The Combination of the Rexinoid, LG100268, and a Selective Estrogen Receptor Modulator, Either Arzoxifene or Acolbifene, Synergizes in the Prevention and Treatment of Mammary Tumors in an Estrogen Receptor–Negative Model of Breast Cancer
Bibliographic record
Abstract
PURPOSE: We tested whether a selective estrogen receptor modulator (SERM) and a rexinoid are active for prevention and treatment in the mouse mammary tumor virus-neu mouse model of estrogen receptor-negative breast cancer. EXPERIMENTAL DESIGN: For prevention, mice were fed a powdered control diet, the SERM arzoxifene (Arz, 20 mg/kg diet), the rexinoid LG100268 (268, 30 mg/kg diet), or the combination for 60 weeks. In a second prevention study, mice were fed Arz (6 mg/kg diet), 268 (30 mg/kg diet), the combination of Arz and 268, the SERM acolbifene (Acol, 3 mg/kg diet), or the combination of Acol and 268 for 52 weeks. For the treatment studies, mice with tumors were fed combinations of a SERM and 268 for 4 weeks. RESULTS: The rexinoid 268 and the SERMs Arz and Acol, as individual drugs, delayed the development of estrogen receptor-negative tumors. Moreover, the combination of a SERM and 268 was strikingly synergistic, as no tumors developed in any mouse fed the combination of 268 and a SERM. Moreover, this drug combination also induced significant tumor regression when used therapeutically. These drugs did not inhibit transgene expression in vitro or in vivo, and the combination of Arz and 268 inhibited proliferation and induced apoptosis in the tumors. CONCLUSION: The combination of a rexinoid and SERM should be considered for future clinical trials.
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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 | 0.001 | 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 | 0.001 |
| 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".