Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 200

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

Cell-Type-Specific Chromatin States Differentially Prime Squamous Cell Carcinoma Tumor-Initiating Cells for Epithelial to Mesenchymal Transition

Open access
Latil, Mathilde, Nassar, Dany, Beck, Benjamin, Boumahdi, Soufiane, Wang, Li, Brisebarre, Audrey, Dubois, Christine, Nkusi, Erwin, Lenglez, Sandrine, Checinska, Agnieszka, Vercauteren Drubbel, Alizée, Devos, Michael, Declercq, Wim, Yi, Rui, Blanpain, Cédric·2017·Cell Stem Cell

Abstract

SUMMARY Epithelial to mesenchymal transition (EMT) in cancer cells has been associated with metastasis, sternness, and resistance to therapy. Some tumors undergo EMT while others do not, which may reflect intrinsic properties of their cell of origin. However, this possibility is largely unexplored. By targeting the same oncogenic mutations to discrete skin compartments, we show that cell-type-specific chromatin and transcriptional states differentially prime tumors to EMT. Squamous cell carcinomas (SCCs) derived from interfollicular epidermis (IFE) are generally well differentiated, while hair follicle (HF) stem cell-derived SCCs frequently exhibit EMT, efficiently form secondary tumors, and possess increased metastatic potential. Transcriptional and epigenomic profiling revealed that IFE and HF tumor-initiating cells possess distinct chromatin landscapes and gene regulatory networks associated with tumorigenesis and EMT that correlate with accessibility of key epithelial and EMT transcription factor binding sites. These findings highlight the importance of chromatin states and transcriptional priming in dictating tumor phenotypes and EMT.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.stem.2016.10.018

Cites

8 of 8 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Cited by

See all 200

Metadata

Authors
Latil, M., Nassar, D., Beck, B., Boumahdi, S., Wang, L., Brisebarre, A., Dubois, C., Nkusi, E., Lenglez, S., Checinska, A., Vercauteren Drubbel, A., Devos, M., Declercq, W., Yi, R., & Blanpain, C.
Venue
Cell Stem Cell
Publisher
Elsevier BV
Published
2017-02
Volume
20
Issue
2
Pages
191-204.e5
ISSN
  • 1875-9777 (ISSN-L)
  • 1934-5909
OpenAlex
W2551820454
Cited by
200
References
8
FWCI
11.59
Type
journal article
Language
en
Open access
bronze

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.