Abstract 4361: A systematic study of the impact of estrogens and selective estrogen receptor modulators on prostate cancer cell proliferation
Bibliographic record
Abstract
Abstract The estrogen receptors ERα and ERβ are expressed in prostate cancer (PCa) cells and are believed to act as an oncogene and a tumor suppressor, respectively, and thus to be attractive therapeutic targets. Indeed, compounds modulating the activity of these receptors already exist and are currently used to treat ERα-positive breast tumors. However, there is a lot of discrepancies regarding the efficacy of anti-estrogen treatments for the management of PCa. Several factors explain these discrepancies, including unspecific antibodies against ERβ, lack of proper estrogenic positive controls, and usage of estrogens and anti-estrogens molecules at high dosages were off-target effects are observed. Our objective was to conduct a systematic study on the impact of estrogenic and anti-estrogenic ligands on PCa cell proliferation and survival. After optimization of our cellular assay using the human breast cancer cell line MCF7, we used five of the most commonly studied human PCa cell models, namely LNCaP, 22Rv1, LAPC4, DU145, and PC3 cells. These cells were treated with nine estrogenic/anti-estrogenic compounds, including five selective estrogen receptor modulators (SERMs), with and without co-treatment with androgens for androgen receptor (AR)-positive cells. In both AR-positive and AR-negative cells, we observed no significant modulation of proliferation following modulation of ERs activity. Using both RNA-seq and Western Blots, ERs expression was mostly undetectable in these models. Our study indicate that commonly used PCa models in vitro are not appropriate models to study the estrogen signaling pathway in PCa. Yet, expression data from PCa tissues indicate a significant expression of both receptors, and indicate that usage of new PCa models or of in vivo models are required to properly study drug repurposing of anti-estrogens and SERMs for PCa treatment. Citation Format: Camille Lafront, Lucas Germain, Cindy Weidmann, Etienne Audet-Walsh. A systematic study of the impact of estrogens and selective estrogen receptor modulators on prostate cancer cell proliferation [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 4361.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".