Androgen receptor decoy molecules block the growth of prostate cancer
Bibliographic record
Abstract
The androgen receptor (AR) is activated by both ligand-dependent and -independent mechanisms. Current therapies for prostate cancer target the ligand-binding domain in the C terminus of the AR. However, ligand-independent activation of the AR occurs by the N-terminal domain (NTD), making the NTD a potential novel target for the treatment of hormone refractory prostate cancer. A possible therapeutic approach is to overexpress an AR NTD peptide to create decoy molecules that competitively bind the interacting proteins required for activation of the endogenous full-length AR. We provide evidence that in vivo expression of AR NTD decoys decreased tumor incidence and inhibited the growth of prostate cancer tumors. This growth inhibition was characterized by a 10-fold decrease in serum levels of prostate-specific antigen (PSA) (46.7 ng/ml+/-19.9 vs. 432.4 ng/ml+/-201.3; P=0.0299) and a 4-fold decrease in tumor volume (92.2 mm3+/-43.4 vs. 331.4 mm3+/-85.5; P=0.011). AR NTD decoy molecules also delayed hormonal progression, as determined by time to rising PSA levels after castration of the host. The tumors treated with AR NTD decoys contained more apoptotic cells and fewer proliferating cells, whereas no effect was seen on the viability of cells that did not depend on the AR. This work provides further evidence of the importance of the NTD of the AR in the progression of prostate cancer and presents a target for the development of antagonists of the AR in the clinical management of this disease.
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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) | 0.001 | 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".