Selective estrogen receptor modulators and betulinic acid act synergistically to target ERα and SP1 transcription factor dependent Pygopus expression in breast cancer
Bibliographic record
Abstract
AIMS: Estrogen and progesterone hormone receptor (ER and PR) expression in invasive breast cancer predicts response to hormone disruptive therapy. Pygopus2 (hPYGO2) encodes a chromatin remodelling protein important for breast cancer growth and cell cycle progression. The aims of this study were to determine the mechanism of expression of hPYGO2 in breast cancer and to examine how this expression is affected therapeutically. METHODS: hPYGO2 and ER protein expression was examined in a breast tumour microarray by immunohistochemistry. hPYGO2 RNA and protein expression was examined in ER+ and ER- breast cancer cell lines in the presence of selective estrogen hormone receptor modulator drugs and the specificity protein-1 (SP1) inhibitor, betulinic acid (BA). The effects of these drugs on the ability for ER and SP1 to bind the hPYGO2 promoter and affect cell cycle progression were studied using chromatin immunoprecipitation assays. RESULTS: hPYGO2 was expressed in seven of eight lines and in nuclei of 98% of 65 breast tumours, including 3 Ductal carcinoma in situ and 62 invasive specimens representing ER-negative (22%) and ER-positive (78%) cases. Treatment with either 4-Hydroxytamoxifen (OHT) or fulvestrant reduced hPYGO2 mRNA 10-fold and protein 5-10-fold within 4 h. Promoter analysis indicated an ER/SP1 binding site at nt -225 to -531 of hPYGO2. SP1 RNA interference and BA reduced hPYGO2 protein and RNA expression by fivefold in both ER- and ER+ cells. Further attenuation was achieved by combining BA and 4-OHT resulting in eightfold reduction in cell growth. CONCLUSIONS: Our findings reveal a mechanistic link between hormone signalling and the growth transcriptional programme. The activation of its expression by ERα and/or SP1 suggests hPYGO2 as a theranostic target for hormone therapy responsive and refractory breast cancer.
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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) | 0.001 | 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 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".