Data from Transcription Factor Stat5 Knockdown Enhances Androgen Receptor Degradation and Delays Castration-Resistant Prostate Cancer Progression <i>In vivo</i>
Bibliographic record
Abstract
Abstract Signal transducer and activator of transcription 5 (Stat5) plays an important role in the transition of prostate cancer (PCa) to its castrate-resistant state. Pharmacologic targeting of Stat5 is a rational approach to delay castrate-resistant progression, in part, because Stat5 cooperates with the androgen receptor (AR) to promote PCa progression. Immunostaining of tissue microarrays was used to correlate Stat5 expression with Gleason grade and to characterize changes in treatment-naive and androgen-deprived human PCa. Potency of a Stat5 antisense oligonucleotide (ASO) on Stat5 knockdown, cell growth, and apoptosis was assessed in LNCaP, C4-2, and DU145 cells. Effects of Stat5 knockdown on AR activity and stability was assessed using a PSA transactivation-luciferase assay and cyclohexamide plus MG132 treatment, respectively. LNCaP tumor-bearing mice were castrated and randomly assigned to treatment with Stat5-ASO or controls. Here, we show that the frequency of Stat5 expression is significantly increased in high Gleason grade as well as in hormone-treated PCa. Also, specific knockdown of Stat5 with ASO abrogates androgen-induced AR nuclear translocation and PSA transactivation despite R1881 stimulation. Moreover, Stat5 knockdown destabilizes AR, which leads to AR degradation via the proteasome. Shown for the first time as a preclinical proof-of-principle, Stat5 knockdown with Stat5-ASO significantly delays CRPC tumor progression in vivo. Thereby, we are able to recapitulate our in vitro results by reducing serum PSA and expression levels of target proteins in the xenograft tumors. Mol Cancer Ther; 10(2); 347–59. ©2011 AACR.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".