ArticleNature communications2019
Mapping lung cancer epithelial-mesenchymal transition states and trajectories with single-cell resolution.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 129 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
129 citing papers in PubMed.
- Decoding epithelial-mesenchymal transitions with multi-omics.Nature reviews. Genetics · 2026Review
- Learning stochastic dynamics and cell-fate landscapes from single-cell snapshots via optimal transport.Science advances · 2026Article
- Reprogramming resistance in advanced lung cancer: epigenetic modulation to restore therapeutic vulnerability.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Systems-level analysis identifies IRF6 as an inhibitor of epithelial-mesenchymal transition.Journal of biological engineering · 2026Article
- Single-cell morphodynamical trajectories enable prediction of gene expression accompanying cell state change.Cell systems · 2026Article
- Inferring stochastic dynamics by biophysical Neural ODE using single-cell transcriptomics.Nature communications · 2026Article
- TRIM29 promotes epithelial-mesenchymal transition, angiogenesis, and stromal remodeling in lung adenocarcinoma: integrated validation at histologic, transcriptomic, and protein levels.Scientific reports · 2026Article
- Single cell snapshot analyses under proper representation reveal that epithelial-mesenchymal transition couples at G1 and G2/M.Communications biology · 2026Article
- Identification, functional insights and therapeutic targeting of EMT tumour states.Nature reviews. Cancer · 2026Review
- CyTOF-based profiling of circulating tumor cells predicts aggressiveness and therapy response in SCLC liquid biopsies at a personalized level.bioRxiv : the preprint server for biology · 2025Article
- Stochastic Nature of Fascia: From Layered Pedagogical Artifact to Morphogenetic Reality in Clinical Anatomy.Life (Basel, Switzerland) · 2025Article
- Impacts of competition and phenotypic plasticity on the viability of adaptive therapy.Mathematical biosciences · 2025Article
- Boosting data interpretation with GIBOOST to enhance visualization of complex high-dimensional data.Briefings in bioinformatics · 2025Article
- Heterogeneity-driven phenotypic plasticity and treatment response in branched-organoid models of pancreatic ductal adenocarcinoma.Nature biomedical engineering · 2025Article
- Fusion of spatiotemporal and network models to prioritize multiscale effects in single-cell perturbations.Briefings in bioinformatics · 2025Article
- HOXD1 regulates neural crest cells differentiation and polycerate development in sheep.Scientific reports · 2025Article
- A Boolean network model of hypoxia, mechanosensing and TGF-β signaling captures the role of phenotypic plasticity and mutations in tumor metastasis.PLoS computational biology · 2025Article
- Article
- An integrative phenotype-structured partial differential equation model for the population dynamics of epithelial-mesenchymal transition.NPJ systems biology and applications · 2025Article
- Conventional techniques and emerging nanotechnologies for early detection of cancer metastasis via epithelial-mesenchymal transition monitoring.National science review · 2025Review
69 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.