Association of Deep and Durable Prostate-specific Antigen Responses with Outcomes in Metastatic Hormone-sensitive Prostate Cancer: Insights from ARASENS and ARANOTE Trials
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Prostate-specific antigen (PSA) is a critical biomarker in metastatic hormone-sensitive prostate cancer (mHSPC), offering insights into disease progression and treatment efficacy. An early PSA response to intensified therapy has significant prognostic implications; however, traditional methods often categorize PSA levels and focus on fixed time points, neglecting its dynamic nature. Joint models integrate longitudinal PSA trajectories and survival outcomes to provide a more comprehensive analysis. This study aims to evaluate the association between longitudinal PSA trajectories and overall survival (OS) in patients with mHSPC. METHODS: We analyzed data from two phase 3 trials: ARASENS (n = 1305; androgen deprivation therapy [ADT] + docetaxel ± darolutamide) and ARANOTE (n = 669; ADT ± darolutamide). PSA trajectories were modeled using linear mixed models with restricted cubic splines, while OS was assessed using a Weibull model. Longitudinal PSA changes and OS were linked through joint modeling. KEY FINDINGS AND LIMITATIONS: In ARASENS, each doubling of the PSA decline rate was associated with a reduction in mortality rate of 29% (hazard ratio [HR] 0.71, 95% confidence interval [CI]: 0.69, 0.74); in ARANOTE, the reduction was 25% (HR 0.75, 95% CI: 0.72, 0.78). Higher baseline PSA levels and sharper postnadir increases were linked to worse outcomes. Darolutamide-treated patients showed favorable PSA trajectories, marked by steeper declines, prolonged nadirs, and slower increases, translating into improved OS. Limitations include interim OS data in ARANOTE and influence of prior treatments on baseline PSA. CONCLUSIONS AND CLINICAL IMPLICATIONS: Joint modeling demonstrates a strong association between PSA dynamics and OS, underscoring darolutamide's potential to positively influence PSA trajectories and improve survival in mHSPC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".