The Search for the Optimal cut-off Value of p53-Immunohistochemistry to Predict Prognosis of Invasive Bladder Cancer: A Multi-Center, Multi-Laboratory Analysis
Bibliographic record
Abstract
Introduction: Mutations in the TP53 gene are indicative of worse outcome in bladder cancer and are usually assessed by immunohistochemistry. To define p53-overexpression, a threshold of >10% is most commonly used (cut-off1). Recently, a novel cut-off (aberrant = 0% or ≥50%) (cut-off2) showed better correlation to clinical outcome. In this study, we evaluate the association between p53-immunohistochemistry cut-offs, clinico-pathological variables and disease-specific survival (DSS). Methods: Seven-hundred-fifty chemotherapy-naïve patients who underwent radical cystectomy were included (92% muscle-invasive bladder cancer. In addition to cut-off1 and cut-off2, a third cut-off (cut-off3) was determined based on the highest Youden-index value. Cut-off values were associated with clinico-pathological variables and FGFR3 mutation status. The Kaplan-Meier method was used to estimate DSS. Results: Aberrant p53-expression was found in 489 (65%) (cut-off1) and 466 (62%) (cut-off2) tumors. Cut-off3 was determined at 25% and aberrant p53-expression in 410 cases (55%) (cutoff3). p53-expression levels were significantly associated with higher pT-stage (cut-off1/2/3: P = 0.047, P = 0.006 and P = 0.0002, respectively), higher grade (all, P < 0.0001), and FGFR3 wild-type (cut-off1: P = 0.02, cut-offs2&3: P = 0.001). Median follow-up was 5.3 years (interquartile range, 4.0-6.0 years). p53-expression was not associated with DSS for any of the three cut-offs (cut-off1/2/3: P-log-rank = 0.566, 0.77 and 0.50, respectively). If we only considered locally advanced bladder cancer, results on DSS remained non-significant. Conclusion: This multi-center, multi-laboratory study showed that, regardless of the cut-off used, p53-immunohistochemistry did not enable selection of patients with worse outcome. Our results suggest that p53-immunohistochemistry alone is not suitable to guide clinical decision making after radical cystectomy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".