Advances, recognition, and interpretation of molecular heterogeneity among conventional and subtype histology of urothelial carcinoma (UC): a survey among urologic pathologists and comprehensive review of the literature
Bibliographic record
Abstract
AIMS: Urothelial carcinoma (UC) demonstrates significant molecular and histologic heterogeneity. The WHO 2022 classification has hinted at adding molecular signatures to the morphologic diagnosis. As morphology and associated molecular repertoire may potentially translate to choices of and response to therapy and relapse rate, broader acceptability of recognizing these key features among uropathologists is needed. This prompted an international survey to ascertain the practice patterns in classical/subtype UC among uropathologists across the globe. METHODS AND RESULTS: A survey instrument was shared among 98 uropathologists using SurveyMonkey software. Anonymized respondent data were analysed. The response rate was 85%. A majority were in concordance with the profiles of luminal (93%) and basal (82%) types. Opinion on the FGFR3 testing platform was variable. While 95% concurred that TERT promoter mutation is the key driver in UC, 72% had the opinion that APOBEC mutagenesis is the main signature in muscle invasive bladder cancer (MIBC). Uropathologists have divergent opinions on MIBC and ERCC2 mutations. Among the participants, 94% would quantify aggressive micropapillary and sarcomatoid histology, while 88% would reevaluate another transurethral resection of the bladder tumour specimen in nonmuscle invasive tumour with micropapillary, small cell, or sarcomatoid histology. A leading number agreed to specific molecular signatures of micropapillary (93%), plasmacytoid (97%), and small cell (86%) subtypes. Ninety-six percent of participants agreed that a small-cell component portends a more aggressive course and should be treated with neoadjuvant chemotherapy and 63% would perform HER2/neu testing only on oncologist's request in advanced tumours. Ninety percent agreed that microsatellite instability testing, although not a standard protocol, should be considered in young patients with upper tract UC. Eighty-six percent agreed that UC with high tumour mutational burden would be a better candidate for immunotherapy. CONCLUSION: In the era of precision medicine, enhanced understanding of molecular heterogeneity of UC will contribute to better therapeutic options, novel biomarker discovery, innovative management protocols, and outcomes. Our survey provides a broad perspective of pathologists' perceptions and experience regarding incorporation of histomolecular approaches to "personalize" therapy. Due to variable clinical adoption, there is a need for additional data using uniform study criteria. This will drive generation of best practice guidelines in this area for widespread and consistent clinical utility.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".