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Record W2079013883 · doi:10.1182/blood-2004-06-2125

Inhibition of Akt increases p27Kip1 levels and induces cell cycle arrest in anaplastic large cell lymphoma

2004· article· en· W2079013883 on OpenAlexaff
George Z. Rassidakis, Marianna Feretzaki, Coralyn Atwell, Ioannis Grammatikakis, Quan Lin, Raymond Lai, François-Xavier Claret, L. Jeffrey Medeiros, Hesham M. Amin

Bibliographic record

VenueBlood · 2004
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsUniversity of Alberta
FundersNational Cancer Institute
KeywordsAnaplastic large-cell lymphomaProtein kinase BAnaplastic lymphoma kinaseCancer researchCell cycleLymphomaPI3K/AKT/mTOR pathwayKinaseCell cycle checkpointChemistryBiologySignal transductionMedicineApoptosisPathologyImmunologyCell biologyBiochemistry

Abstract

fetched live from OpenAlex

Anaplastic large cell lymphoma (ALCL) is a highly proliferative neoplasm that frequently carries the t(2;5)(p23;q35) and aberrantly expresses nucleophosmin-anaplastic lymphoma kinase (NPM-ALK). Previously, NPM-ALK had been shown to activate the phosphatidylinositol 3 kinase (PI3K)/Akt pathway. As the cyclin-dependent kinase (CDK) inhibitor p27(Kip1) (p27) is usually not expressed in ALCL, we hypothesized that activated Akt (pAkt) phosphorylates p27 resulting in increased p27 proteolysis and cell cycle progression. Here we demonstrate that inhibition of pAkt activity in ALCL decreases p27 phosphorylation and degradation, resulting in increased p27 levels and cell cycle arrest. Using immunohistochemistry, pAkt was detected in 24 (57%) of 42 ALCL tumors, including 8 (44%) of 18 ALK-positive tumors and 16 (67%) of 24 ALK-negative tumors, and was inversely correlated with p27 levels. The mean percentage of p27-positive tumor cells was 5% in the pAkt-positive group compared with 26% in the pAkt-negative group (P = .0076). These findings implicate that Akt activation promotes cell cycle progression through inactivation of p27 in ALCL.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.369
Threshold uncertainty score0.486

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 head0.228
Teacher spread0.218 · 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 designObservational
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

Citations104
Published2004
Admission routes1
Has abstractyes

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