Abstract 3743: Translational regulation by ERα in hormone-dependent cancers
Bibliographic record
Abstract
Abstract The estrogen receptor α (ERα) activities are complex: in the cytoplasm ERα can directly stimulate survival signalling at the cell membrane, while in the nucleus ERα activates and represses the transcription of target genes. We recently showed that in prostate cancer ERα expression is associated with increased proliferation and higher clinical grade. Here we explore the role of ERα in coordinating transcription and mRNA translation. Unexpectedly, loss of ERα expression leads to decoupling of transcription and translation events. Namely, mRNAs whose levels are induced by ERα loss exhibit reduced translation efficiency and, vice versa, mRNAs whose levels are reduced by ERα loss exhibit enhanced translation efficiency. Such regulation is manifested at the protein level and targets a range of key cellular functions including translation and metabolism. Our detailed mechanistic assessment reveal that while ERα-regulated microRNA levels contribute to global changes in mRNA levels, translational buffering is explained by changes in ribosome processivity and elongation rate. Overall, we have recently identified a process by which ERα drastically impacts the translation of a subset of mRNAs in cancer cells. We propose that this new regulatory pathway plays a major role in mediating biological effects of ERα in neoplastic tissues. Moreover, our findings have important implications in understanding alterations in gene expression programs following treatment with ERα antagonists. Citation Format: Julie Lorent, Vincent van Hoef, Richard Rebello, Mitchell Lawrence, Ivan Topisirovic, Ola Larsson, Luc Furic. Translational regulation by ERα in hormone-dependent cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3743.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".