Improvements in Radiographic Progression-Free Survival Stratified by <i>ERG</i> Gene Status in Metastatic Castration-Resistant Prostate Cancer Patients Treated with Abiraterone Acetate
Bibliographic record
Abstract
PURPOSE: Gene fusions leading to androgen receptor-modulated ERG overexpression occur in up to 70% of metastatic castration-resistant prostate cancers (mCRPC). We assessed the association between ERG rearrangement status and clinical benefit from abiraterone acetate. EXPERIMENTAL DESIGN: COU-AA-302 is a phase III trial comparing abiraterone acetate and prednisone versus prednisone in chemotherapy-naïve mCRPC. ERG status was evaluated by FISH on archival tumors. End points included radiographic progression-free survival (rPFS), time to PSA progression (TTPP), rate of ≥50% PSA decline from baseline, and overall survival (OS). Cox regression was used to evaluate association with time-to-event measures and Cochran-Mantel-Haenszel for PSA response. RESULTS: ERG status was defined for 348 of 1,088 intention-to-treat patients. ERG was rearranged in 121 of 348 patients with confirmed ERG status (35%). Cancers with an ERG fusion secondary to deletion of 21q22 and increased copy number of fusion sequences (class 2+ Edel) had a greater improvement in rPFS after abiraterone acetate and prednisone [22 vs. 5.4 months; HR (95% confidence interval, CI), 0.31 (0.15-0.68); P = 0.0033] than cancers with no ERG fusion [16.7 vs. 8.3 months; 0.53 (0.38-0.74); P = 0.0002] or other classes of ERG rearrangement. There was also greater benefit in this subgroup for TTPP. CONCLUSIONS: Both ERG-rearranged and wild-type cancers had a significant improvement in rPFS with abiraterone acetate and prednisone in the COU-AA-302 trial. However, our data suggest that 2+ Edel cancers, accounting for 15% of all mCRPC patients and previously associated with a worse outcome, derived the greatest benefit.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".