Short Hairpin RNA Knockdown of the Androgen Receptor Attenuates Ligand-Independent Activation and Delays Tumor Progression
Bibliographic record
Abstract
Progression to androgen independence is the lethal end stage of prostate cancer. We used expression of androgen receptor (AR)-targeted short hairpin RNAs (shRNA) to directly test the requirement for AR in ligand-independent activation of androgen-regulated genes and hormone-independent tumor progression. Transient transfection of LNCaP human prostate cancer cells showed that AR shRNA decreased R1881 induction of the prostate-specific antigen (PSA)-luciferase reporter by 96%, whereas activation by forskolin, interleukin-6, or epidermal growth factor was inhibited 48% to 75%. Whereas the antiandrogen bicalutamide provided no further suppression, treatment with the mitogen-activated protein kinase (MAPK) inhibitor U0126 completely abrogated the residual activity, indicating a MAPK-dependent, AR-independent pathway for regulating the PSA promoter. Expression of doxycycline-inducible AR shRNA expression in LNCaP cells resulted in decreased levels of AR and PSA as well as reduced proliferation in vitro. When these cells were grown as xenografts in immunocompromised mice, induction of AR shRNA decreased serum PSA to below castration nadir levels and significantly retarded tumor growth over the entire 55-day experimental period. This is the first demonstration that, by inducibly suppressing AR expression in vivo, there is an extensive delay in progression to androgen independence as well as a dramatic inhibition of tumor growth and decrease in serum PSA, which exceeds that seen with castration alone. Based on these findings, we propose that suppressing AR expression may provide superior therapeutic benefit in reducing tumor growth rate than castration and may additionally be very effective in delaying progression to androgen independence.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".