Genomic characterization of non-schistosomiasis-related squamous cell carcinoma of the urinary bladder: A retrospective exploratory study
Bibliographic record
Abstract
BACKGROUND: Non-schistosomiasis related-squamous cell carcinoma of urinary bladder (NSR-SCCUB) is a rare tumor subtype distinct from urothelial carcinoma (UC). Studies assessing molecular biomarkers in bladder cancer have generally focused on UC, and genomic data of NSR-SCCUB is limited. We aim to provide additional insight into the molecular underpinnings of this rare entity. METHODS: NSR-SCCUB patients were identified retrospectively at Princess Margaret Cancer Centre between 2002 and 2017. Demographics, disease characteristics, therapeutic approaches, and outcomes were collected. Tissue samples were interrogated using the Oncomine Comprehensive Assay v3 (ThermoFisher). Kaplan-Meier method was used to estimate the disease-free survival and overall survival (OS). RESULTS: Overall, 11 patients with NSR-SCCUB were identified between 2002 and 2017 with adequate tissue samples. Median age was 71 years (45-86), predominantly male (63.6%). At time of diagnosis, 9 patients (81.8%) had muscle-invasive disease, 1 (9.1%) had non-muscle invasive, and 1 (9.1%) had advanced disease. Nine (81.8%) patients had radical cystectomy and pelvic lymph nodes dissection. Eight (72.7%) patients had pT3 or pT4 with N0, and 5 (45.5%) were grade 3. Median OS was 12.5 months (95% CI 7.7-17.2 months). Single nucleotide variants or insertion/deletions were identified in TP53, TERT, PIK3CA, PTEN, CREBBP, FBXW7, and FGFR3. Amplifications were found in CCND1, and EGFR. CONCLUSIONS: NSR-SCCUB has potentially actionable genomic alterations with anticancer agents and many of these aberrations are also seen in UC. The recruitment of NSR-SCCUB patients harboring such mutations should be considered in biomarker driven urinary bladder cancer studies.
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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 | 0.001 |
| 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 | 0.001 |
| 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".