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Record W2061484408 · doi:10.1073/pnas.160016897

Down-regulation of p21 <sup>WAF1/CIP1</sup> or p27 <sup>Kip1</sup> abrogates antiestrogen-mediated cell cycle arrest in human breast cancer cells

2000· article· en· W2061484408 on OpenAlexaff
Sandrine Cariou, Jeffrey Donovan, W. Michael Flanagan, Andrea Milić, Nandita Bhattacharya, Joyce M. Slingerland

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2000
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEstrogen and related hormone effects
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsAntiestrogenCyclin-dependent kinaseCyclin-dependent kinase 2Cyclin ECell cycleCancer researchTamoxifenCyclin-dependent kinase 4ChemistryKinaseCyclinCell cycle checkpointEstrogen receptorCyclin DCyclin ACell biologyBiologyBreast cancerCellInternal medicineCancerBiochemistryMedicine

Abstract

fetched live from OpenAlex

Estrogens and antiestrogens influence the G(1) phase of the cell cycle. In MCF-7 breast cancer cells, estrogen stimulated cell cycle progression through loss of the kinase inhibitor proteins (KIPs) p27 and p21 and through G(1) cyclin-dependent kinase (cdk) activation. Treatment with antiestrogen drugs, Tamoxifen or ICI 182780, caused cell cycle arrest, with up-regulation of both p21 and p27 levels, an increase in their binding to cyclin E-cdk2, and kinase inhibition. The requirement for these KIPs in the arrests induced by estradiol depletion or by antiestrogens was investigated with antisense. Antisense inhibition of p21 or p27 expression in estradiol-depleted or antiestrogenarrested MCF-7 led to abrogation of cell cycle arrest, with loss of cyclin E-associated KIPs, activation of cyclin E-cdk2, and S phase entrance. These data demonstrate that depletion of either p21 or p27 can mimic estrogen-stimulated cell cycle activation and indicate that both of these KIPs are critical mediators of the therapeutic effects of antiestrogens in breast cancer.

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 categoriesnone
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 score0.016
Threshold uncertainty score0.541

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.265
Teacher spread0.255 · 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

Citations225
Published2000
Admission routes1
Has abstractyes

Explore more

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