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Mapping Lung Cancer Epithelial-Mesenchymal Transition States and Trajectories with Single-Cell Resolution

Loukia G. Karacosta, Benedict Anchang, Nikolaos Ignatiadis, Samuel C. Kimmey, Jalen A. Benson, Joseph B. Shrager, Robert Tibshirani, View ORCID ProfileSean C. Bendall, Sylvia K. Plevritis
doi: https://doi.org/10.1101/570341
Loukia G. Karacosta
1Departments of Radiology, Stanford University
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Benedict Anchang
1Departments of Radiology, Stanford University
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Nikolaos Ignatiadis
2Departments of Statistics, Stanford University
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Samuel C. Kimmey
3Departments of Pathology, Stanford University
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Jalen A. Benson
4Departments of Cardiothoracic Surgery, Stanford University
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Joseph B. Shrager
4Departments of Cardiothoracic Surgery, Stanford University
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Robert Tibshirani
2Departments of Statistics, Stanford University
5Departments of Biomedical Data Sciences, Stanford University
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Sean C. Bendall
3Departments of Pathology, Stanford University
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  • ORCID record for Sean C. Bendall
Sylvia K. Plevritis
1Departments of Radiology, Stanford University
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  • For correspondence: sylvia.plevritis@stanford.edu
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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.

Footnotes

  • ↵* Co-senior author

  • Loukia G. Karacosta: loukia{at}stanford.edu, Benedict Anchang: anchang{at}stanford.edu, Nikolaos Ignatiadis: ignat{at}stanford.edu, Samuel C. Kimmey: skimmey{at}stanford.edu, Jalen A. Benson: jabenson{at}stanford.edu, Joseph B. Shrager: shrager{at}stanford.edu, Robert Tibshirani: tibs{at}stanford.edu, Sean C. Bendall: bendall{at}stanford.edu

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted March 07, 2019.
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Mapping Lung Cancer Epithelial-Mesenchymal Transition States and Trajectories with Single-Cell Resolution
Loukia G. Karacosta, Benedict Anchang, Nikolaos Ignatiadis, Samuel C. Kimmey, Jalen A. Benson, Joseph B. Shrager, Robert Tibshirani, Sean C. Bendall, Sylvia K. Plevritis
bioRxiv 570341; doi: https://doi.org/10.1101/570341
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Mapping Lung Cancer Epithelial-Mesenchymal Transition States and Trajectories with Single-Cell Resolution
Loukia G. Karacosta, Benedict Anchang, Nikolaos Ignatiadis, Samuel C. Kimmey, Jalen A. Benson, Joseph B. Shrager, Robert Tibshirani, Sean C. Bendall, Sylvia K. Plevritis
bioRxiv 570341; doi: https://doi.org/10.1101/570341

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