Inhibition of the Androgen Receptor as a Novel Mechanism of Taxol Chemotherapy in Prostate Cancer
Bibliographic record
Abstract
Taxol chemotherapy is one of the few therapeutic options for men with castration-resistant prostate cancer (CRPC). However, the working mechanisms for Taxol are not fully understood. Here, we showed that treatment of 22Rv1, a PTEN-positive CRPC cell line, with paclitaxel and its semisynthetic analogue docetaxel decreases expression of the androgen receptor (AR)-activated genes prostate-specific antigen (PSA) and Nkx3.1 but increases expression of the AR repression gene maspin, suggesting that Taxol treatment inhibits AR activity. This was further supported by the observation that the activity of AR luciferase reporter genes was inhibited by paclitaxel. In contrast, paclitaxel treatment failed to inhibit AR activity in the PTEN-null CRPC cell line C4-2. However, pretreatment of C4-2 cells with the phosphoinositide 3-kinase inhibitor LY294002 restored paclitaxel inhibition of the AR. Treatment of 22Rv1 xenografts in mice with docetaxel induced mitotic arrest and a decrease in PSA expression in tumor cells adjacent to vascular vessels. We further showed that paclitaxel induces nuclear accumulation of FOXO1, a known AR suppressive nuclear factor, and increases the association of FOXO1 with AR proteins in the nucleus. FOXO1 knockdown with small interfering RNA attenuated the inhibitory effect of paclitaxel on AR transcriptional activity, expression of PSA and Nkx3.1, and cell survival. These data reveal a previously uncharacterized, FOXO1-mediated, AR-inhibitory effect of Taxol in CRPC cells that may play an important role in Taxol-mediated inhibition of CRPC growth.
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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".