A physiologic role for testosterone in limiting estrogenic stimulation of the breast
Bibliographic record
Abstract
OBJECTIVE: The normal ovary produces abundant testosterone in addition to estradiol (E(2)) and progesterone, but usually only the latter two hormones are "replaced" in the treatment of ovarian failure and menopause. Some clinical and genetic evidence suggests, however, that endogenous androgens normally inhibit estrogen-induced mammary epithelial proliferation (MEP) and thereby may protect against breast cancer. DESIGN: To investigate the role of endogenous androgen in regulating mammary epithelial proliferation, normal-cycling rhesus monkeys were treated with flutamide, an androgen receptor antagonist. To evaluate the effect of physiological testosterone (T) supplementation of estrogen replacement therapy, ovariectomized monkeys were treated with E(2), E(2) plus progesterone, E(2) plus T, or vehicle. RESULTS: We show that androgen receptor blockade in normal female monkeys results in a more than twofold increase in MEP, indicating that endogenous androgens normally inhibit MEP. Moreover, we show that addition of a small, physiological dose of T to standard estrogen therapy almost completely attenuates estrogen-induced increases in MEP in the ovariectomized monkey, suggesting that the increased breast cancer risk associated with estrogen treatment could be reduced by T supplementation. Testosterone reduces mammary epithelial estrogen receptor (ER) alpha and increases ERbeta expression, resulting in a marked reversal of the ERalpha/beta ratio found in the estrogen-treated monkey. Moreover, T treatment is associated with a significant reduction in mammary epithelial MYC expression, suggesting that T's antiestrogenic effects at the mammary gland involve alterations in ER signaling to MYC. CONCLUSIONS: These findings suggest that treatment with a balanced formulation including all ovarian hormones may prevent or reduce estrogenic cancer risk in the treatment of girls and women with ovarian failure.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".