Abstract A55: Histone deacetylase inhibitors as differentiation agents in breast cancer cells
Bibliographic record
Abstract
Abstract FOXA1 and GATA3 are two luminal lineage transcription factors that form a cross-regulatory transcriptional network that controls the morphogenesis of the mammary gland and regulates estrogen receptor (ER) signaling. Approximately two thirds of breast tumors overexpress ER at the time of diagnosis. Antiestrogen therapy has been effective in blocking the proliferative effects of ER, but unfortunately a significant proportion of patients will relapse due to resistance. Histone deacetylase inhibitors (HDACis) have been shown to suppress ER expression in ER+ breast cancer cells and are currently being tested in clinical trials in combination with antiestrogens for treatment of ER+ breast tumors. Here, we report that the HDACi Trichostatin A (TSA) abrogates the expression of FOXA1 and GATA3, as well as that of ER in MCF-7 breast cancer cells. Using gene expression microarrays, we observed that several markers of lactogenic differentiation including the cholesterol biosynthesis pathway were induced by TSA treatment. Moreover, expression of ER, FOXA1 and GATA3 is reduced during lactation in the mouse mammary gland. Finally, overexpression of GATA3 in MCF-7 cells reduced the TSA-mediated induction of lactogenic markers, suggesting that GATA3 may act as a repressor of lactogenic differentiation. Altogether, our studies suggest that HDACi treatment partially mimics differentiation events taking place in ER+ cells during lactogenesis. Future experiments will examine the mechanisms and clinical relevance of the differentiation properties of HDACis. Citation Format: Houssam Ismail, Martine Bail, David Laperrière, Khalid Hilmi, Sylvie Mader. Histone deacetylase inhibitors as differentiation agents in breast cancer cells. [abstract]. In: Proceedings of the AACR Special Conference on Chromatin and Epigenetics in Cancer; Jun 19-22, 2013; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2013;73(13 Suppl):Abstract nr A55.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".