A correlative biomarker study and integrative prognostic model in chemotherapy‐naïve metastatic castration‐resistant prostate cancer treated with enzalutamide
Bibliographic record
Abstract
BACKGROUND: There is a considerable need to incorporate biomarkers of resistance to new antiandrogen agents in the management of castration-resistant prostate cancer (CRPC). METHODS: We conducted a phase II trial of enzalutamide in first-line chemo-naïve asymptomatic or minimally symptomatic mCRPC and analyzed the prognostic value of TMPRSS2-ERG and other biomarkers, including circulating tumor cells (CTCs), androgen receptor splice variant (AR-V7) in CTCs and plasma Androgen Receptor copy number gain (AR-gain). These biomarkers were correlated with treatment response and survival outcomes and developed a clinical-molecular prognostic model using penalized cox-proportional hazard model. This model was validated in an independent cohort. RESULTS: Ninety-eight patients were included. TMPRSS2-ERG fusion gene was detected in 32 patients with no differences observed in efficacy outcomes. CTC detection was associated with worse outcome and AR-V7 in CTCs was associated with increased rate of progression as best response. Plasma AR gain was strongly associated with an adverse outcome, with worse median prostate specific antigen (PSA)-PFS (4.2 vs. 14.7 m; p < 0.0001), rad-PFS (4.5 vs. 27.6 m; p < 0.0001), and OS (12.7 vs. 38.1 m; p < 0.0001). The clinical prognostic model developed in PREVAIL was validated (C-Index 0.70) and the addition of plasma AR (C-Index 0.79; p < 0.001) increased its prognostic ability. We generated a parsimonious model including alkaline phosphatase (ALP); PSA and AR gain (C-index 0.78) that was validated in an independent cohort. CONCLUSIONS: TMPRSS2-ERG detection did not correlate with differential activity of enzalutamide in first-line mCRPC. However, we observed that CTCs and plasma AR gain were the most relevant biomarkers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".