Large, Nested Variant of Urothelial Carcinoma Is Enriched with Activating Mutations in Fibroblast Growth Factor Receptor-3 among Other Targetable Mutations
Bibliographic record
Abstract
The large, nested variant of urothelial carcinoma (LNVUC) is characterized by bland histomorphology mimicking that of benign von Brunn nests. In the current study, we aimed to investigate the Fibroblast Growth Factor Receptor-3 (FGFR-3) activation and missense mutation in 38 cases, including 6 cases diagnosed with LNVUC and 32 with metastatic invasive urothelial carcinoma (UC). Initially, six formalin-fixed paraffin-embedded (FFPE) tissue samples of the LNVUC were subjected to whole-exome sequencing (WES), and then we performed targeted sequencing on 32 cases of metastatic invasive UC of various morphological subtypes, which were interrogated for the FGFR3. Our results revealed 3/6 (50%) LNVUC cases evaluated by WES in our study showed an activating mutation in FGFR-3, 33% showed an activating mutation in PIK3CA, and 17% showed activating mutation in GNAS or MRE11. Additionally, 33% of cases showed a truncating mutation in CDKN1B. All LNVUC in our study that harbored the FGFR-3 mutation showed additional activating or truncating mutations in other genes. Overall, 6/32 (18.75%) cases of random metastatic invasive UC showed missense mutations of the FGFR-3 gene. The LNVUC variant showed the higher incidence of FGFR-3 mutations compared to other types of mutations. Additionally, all LNVUC cases show additional activating or truncating mutations in other genes, thus being amenable to novel targeted therapy.
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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 | 0.001 | 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".