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Record W2747905863 · doi:10.1158/1535-7163.mct-17-0292

Estrogen Receptor β Is a Novel Target in Acute Myeloid Leukemia

2017· article· en· W2747905863 on OpenAlexaff
Sarah-Grace Rota, Alessia Roma, Iulia Dude, Christina Ma, Robert S. Stevens, Janet MacEachern, J Graczyk, Shaundrei Mabriel G. Espiritu, Praveen P. N. Rao, Mark D. Minden, Elena Kreinin, David A. Hess, Andrew C. Doxey, Paul A. Spagnuolo

Bibliographic record

VenueMolecular Cancer Therapeutics · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEstrogen and related hormone effects
Canadian institutionsWestern UniversityGrand River HospitalUniversity of GuelphRegional Municipality of WaterlooOntario Institute for Cancer ResearchUniversity of Waterloo
Fundersnot available
KeywordsMyeloid leukemiaEstrogen receptorLeukemiaCancer researchHaematopoiesisGene knockdownPharmacologyBiologyMyeloidStem cellCell cultureCancerImmunologyCell biologyBreast cancer

Abstract

fetched live from OpenAlex

Abstract Acute myeloid leukemia (AML) is a devastating disease characterized by poor patient outcome and suboptimal chemotherapeutics. Here, a high-throughput screen identified diosmetin, a citrus flavonoid, with anti-AML activity. Diosmetin imparted selective toxicity against leukemia and leukemia stem cells in vitro and in vivo with no effect on normal hematopoietic stem cells. Mechanistically, we demonstrated that diosmetin targets estrogen receptor (ER) β. ERβ expression conferred cell sensitivity, as patient-derived AML cells with high levels of ERβ were sensitive, whereas cells with low ERβ were insensitive to diosmetin. Knockdown of ERβ confirmed resistance, whereas overexpression enhanced sensitivity to diosmetin, which was demonstrated to be mediated by reactive oxygen species signaling. In summary, these studies highlight targeting of ERβ with diosmetin as a potential novel therapeutic strategy for the treatment of AML. Mol Cancer Ther; 16(11); 2618–26. ©2017 AACR.

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 categoriesMeta-epidemiology (narrow)
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 score0.087
Threshold uncertainty score1.000

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 science0.0010.000
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 head0.014
GPT teacher head0.283
Teacher spread0.269 · 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

Citations40
Published2017
Admission routes1
Has abstractyes

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