Targeting Androgen Receptor Activation Function-1 with EPI to Overcome Resistance Mechanisms in Castration-Resistant Prostate Cancer
Bibliographic record
Abstract
PURPOSE: Persistent androgen receptor (AR) transcriptional activity is clinically evident in castration-resistant prostate cancer (CRPC). Therefore, AR remains as a viable therapeutic target for CRPC. All current hormonal therapies target the C-terminus ligand-binding domain (LBD) of AR. By using EPI to target AR activation function-1 (AF-1), in the N-terminal domain that is essential for AR transactivation, we evaluate the ability of EPI to overcome several clinically relevant AR-related mechanisms of resistance. EXPERIMENTAL DESIGN: To study the effect of EPI on AR transcriptional activity against overexpressed coactivators, such as SRC1-3 and p300, luciferase reporter assays were performed using LNCaP cells. AR-negative COS-1 cells were employed for reporter assays to examine whether the length of polyglutamine tract affects inhibition by EPI. The effect of EPI on constitutively active AR splice variants was studied in LNCaP95 cells, which express AR-V7 variant. To evaluate the effect of EPI on the proliferation of LNCaP95 cells, we performed in vitro BrdUrd incorporation assay and in vivo studies using xenografts in mice. RESULTS: EPI effectively overcame several molecular alterations underlying aberrant AR activity, including overexpressed coactivators, AR gain-of-function mutations, and constitutively active AR-V7. EPI inhibited AR transcriptional activity regardless of the length of polyglutamine tract. Importantly, EPI significantly inhibited the in vitro and in vivo proliferation of LNCaP95 prostate cancer cells, which are androgen independent and enzalutamide resistant. CONCLUSIONS: These findings support EPI as a promising therapeutic agent to treat CRPC, particularly against tumors driven by constitutively active AR splice variants that are resistant to LBD-targeting drugs. Clin Cancer Res; 22(17); 4466-77. ©2016 AACRSee related commentary by Sharp et al., p. 4280.
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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".