Androgen Levels Increase by Intratumoral <i>De novo</i> Steroidogenesis during Progression of Castration-Resistant Prostate Cancer
Bibliographic record
Abstract
Although systemic androgen deprivation prolongs life in advanced prostate cancer, remissions are temporary because patients almost uniformly progress to a state of a castration-resistant prostate cancer (CRPC) as indicated by recurring PSA. This complex process of progression does not seem to be stochastic as the timing and phenotype are highly predictable, including the observation that most androgen-regulated genes are reactivated despite castrate levels of serum androgens. Recent evidence indicates that intraprostatic levels of androgens remain moderately high following systemic androgen deprivation therapy, whereas the androgen receptor (AR) remains functional, and silencing the AR expression following castration suppresses tumor growth and blocks the expression of genes known to be regulated by androgens. From these observations, we hypothesized that CRPC progression is not independent of androgen-driven activity and that androgens may be synthesized de novo in CRPC tumors leading to AR activation. Using the LNCaP xenograft model, we showed that tumor androgens increase during CRPC progression in correlation to PSA up-regulation. We show here that all enzymes necessary for androgen synthesis are expressed in prostate cancer tumors and some seem to be up-regulated during CRPC progression. Using an ex vivo radiotracing assays coupled to high-performance liquid chromatography-radiometric/mass spectrometry detection, we show that tumor explants isolated from CRPC progression are capable of de novo conversion of [(14)C]acetic acid to dihydrotestosterone and uptake of [(3)H]progesterone allows detection of the production of six other steroids upstream of dihydrotestosterone. This evidence suggests that de novo androgen synthesis may be a driving mechanism leading to CRPC progression following castration.
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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".