Nuclear Receptor Interactions in Breast Cancer: The Role of Kinase Signaling Pathways
Bibliographic record
Abstract
Retinoids are vitamin A derivatives, which cause growth inhibition, differentiation and/or apoptosis in various cell types, including some breast cancer cells. In general, estrogen receptor (ER)-positive cells are retinoic acid (RA) sensitive, whereas ER-negative cells are resistant. In this report, I show that ER-negative MDA-MB-231 cells are strongly growth inhibited by retinoids in combination with a PKC inhibitor. While neither RA nor GF109203X (GF) has a significant growth inhibitory effect in these cells, RA+GF potently suppress proliferation and induce apoptosis. Moreover, GF was found to enhance RA-induced transcriptional activation of an RARE reporter construct. Expression of phosphorylated as well as total PKC alpha and delta was decreased by GF and this was potentiated by RA. In addition, treatment with GF caused a sustained activation of ERK1/2 and p38-MAPK. Importantly, inhibition of ERK but not p38 or JNK suppressed apoptosis induced by RA+GF, indicating that activation of ERK is specifically required. In support of this novel finding, the ability of other PKC inhibitors to cause apoptosis in combination with RA correlates with ability to cause sustained activation of ERK. Moreover, it appears that inhibition/downregulation of PKC delta is specifically involved.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".