The <i>FGFR3</i> Mutation is Related to Favorable pT1 Bladder Cancer
Bibliographic record
Abstract
PURPOSE: Stage pT1 bladder cancer comprises a heterogeneous group of tumors for which different management options are advocated. FGFR3 mutations are linked to favorable (low grade/stage) pTa bladder cancer while altered P53 is common in cases of high grade, muscle invasive (pT2 or greater) bladder cancer. We determined the frequency of FGFR3 mutations and P53 alterations in patients with pT1 bladder cancer and correlated these data to histopathological variables and clinical outcomes. MATERIALS AND METHODS: We included 132 patients with primary pT1 bladder cancer from a total of 2 academic centers. A uropathologist reviewed the slides for grade and confirmed the pT1 diagnosis. FGFR3 mutation status was examined by SNaPshot® analysis and P53 expression was determined by standard immunohistochemistry. Kaplan-Meier and multivariate analyses were used to assess progression. RESULTS: FGFR3 mutations were detected in 37 of 132 pT1 bladder cancer cases (28%) and altered P53 was seen in 71 (54%). Only 8% of patients had the 2 molecular alterations (p = 0.001). FGFR3 mutation correlated with lower grade and altered P53 correlated with high grade pT1 bladder cancer. Median followup was 6.5 years. FGFR3 mutation status and carcinoma in situ were significant for predicting progression on univariate and multivariate analyses but P53 status was not. CONCLUSIONS: FGFR3 mutations selectively identify patients with pT1 bladder cancer who have favorable disease characteristics. Further study may confirm that FGFR3 identifies those who would benefit from a conservative approach to the disease.
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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 | 0.002 |
| 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) | 0.003 | 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".