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Record W3083509257 · doi:10.1158/1538-7445.am2020-4375

Abstract 4375: Inhibition of triple negative breast cancer cell progression using estrogen related receptor alpha endogenous ligands

2020· article· en· W3083509257 on OpenAlexaff
Faegheh Ghanbari, Sylvie Mader, Anie Philip

Bibliographic record

VenueCancer Research · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEstrogen and related hormone effects
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsTriple-negative breast cancerBreast cancerEstrogen receptorCancer researchReceptorAgonistEstrogen-related receptor alphaEstrogenEstrogen receptor alphaCancerEndogenyInternal medicineEstrogen receptor betaEndocrinologyMedicineBiologyPharmacologyOncology

Abstract

fetched live from OpenAlex

Abstract Triple-negative breast cancer (TNBC), which lacks estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), is considered to be more metastatic, and has poorer prognosis and higher risk of recurrence than other subtypes of breast cancer. The death rate in patients with TNBC is twice that of ERα positive tumors mainly because there are fewer targeted therapies that treat TNBC patients [1]. Therefore, there is a need to discover new drug-targeted therapy for these patients. Several lines of evidence indicate that estrogen related receptors (ERRs), which belong to the orphan nuclear receptor superfamily, play a crucial role in breast cancer, with ERRα overexpression reportedly leading to adverse clinical outcomes in TNBC patients [2]. Despite intensive efforts, no endogenous ligand has been identified for ERRs so far. The discovery of ligands that bind these receptors may lead to novel strategies for the treatment of TNBC. In this study we show two endogenous ligands of ERRs: 1) estradienolone (ED), a novel endogenous steroid during pregnancy which acts as an inverse agonist of ERRs, 2) cholesterol as an agonist of ERRs. Our recent results show that ED acts as an inverse agonist of ERRα and ERRγ by directly interacting with these receptors, and inhibiting their transcriptional activity. We also demonstrate that ED has strong anti-mitogenic properties. ED inhibits the growth of both estrogen receptor-positive (MCF-7) and estrogen receptor-negative (MDA-MB-231) breast cancer cells in a dose dependent manner, while of displaying a little effect on normal epithelial breast cells. In addition, we show that cholesterol binds directly and specifically to ERRα, it increases the transcriptional activity of ERRα in a peroxisome proliferator-activated receptor coactivator-1α (PGC-1) dependent manner. Our finding suggests that cholesterol enhances the interaction of ERRα and its coactivator PGC-1, and this leads to induce the expression of ERRα itself (an specific auto-induction) and the metabolic target genes of ERRα to fuel TNBC cells proliferation and migration. Moreover, our results demonstrates that the effect of cholesterol or lovastatin (a drug known to inhibit cholesterol synthesis) in triple negative breast cancer (MDA-MB 231) requires ERRα. These data suggest that both ED-ERR and cholesterol-ERR interactions may represent novel druggable signaling pathway in TNBC. Citation Format: Faegheh Ghanbari, Sylvie Mader, Anie Philip. Inhibition of triple negative breast cancer cell progression using estrogen related receptor alpha endogenous ligands [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4375.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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)0.0030.001

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 head0.051
GPT teacher head0.355
Teacher spread0.304 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2020
Admission routes1
Has abstractyes

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