Tumorigenic pathways in low-stage bladder cancer based on p53, MDM2 and p21 phenotypes
Bibliographic record
Abstract
Our aim was to determine whether the pattern of expression of the interrelated proteins p53, MDM2 and p21 could shed light on the etiopathogenic mechanisms of superficial bladder tumors. Protein expression was detected by immunohistochemistry (IHC) using monoclonal antibodies (MAbs) Pab 1801 for p53, IF2 for MDM2 and EA10 for p21 on 269 newly diagnosed bladder tumors (214 pTa and 55 pT1; 93 grade 1, 145 grade 2 and 31 grade 3). While no p21 immunoreactivity was found in normal urothelium, 85% of tumors were strongly p21-positive. MDM2 was overexpressed in 44% of tumors, almost all being also positive for p21. p53 was overexpressed in 20% of cases: 66% of p53-positive tumors were also MDM2 positive, compared with only 38% of p53-negative tumors. p53 mutations were studied by direct DNA sequencing in a subset of 24 high-grade tumors. Both MDM2 and p21 were less frequently expressed in p53 mutated tumors compared with tumors with a wild-type gene. Distinct phenotypes were correlated with the frequency of poorly differentiated (grade 3) tumors. The most common phenotypes were p21+/MDM2−/p53− and p21+/MDM2+/p53− observed in 38% and 29% of tumors, respectively. Grade 3 tumors were found in 4% and 8% of these 2 groups, in contrast with 30% frequency in p21+/p53+ tumors (p = 0.002) regardless of their MDM2 phenotype. Four of the 5 (80%) tumors that were p53-positive but negative for p21 were grade 3. Our data suggest that several tumorigenic pathways for superficial bladder tumors can be reflected by the expression pattern of these 3 proteins. Int. J. Cancer (Pred. Oncol.) 89:100–104, 2000. © 2000 Wiley-Liss, Inc.
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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 | 0.000 |
| 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".