Paracrine sonic hedgehog signaling contributes significantly to acquired steroidogenesis in the prostate tumor microenvironment
Bibliographic record
Abstract
Despite the substantial benefit of androgen deprivation therapy (ADT) for metastatic prostate cancer, patients often progress to castration-resistant disease (CRPC) that is more difficult to treat. CRPC is associated with renewed androgen receptor activity in tumor cells and restoration of tumor androgen levels through acquired intratumoral steroidogenesis (AIS). Although prostate cancer (PCa) cells have been shown to have steroidogenic capability in vitro, we previously found that benign prostate stromal cells (PrSCs) can also synthesize testosterone (T) from an adrenal precursor, DHEA, when stimulated with a hedgehog (Hh) pathway agonist, SAG. Here, we show exposure of PrSCs to a different Smoothened (Smo) agonist, Ag1.5, or to conditioned medium from sonic hedgehog overexpressing LNCaP cells induces steroidogenic enzyme expression in PrSCs and significantly increases production of T and its precursor steroids in a Smo-dependent manner from 22-OH-cholesterol substrate. Hh agonist-/ligand-treated PrSCs produced androgens at a rate similar to or greater than that of PCa cell lines. Likewise, primary bone marrow stromal cells became more steroidogenic and produced T under the influence of Smo agonist. Treatment of mice bearing LNCaP xenografts with a Smo antagonist, TAK-441, delayed the onset of CRPC after castration and substantially reduced androgen levels in residual tumors. These outcomes support the idea that stromal cells in ADT-treated primary or metastatic prostate tumors can contribute to AIS as a consequence of a paracrine Hh signaling microenvironment. As such, Smo antagonists may be useful for targeting prostate tumor stromal cell-derived AIS and delaying the onset of CRPC after ADT.
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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".