showing differential EMT-MET traits related to metastasis and drug sensitivity. |
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| AbstractList | Elucidating a continuum of epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET) states in clinical samples promises new insights in cancer progression and drug response. Using mass cytometry time-course analysis, we resolve lung cancer EMT states through TGFβ-treatment and identify through TGFβ-withdrawal, an MET state previously unrealized. We demonstrate significant differences between EMT and MET trajectories using a novel computational tool (TRACER) for reconstructing trajectories between cell states. Additionally, we construct a lung cancer reference map of EMT and MET states referred to as the EMT-MET STAte MaP (STAMP). Using a neural net algorithm, we project clinical samples onto the EMT-MET STAMP to characterize their phenotypic profile with single-cell resolution in terms of our in vitro EMT-MET analysis. In summary, we provide a framework that can be extended to phenotypically characterize clinical samples in the context of in vitro studies showing differential EMT-MET traits related to metastasis and drug sensitivity. |
| Author | Benedict Anchang Bendall, Sean C Karacosta, Loukia G Benson, Jalen A Shrager, Joseph B Tibshirani, Robert Ignatiadis, Nikolaos Kimmey, Samuel C Plevritis, Sylvia K |
| Author_xml | – sequence: 1 givenname: Loukia surname: Karacosta middlename: G fullname: Karacosta, Loukia G – sequence: 2 fullname: Benedict Anchang – sequence: 3 givenname: Nikolaos surname: Ignatiadis fullname: Ignatiadis, Nikolaos – sequence: 4 givenname: Samuel surname: Kimmey middlename: C fullname: Kimmey, Samuel C – sequence: 5 givenname: Jalen surname: Benson middlename: A fullname: Benson, Jalen A – sequence: 6 givenname: Joseph surname: Shrager middlename: B fullname: Shrager, Joseph B – sequence: 7 givenname: Robert surname: Tibshirani fullname: Tibshirani, Robert – sequence: 8 givenname: Sean surname: Bendall middlename: C fullname: Bendall, Sean C – sequence: 9 givenname: Sylvia surname: Plevritis middlename: K fullname: Plevritis, Sylvia K |
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| Cites_doi | 10.1007/11561071_34 10.1158/1078-0432.CCR-15-0876 |
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| Copyright | 2019. Notwithstanding the ProQuest Terms and conditions, you may use this content in accordance with the associated terms available at https://www.biorxiv.org/content/10.1101/570341v1 2019, Posted by Cold Spring Harbor Laboratory |
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| Title | Mapping Lung Cancer Epithelial-Mesenchymal Transition States and Trajectories with Single-Cell Resolution |
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