International Society of Urological Pathology Consensus Conference on Current Issues in Bladder Cancer. Working Group 4
Bibliographic record
Abstract
Molecular subtyping has been a major focus of bladder cancer research over the past decade. Despite many promising associations with clinical outcomes and treatment response, its clinical impact has yet to be defined. As part of the 2022 International Society of Urological Pathology Conference on Bladder Cancer, we reviewed the current state of the science for bladder cancer molecular subtyping. Our review included several different subtyping systems. We derived the following 7 principles, which summarize progress and challenges of molecular subtyping: (1) bladder cancer has 3 major molecular subtypes: luminal, basal-squamous, and neuroendocrine; (2) signatures of the tumor microenvironment differ greatly among bladder cancers, particularly among luminal tumors; (3) luminal bladder cancers are biologically diverse, and much of this diversity results from differences in features unrelated to the tumor microenvironment, such as FGFR3 signaling and RB1 inactivation; (4) molecular subtype of bladder cancer associates with tumor stage and histomorphology; (5) many subtyping systems include idiosyncrasies, such as subtypes recognized by no other system; (6) there are broad fuzzy borders between molecular subtypes, and cases that fall on these fuzzy borders are often classified differently by different subtyping systems; and (7) when there are histomorphologically distinct regions within a single tumor, the molecular subtypes of these regions are often discordant. We reviewed several use cases for molecular subtyping, highlighting their promise as clinical biomarkers. Finally, we conclude that data are currently insufficient to support the routine use of molecular subtyping to guide bladder cancer management, an opinion shared with the majority of conference attendees. We also conclude that molecular subtype should not be considered an "intrinsic" property of a tumor but should instead be considered the result of a specific laboratory test, performed using a specific testing platform and classification algorithm, validated for a specific clinical application.
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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 | 0.039 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 0.011 |
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".