ERCC1 as a Prognostic and Predictive Biomarker for Urothelial Carcinoma of the Bladder following Radical Cystectomy
Bibliographic record
Abstract
PURPOSE: ERCC1 is the key enzyme of the nucleotide excision repair pathway, which maintains genomic stability. ERCC1 has been proposed as a prognostic and predictive biomarker for patients with urothelial carcinoma of the bladder but there are limited data on patients after radical cystectomy. MATERIALS AND METHODS: ERCC1 was evaluated by immunohistochemistry in radical cystectomy specimens of 432 patients. Associations with disease-free and cancer specific survival, and the effect of adjuvant cisplatin based chemotherapy were assessed. Further, ERCC1 mRNA expression and in vitro sensitivity to cisplatin were correlated in 25 bladder urothelial carcinoma cell lines. RESULTS: ERCC1 was expressed in 308 tumors (71.3%). There was no association with clinicopathological variables (each p >0.3). Median postoperative followup was 128 months. On multivariable analyses patients with ERCC1 positive tumors had significantly better disease-free survival (HR 0.70, p = 0.028) and cancer specific survival (HR 0.70, p = 0.032) than those with ERCC1 negative tumors. Discrimination of the multivariable models increased by 0.7% to 0.9% following the inclusion of ERCC1. There was no modification of the effect of adjuvant cisplatin based combination chemotherapy by ERCC1 status (p = 0.38 and 0.88, respectively). There was also no correlation between ERCC1 and sensitivity to cisplatin in vitro (R(2) = 0.02, p = 0.46). CONCLUSIONS: ERCC1 may be a prognostic biomarker for urothelial carcinoma of the bladder. Patients with ERCC1 positive tumors may have better survival than those with ERCC1 negative tumors. However, the efficacy of adjuvant cisplatin based chemotherapy appears to be unrelated to ERCC1 status.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".