Neoadjuvant Enzalutamide Prior to Prostatectomy
Bibliographic record
Abstract
Abstract Purpose: Prostate cancer is dependent on androgen receptor (AR) activation. Optimal AR antagonism may effectively cytoreduce local disease and suppress or eliminate micrometastases. We evaluated neoadjuvant therapy prior to prostatectomy with the potent AR antagonist enzalutamide (enza) either alone or in combination with dutasteride (dut) and leuprolide (enza/dut/luteinizing hormone-releasing hormone analogues [LHRHa]). Experimental Design: Forty-eight of 52 men with intermediate or high-risk localized prostate cancer proceeded to prostatectomy after neoadjuvant enzalutamide or enza/dut/LHRHa for 6 months. We assessed pathologic complete response (pCR), minimal residual disease (MRD; ≤3 mm maximum diameter of residual disease), residual cancer burden (RCB), and expression of PSA and serum and tissue androgen concentrations. We compared the proportion of patients with pCR in each treatment arm with a historical control rate of 5%, based on previous reports of flutamide with LHRHa. Results: In the enzalutamide arm, none of the 25 patients achieved pCR or MRD. In the enza/dut/LHRHa arm, one of 23 patients (4.3%) achieved pCR and 3 of 23 (13.0%) achieved MRD. Median RCB was higher in the enzalutamide arm than in the enza/dut/LHRHa arm (0.41 cm3 vs. 0.06 cm3, respectively). Tissue testosterone and dihydrotestosterone levels correlated with RCB. No adverse events leading to study drug discontinuation were reported. Conclusions: Combination therapy with enza/dut/LHRHa resulted in pCR and MRD rates comparable with historical controls. Evidence of continued AR activity in residual tumor suggests that AR signaling may contribute to survival. Strategies to more effectively ablate AR activity are warranted to determine whether more substantial antitumor effects are observed. Clin Cancer Res; 23(9); 2169–76. ©2016 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".