Catalytic inhibitors of DNA topoisomerase II suppress the androgen receptor signaling and prostate cancer progression
Bibliographic record
Abstract
// Haolong Li 1 , Ning Xie 1 , Martin E. Gleave 1 and Xuesen Dong 1 1 The Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Canada Correspondence to: Xuesen Dong, email: // Keywords : topoisomerase II, catalytic inhibitor, androgen receptor, prostate cancer, castration resistant prostate cancer Received : April 13, 2015 Accepted : April 23, 2015 Published : May 12, 2015 Abstract Although the new generation of androgen receptor (AR) antagonists like enzalutamide (ENZ) prolong survival of metastatic castration-resistant prostate cancer (CRPC), AR-driven tumors eventually recur indicating that additional therapies are required to fully block AR function. Since DNA topoisomerase II (Topo II) was demonstrated to be essential for AR to initiate gene transcription, this study tested whether catalytic inhibitors of Topo II can block AR signaling and suppress ENZ-resistant CRPC growth. Using multiple prostate cancer cell lines, we showed that catalytic Topo II inhibitors, ICRF187 and ICRF193 inhibited transcription activities of the wild-type AR, mutant ARs (F876L and W741C) and the AR-V7 splice variant. ICRF187 and ICRF193 decreased AR recruitment to target promoters and reduced AR nuclear localization. Both ICRF187 and ICRF193 also inhibited cell proliferation and delayed cell cycling at the G2/M phase. ICRF187 inhibited tumor growth of castration-resistant LNCaP and 22RV1 xenografts as well as ENZ-resistant MR49F xenografts. We conclude that catalytic Topo II inhibitors can block AR signaling and inhibit tumor growth of CRPC xenografts, identifying a potential co-targeting approach using these inhibitors in combination with AR pathway inhibitors in CRPC.
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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".