Correlation between Prostate-Specific Antigen Kinetics and Overall Survival in Abiraterone Acetate–Treated Castration-Resistant Prostate Cancer Patients
Bibliographic record
Abstract
PURPOSE: We constructed a biomarker-survival modeling framework to explore the relationship between prostate-specific antigen (PSA) kinetics and overall survival (OS) in metastatic castration-resistant prostate cancer (mCRPC) patients following oral administration of 1,000 mg/day of abiraterone acetate (AA). EXPERIMENTAL DESIGN: The PSA-survival modeling framework was based on data from two phase III studies, COU-AA-301 (chemotherapy pretreated, n = 1,184) and COU-AA-302 (chemotherapy naïve, n = 1,081), and included a mixed-effects tumor growth inhibition model and a Cox proportional hazards survival model. RESULTS: The effect of AA on PSA kinetics was significant (P < 0.0001) and comparable between the chemotherapy-naïve and -pretreated patients. PSA kinetics [e.g., PSA nadir, PSA response rate (≥30%, 50%, and 90%), time to PSA progression, PSA doubling time (PSADT)] were highly associated with OS in both populations. The model-based posttreatment PSADT had the strongest association with OS (HR ∼0.9 in both populations). The models could accurately predict survival outcomes. After adjusting for PSA kinetic endpoints, the treatment effect of AA on survival was no longer statistically significant in both studies, and the Prentice criteria of surrogacy were met for the PSA kinetic endpoints. A strong correlation was also observed between PSA and radiographic progression-free survival. CONCLUSIONS: The analysis revealed a consistent treatment effect of AA on PSA kinetics and strong associations between PSA kinetics and OS in chemotherapy-pretreated and -naïve patients, thereby providing a rationale to consider PSA kinetics as surrogacy endpoints to indicate clinical benefit in AA-treated patients with mCRPC regardless of chemotherapy treatment.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".