Bypassing Drug Resistance Mechanisms of Prostate Cancer with Small Molecules that Target Androgen Receptor–Chromatin Interactions
Bibliographic record
Abstract
Abstract Human androgen receptor (AR) is a hormone-activated transcription factor that is an important drug target in the treatment of prostate cancer. Current small-molecule AR antagonists, such as enzalutamide, compete with androgens that bind to the steroid-binding pocket of the AR ligand–binding domain (LBD). In castration-resistant prostate cancer (CRPC), drug resistance can manifest through AR-LBD mutations that convert AR antagonists into agonists, or by expression of AR variants lacking the LBD. Such treatment resistance underscores the importance of novel ways of targeting the AR. Previously, we reported the development of a series of small molecules that were rationally designed to selectively target the AR DNA-binding domain (DBD) and, hence, to directly interfere with AR–DNA interactions. In the current work, we have confirmed that the lead AR DBD inhibitor indeed directly interacts with the AR-DBD and tested that substance across multiple clinically relevant CRPC cell lines. We have also performed a series of experiments that revealed that genome-wide chromatin binding of AR was dramatically impacted by the lead compound (although with lesser effect on AR variants). Collectively, these observations confirm the novel mechanism of antiandrogen action of the developed AR-DBD inhibitors, establishing proof of principle for targeting DBDs of nuclear receptors in endocrine cancers. Mol Cancer Ther; 16(10); 2281–91. ©2017 AACR.
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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) | 0.003 | 0.001 |
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".