Molecular pathology of bladder cancer
Bibliographic record
Abstract
Significant progress has been achieved in elucidating the molecular underpinnings of bladder cancer initiation and progression. Translational research has identified mutations in chromatin-modifying genes such as KMT2D and KDM6A, which facilitate colonization of larger regions of the urothelium. Subsequent mutations in TP53, PIK3CA, FGFR3 or RB1 drive malignant transformation. Advances in personalized oncology now integrate clinical, pathological and molecular classifications in bladder cancer, representing a paradigm shift in the management of locally advanced and metastatic disease. Alterations in FGFR3, commonly found in the luminal-papillary molecular subtype associated with low response to immunotherapy, are the target of erdafitinib. Enfortumab vedotin, which targets Nectin-4 (expressed in >95% of urothelial carcinomas), is approved for patients who progress after chemotherapy and/or immunotherapy. Evidence suggests that Nectin-4 gene amplification may further refine patient stratification. Sacituzumab govitecan, an antibody-drug conjugate directed against Trop-2, is effective in basal, luminal and stroma-rich subtypes but not in neuroendocrine carcinomas. In addition, therapies developed for HER2-positive breast cancer have shown efficacy in urothelial carcinoma, with recent data from the DESTINY pan-tumour phase II trial leading to FDA approval of trastuzumab deruxtecan for HER2-overexpressing metastatic urothelial carcinoma. This paper is a comprehensive review of the molecular pathology of bladder cancer, highlighting advances in molecular classification, biomarkers and personalized therapies. The transition from morphology-based classifications to combined morphological and molecular approaches, with therapeutic implications, is also addressed.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".