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Record W2539421776 · doi:10.1038/srep35383

CDK4 regulates cancer stemness and is a novel therapeutic target for triple-negative breast cancer

2016· article· en· W2539421776 on OpenAlexafffund
Meiou Dai, Chenjing Zhang, Ayad Ali, Xinyuan Hong, Jun Tian, Chieh Lo, Nadège Fils-Aimé, Sergio A. Burgos, Suhad Ali, Jean‐Jacques Lebrun

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsTriple-negative breast cancerBreast cancerCancer researchCancerMedicineCancer stem cellCA15-3Cyclin-dependent kinase 4Stem cellOncologyInternal medicineBiologyCell cycleCyclin-dependent kinase 2

Abstract

fetched live from OpenAlex

Triple negative breast cancers exhibit very aggressive features and poor patient outcomes. These tumors are enriched in cancer stem cells and exhibit resistance to most treatments and chemotherapy. In this study, we found the cyclin-dependent kinase (CDK4) to act as a cancer stem cell regulator and novel prognostic marker in triple negative breast cancers. We found CDK4 to be highly expressed in these tumors and its expression to correlate with poor overall and relapse free survival outcomes, high tumor grade and poor prognostic features of triple negative breast cancer patients. Moreover, we found that blocking CDK4 expression or kinase activity, using a pharmacological inhibitor prevented breast cancer stem cell self-renewal. Interestingly, suppression of CDK4 expression or kinase activity reversed the basal-B TNBC mesenchymal phenotype to an epithelial- and luminal-like phenotype which correlates with better clinical prognosis. Finally, blocking CDK4 activity efficiently eliminated both normal and chemotherapy-resistant cancer cells in triple negative breast cancers, highlighting CDK4 as a promising novel therapeutic target for these aggressive breast tumors.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.372
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.030
GPT teacher head0.307
Teacher spread0.277 · 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.

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

Citations62
Published2016
Admission routes2
Has abstractyes

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