BMX controls 3βHSD1 and sex steroid biosynthesis in cancer
Bibliographic record
Abstract
Prostate cancer is highly dependent on androgens and the androgen receptor (AR). Hormonal therapies inhibit gonadal testosterone production, block extragonadal androgen biosynthesis, or directly antagonize AR. Resistance to medical castration occurs as castration-resistant prostate cancer (CRPC) and is driven by reactivation of the androgen-AR axis. 3β-hydroxysteroid dehydrogenase-1 (3βHSD1) serves as the rate-limiting step for potent androgen synthesis from extragonadal precursors, thereby stimulating CRPC. Genetic evidence in men demonstrates the role of 3βHSD1 in driving CRPC. In postmenopausal women, 3βHSD1 is required for synthesis of aromatase substrates and plays an essential role in breast cancer. Therefore, 3βHSD1 lies at a critical junction for the synthesis of androgens and estrogens, and this metabolic flux is regulated through germline-inherited mechanisms. We show that phosphorylation of tyrosine 344 (Y344) occurs and is required for 3βHSD1 cellular activity and generation of Δ4, 3-keto-substrates of 5α-reductase and aromatase, including in patient tissues. BMX directly interacts with 3βHSD1 and is necessary for enzyme phosphorylation and androgen biosynthesis. In vivo blockade of 3βHSD1 Y344 phosphorylation inhibits CRPC. These findings identify what we believe to be new hormonal therapy pharmacologic vulnerabilities for sex-steroid dependent cancers.
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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) | 0.003 | 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".