Profiling of the transcriptional response to all-trans retinoic acid in breast cancer cells reveals RARE-independent mechanisms of gene expression
Bibliographic record
Abstract
Retinoids, derivatives of vitamin A, are key physiological molecules with regulatory effects on cell differentiation, proliferation and apoptosis. As a result, they are of interest for cancer therapy. Specifically, models of breast cancer have varied responses to manipulations of retinoid signaling. This study characterizes the transcriptional response of MDA-MB-231 and MDA-MB-468 breast cancer cells to retinaldehyde dehydrogenase 1A3 (ALDH1A3) and all-trans retinoic acid (atRA). We demonstrate limited overlap between ALDH1A3-induced gene expression and atRA-induced gene expression in both cell lines, suggesting that the function of ALDH1A3 in breast cancer progression extends beyond its role as a retinaldehyde dehydrogenase. Our data reveals divergent transcriptional responses to atRA, which are largely independent of genomic retinoic acid response elements (RAREs) and consistent with the opposing responses of MDA-MB-231 and MDA-MB-468 to in vivo atRA treatment. We identify transcription factors associated with each gene set. Manipulation of the IRF1 transcription factor demonstrates that it is the level of atRA-inducible and epigenetically regulated transcription factors that determine expression of target genes (e.g. CTSS, cathepsin S). This study provides a paradigm for complex responses of breast cancer models to atRA treatment, and illustrates the need to characterize RARE-independent responses to atRA in a variety of models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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) | 0.000 | 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 teacher head, 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".