The Estrogen Receptor Pathway in Rhabdomyosarcoma: A Role for Estrogen Receptor-β in Proliferation and Response to the Antiestrogen 4′OH-Tamoxifen
Bibliographic record
Abstract
Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma in children. Highly malignant, RMS frequently fails to respond to conventional aggressive multimodal radiation, surgery, and chemotherapy treatment protocols that also cause significant sequelae in the growing child. Other tumors of mesenchymal origin, such as locally aggressive fibromatoses and desmoid tumors, have been successfully treated with a selective estrogen receptor (ER) modulator, tamoxifen. In an effort to identify new targets for RMS therapy, our group investigated the previously uncharacterized ER pathway in RMS cell culture and primary tumors. We detected ER isoform beta (ER beta), but not isoform alpha, RNA, and protein in five RMS cell lines. Immunohistochemical staining of primary RMS tumor sections confirmed high levels of ER beta but not ER alpha protein. RMS cell growth was dramatically inhibited in steroid-free conditions, and this growth inhibition was rescued with 17-beta-estradiol (E2) supplementation. Exposure of RMS cells to 4'OH-tamoxifen (4OHT) decreased cell viability and inhibited colony formation as detected by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide and colony-forming assays. 4OHT also induced apoptotic signaling in RMS cells as detected by cleavage of caspase-3 and poly(ADP)ribose polymerase. This effect increased 3- to 8-fold in steroid-deprived conditions but was rescued by supplementation with E2. Immunofluorescence studies detected a change in the subcellular localization of ER beta in response to 4OHT. Together, these data suggest an active ER beta-mediated signal transduction pathway in RMS. The ability of 4OHT to induce apoptotic signaling and disrupt estradiol-mediated proliferation provides a rationale to explore a role for selective ER modulators in the treatment of RMS.
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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".