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Record W2986547896 · doi:10.1038/s41467-019-12832-z

ZEB1/NuRD complex suppresses TBC1D2b to stimulate E-cadherin internalization and promote metastasis in lung cancer

2019· article· en· W2986547896 on OpenAlexafffund
Roxsan Manshouri, Étienne Coyaud, Samrat T. Kundu, David H. Peng, Sabrina A. Stratton, Kendra Alton, Rakhee Bajaj, Jared J. Fradette, Rosalba Minelli, Michael Peoples, Alessandro Carugo, Fengju Chen, Christopher A. Bristow, Jeffrey J. Kovacs, Michelle Barton, Tim Heffernan, Chad J. Creighton, Brian Raught, Don L. Gibbons

Bibliographic record

VenueNature Communications · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
FundersCancer Prevention and Research Institute of TexasUniversity of Texas MD Anderson Cancer CenterPrincess Margaret Cancer FoundationNational Cancer InstituteNational Institutes of HealthU.S. Department of Health and Human Services
KeywordsInternalizationCadherinMetastasisCancer researchLung cancerCancer metastasisCancerCell biologyBiologyMedicineOncologyCellGenetics

Abstract

fetched live from OpenAlex

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related death worldwide, due in part to the propensity of lung cancer to metastasize. Aberrant epithelial-to-mesenchymal transition (EMT) is a proposed model for the initiation of metastasis. During EMT cell-cell adhesion is reduced allowing cells to dissociate and invade. Of the EMT-associated transcription factors, ZEB1 uniquely promotes NSCLC disease progression. Here we apply two independent screens, BioID and an Epigenome shRNA dropout screen, to define ZEB1 interactors that are critical to metastatic NSCLC. We identify the NuRD complex as a ZEB1 co-repressor and the Rab22 GTPase-activating protein TBC1D2b as a ZEB1/NuRD complex target. We find that TBC1D2b suppresses E-cadherin internalization, thus hindering cancer cell invasion and metastasis.

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 score0.632
Threshold uncertainty score0.431

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 head0.020
GPT teacher head0.354
Teacher spread0.334 · 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

Citations102
Published2019
Admission routes2
Has abstractyes

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