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Record W2121650311 · doi:10.1002/ijc.29081

Retinoic acid signaling in cancer: The parable of acute promyelocytic leukemia

2014· review· en· W2121650311 on OpenAlexfundno aff
Julien Ablain, Hugues de Thé

Bibliographic record

VenueInternational Journal of Cancer · 2014
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinoids in leukemia and cellular processes
Canadian institutionsnot available
FundersInstitute of Cancer ResearchEuropean Research CouncilInstitut National Du CancerUniversité Paris DiderotInstitut Universitaire de FranceCentre National de la Recherche ScientifiqueLigue Contre le CancerFondation ARC pour la Recherche sur le CancerInstitut National de la Santé et de la Recherche Médicale
KeywordsAcute promyelocytic leukemiaRetinoic acidCancer researchLeukemiaRetinoic acid receptor alphaChromosomal translocationRetinoic acid receptorCancerPromyelocytic leukemia proteinBiologyRetinoic acid-inducible orphan G protein-coupled receptorDiseaseMedicineBioinformaticsImmunologyGeneticsInternal medicineGene

Abstract

fetched live from OpenAlex

Inevitably fatal some 40 years, acute promyelocytic leukemia (APL) can now be cured in more than 95% of cases. This clinical success story is tightly linked to tremendous progress in our understanding of retinoic acid (RA) signaling. The discovery of retinoic acid receptor alpha (RARA) was followed by the cloning of the chromosomal translocations driving APL, all of which involve RARA. Since then, new findings on the biology of nuclear receptors have progressively enlightened the basis for the clinical efficacy of RA in APL. Reciprocally, the disease offered a range of angles to approach the cellular and molecular mechanisms of RA action. This virtuous circle contributed to make APL one of the best-understood cancers from both clinical and biological standpoints. Yet, some important questions remain unanswered including how lessons learnt from RA-triggered APL cure can help design new therapies for other malignancies.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.970
Threshold uncertainty score0.635

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.019
GPT teacher head0.360
Teacher spread0.341 · 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 designOther design
Domainnot available
GenreReview

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

Citations76
Published2014
Admission routes1
Has abstractyes

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