Development of a Transcriptional Amplification System Based on the PEG3 Promoter to Target Androgen Receptor-Positive and -Negative Prostate Cancer Cells
Bibliographic record
Abstract
Localized prostate cancer (PCa) is often curable, whereas metastatic disease treated by castration inevitably progresses toward castration-resistant PCa (CRPC). Most CRPC treatments target androgen receptor (AR) signaling. However, not all CRPC cells rely on AR activity for survival and proliferation. With advances in immunotherapy and fluid biopsies for cancer management, expression systems specific for both AR-positive and -negative PCa are required for virus-based vaccines and cell imaging. To target both AR-responsive and non-responsive cells, we developed a three-step transcriptional amplification (3STA) system based on the progression elevated gene-3 (PEG3) promoter named PEG3AP1-3STA. Notably, we report on different genetic modifications that significantly improved PEG3 promoter’s strength in PCa cells. Adenoviruses incorporating PEG3 promoter with and without transcriptional amplification systems were generated. The potential of PEG3AP1-3STA to target PCa cells was then evaluated in vitro and in vivo in androgen-responsive and non-responsive PCa cell lines. PEG3AP1-3STA was shown to be active in all PCa cell lines and not regulated by androgens, and its activity was amplified 97-fold compared to that of a non-amplified promoter. The PEG3AP1-3STA system can thus be used to target advanced AR+ and AR− cells for imaging or immunovirotherapy in advanced PCa.
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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) | 0.001 | 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".