Galiellalactone inhibits the STAT3/AR signaling axis and suppresses Enzalutamide-resistant Prostate Cancer
Bibliographic record
Abstract
Abstract Most prostate cancer patients will progress to a castration-resistant state (CRPC) after androgen ablation therapy and despite the development of new potent anti-androgens, like enzalutamide (ENZ), which prolong survival in CRPC, ENZ-resistance (ENZR) rapidly occurs. Re-activation of the androgen receptor (AR) is a major mechanism of resistance. Interrogating our in vivo derived ENZR model, we discovered that transcription factor STAT3 not only displayed increased nuclear localization but also bound to and facilitated AR activity. We observed increased STAT3 S727 phosphorylation in ENZR cells, which has been previously reported to facilitate AR binding. Strikingly, ENZR cells were more sensitive to inhibition with STAT3 DNA-binding inhibitor galiellalactone (GPA500) compared to CRPC cells. Treatment with GPA500 suppressed AR activity and significantly reduced expression of Cyclin D1, thus reducing cell cycle progression into S phase and hindering cell proliferation. In vivo, GPA500 reduced tumor volume and serum PSA in ENZR xenografts. Lastly, the combination of ENZ and GPA500 was additive in the inhibition of AR activity and proliferation in LNCaP and CRPC cells, providing rationale for combination therapy. Overall, these results suggest that STAT3 inhibition is a rational therapeutic approach for ENZR prostate cancer, and could be valuable in CRPC in combination with ENZ.
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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".