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

• Published in: bioRxiv
• Main Authors: Karacosta, Loukia G, Benedict Anchang, Ignatiadis, Nikolaos, Kimmey, Samuel C, Benson, Jalen A, Shrager, Joseph B, Tibshirani, Robert, Bendall, Sean C, Plevritis, Sylvia K
• Format: Paper
• Language: English
• Published: Cold Spring Harbor Cold Spring Harbor Laboratory Press 07.03.2019; Cold Spring Harbor Laboratory
• Edition: 1.1
• Subjects: Computer applications; Cytometry; Lung cancer; Mesenchyme; Metastases; Systems Biology
• ISSN: 2692-8205; 2692-8205
• DOI: 10.1101/570341

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.