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Record W4403464788 · doi:10.1016/j.isci.2024.111169

ZEB2 drives intra-tumor heterogeneity and skin squamous cell carcinoma formation with distinct EMP transition states

2024· article· en· W4403464788 on OpenAlexaff
Jeroen Verstappe, Nicolas Skrypek, Jordy De Coninck, Bieke Soen, Joachim Taminau, Marianthi Tatari, Kenneth Bruneel, Nele Loret, Kato De Clercq, Caroline Van den Broecke, Koen Van de Vijver, Jo Van Dorpe, Jody J. Haigh, Bram De Craene, Steven Goossens, Niels Vandamme, Geert Berx

Bibliographic record

VenueiScience · 2024
Typearticle
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsUniversity of ManitobaResearch Institute in Oncology and HematologyCancerCare Manitoba
FundersBijzonder Onderzoeksfonds UGentKom op tegen KankerUniversiteit GentStichting Tegen KankerFonds Wetenschappelijk OnderzoekKræftens Bekæmpelse
KeywordsBasal cellTransition (genetics)ChemistryBiophysicsCancer researchPathologyBiologyMedicineBiochemistry

Abstract

fetched live from OpenAlex

About 5% of patients with cutaneous squamous cell carcinoma (cSCC) have a poor prognosis which is associated with a loss of tumor differentiation, invasion and metastasis, all of which are linked to the process of epithelial-to-mesenchymal plasticity (EMP). Here, we showed that the EMP-associated transcription factor ZEB2 drives cSCC heterogeneity which resembles biphasic carcinosarcoma-like tumors. Single cell RNA sequencing revealed distinct subpopulations ranging from fully epithelial (E) to intermediate (EM) to fully mesenchymal (M), associated with the gradual loss of cell surface markers EPCAM, CDH1, ITGB4, and CD200. Mesenchymal features were associated with a higher metastatic capacity and anoikis resistance, yet this comes with a sensitivity toward TNF-induced cell death. Altogether we provide insights in cSCC heterogeneity and modes to target mesenchymal-metastasis inducing cells.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.457
Threshold uncertainty score0.259

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.246
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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