Abstract 1003: Modulating estrogen related receptors (ERRs) activity in breast cancer using endogenous ligands
Bibliographic record
Abstract
Abstract Estrogen-receptor related receptors which consists of ERRα, ERRβ and ERRγ belong to the orphan nuclear receptor subfamily of NR3B (nuclear receptor subfamily 3, group B). The ERRs have been shown to actively modulate estrogenic responses, and to play an essential role in pregnancy, and are implicated in breast cancer progression. Despite intensive efforts, no endogenous ligand has been identified for ERRs so far. The discovery of ligands that bind these orphan receptors will allow the manipulation of this pathway and may lead to novel strategies for the treatment of cancer and other diseases. 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 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 physiologically relevant ERR pathways in the human. Citation Format: Faegheh Ghanbari, Sylvie Mader, Anie Philip. Modulating estrogen related receptors (ERRs) activity in breast cancer using endogenous ligands [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1003.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".