Abstract 2105: Regulation of estrogen receptor turnover by lysine 302 methylation
Bibliographic record
Abstract
Abstract The estrogen pathway promotes growth of breast cancer, the leading cancer diagnosis among US women. The antiestrogen therapy Tamoxifen has been the mainline therapy for the 70% of women whose tumors are estrogen receptor alpha (ERα) positive for over thirty years. Understanding how ERα is regulated, particularly in the context of Tamoxifen, is essential for the treatment of breast cancer. In previous work, we showed that SetD7 mediated methylation of ERα at lysine 302 (K302) is a key mediator of estrogen receptor alpha stability. Downregulation of SetD7 or mutation of K302 increased the rate of ERα turnover resulting in a compromised estrogen driven transcriptional response. However, the precise mechanism by which lysine 302 methylation regulates ERα stability and transcriptional activity is not yet known. In this study, we used a novel small molecule inhibitor of SetD7, (R)-PFI-2, as a chemical probe to further investigate the role of K302 methylation in the regulation of ERα. Treatment of ERα expressing MCF7 breast cancer cells with (R)-PFI-2 resulted in a dose-dependent shift in the 66kDa ERα species to a slower migrating form as detected by protein electrophoresis and immunoblotting. Molecular mass estimates of this slower migrating form are consistent with a sumoylation event. Interestingly, (R)-PFI-2-induced accumulation of the slower migrating form was only observed upon inhibition of the proteasome with MG132, suggesting that this alternately modified form of ERα represents an intermediate in the pathway to degradation. A similar time-dependent shift to the same slower migrating form was observed upon estrogen depletion of MCF7 cells stably knocked down for SetD7, but not in control cells. Furthermore, an inverse relationship was observed between endogenous levels of SetD7 and the levels of modified ER in two strains of MCF7 cell line that differ in their sensitivity to Tamoxifen. These data suggest that methylation of ERα at K302 by SetD7 may stabilize ERα by blocking another post-translational modification, possibly sumoylation, necessary for its turnover. The relationship between ER methylation, sumoylation and Tamoxifen sensitivity will be discussed. Citation Format: Elizabeth L. Zoeller, Dalia Barsyte-Lovejoy, Peter J. Brown, Dafydd R. Owen, Cheryl H. Arrowsmith, Paula M. Vertino. Regulation of estrogen receptor turnover by lysine 302 methylation. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2105. doi:10.1158/1538-7445.AM2014-2105
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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) | 0.003 | 0.001 |
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.
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".