Prognostic value of ERG oncoprotein in prostate cancer recurrence and cause‐specific mortality
Bibliographic record
Abstract
BACKGROUND: ETS-related gene (ERG) protein is present in 40-70% of prostate cancer and is correlated with TMPRSS2-ERG gene rearrangements. This study evaluated ERG expression at radical prostatectomy to determine whether it was predictive of earlier relapse or prostate cancer-specific mortality (PCSM). METHODS: One hundred patients who underwent radical prostatectomy at Virginia Mason in Seattle between 1991 and 1997 were identified. Recurrence was confirmed by tissue diagnosis or radiographic signs. PCSM was confirmed by death certificates. Thirty-three patients with metastases or PCSM were matched to patients without recurrence at a 1:2 ratio. Paraffin embedded tissue was stained with two anti-ERG monoclonal antibodies, EPR3864 and 9FY. Nuclear expression intensity was evaluated as present/absent, on a 4-point relative intensity scale, and as a composite score (0-300). RESULTS: Mean follow-up was 10.26 years. The two antibodies were highly correlated (P < 0.0001). Patients with higher ERG expression intensity and composite scores were significantly more likely to develop biochemical relapse, metastases, and PCSM. Kaplan-Meier survival curve analysis for the composite score of ERG expression revealed a significant association between higher ERG expression (EPR3864) and shorter PCa-specific survival (P = 0.047). CONCLUSIONS: While the presence of ERG expression at the time of surgery was not predictive of earlier relapse or PCSM, the relative intensity and composite score for ERG expression was prognostic for the development of biochemical relapse, metastases, and PCSM. Quantitative ERG scoring may be useful to identify patients who would benefit from adjuvant treatment or closer follow-up, allowing more accurate individual patient treatment plans.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".