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 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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".