MRE11 Expression Is Predictive of Cause-Specific Survival following Radical Radiotherapy for Muscle-Invasive Bladder Cancer
Bibliographic record
Abstract
Radical radiotherapy and surgery achieve similar cure rates in muscle-invasive bladder cancer, but the choice of which treatment would be most beneficial cannot currently be predicted for individual patients. The primary aim of this study was to assess whether expression of any of a panel of DNA damage signaling proteins in tumor samples taken before irradiation could be used as a predictive marker of radiotherapy response, or rather was prognostic. Protein expression of MRE11, RAD50, NBS1, ATM, and H2AX was studied by immunohistochemistry in pretreatment tumor specimens from two cohorts of bladder cancer patients (validation cohort prospectively acquired) treated with radical radiotherapy and one cohort of cystectomy patients. In the radiotherapy test cohort (n = 86), low tumor MRE11 expression was associated with worse cancer-specific survival compared with high expression [43.1% versus 68.7% 3-year cause-specific survival (CSS), P = 0.012] by Kaplan-Meier analysis. This was confirmed in the radiotherapy validation cohort (n = 93; 43.0% versus 71.2%, P = 0.020). However, in the cystectomy cohort (n = 88), MRE11 expression was not associated with cancer-specific survival, commensurate with MRE11 being a predictive marker. High MRE11 expression in the combined radiotherapy cohort had a significantly better cancer-specific survival compared with the high-expression cystectomy cohort (69.9% versus 53.8% 3-year CSS, P = 0.021). In this validated immunohistochemistry study, MRE11 protein expression was shown and confirmed as a predictive factor associated with survival following bladder cancer radiotherapy, justifying its inclusion in subsequent trial designs. MRE11 expression may ultimately allow patient selection for radiotherapy or cystectomy, thus improving overall cure rates.
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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 | 0.000 | 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".