Performance of IHC3-Uro Immunohistochemistry-based Molecular Subtyping in Muscle-Invasive Bladder Cancer
Bibliographic record
Abstract
Molecular subtyping in muscle-invasive bladder cancer (MIBC) has shown important relationships to both prognosis and chemosensitivity. However, insufficient validation and complex testing methods have prevented molecular subtyping from being clinically implemented. To address these shortcomings, 13 immunohistochemistry (IHC) assays were previously validated to identify Three key intrinsic subtypes of MIBC. We distilled this work into a three-antibody algorithm termed IHC3-Uro which uses GATA3, p16 and KRT5 to identify key IHC-based subtypes in MIBC. If effective in determining subtype, this simple IHC-based test could serve as a useful tool in pathologic practice to guide clinical decision making. We applied IHC3-Uro to three independent MIBC cohorts in order to identify molecular subtypes and explore their relationships to clinicopathologic variables and outcomes. In this work, 89% of samples were assigned to Uro, GU and Basal intrinsic molecular subtypes. This subtyping appeared to be the most effective in TURBT samples, where GATA3 staining was most consistent. These subtypes showed prognostic associations, where the GU subtype and a subset of Uro tumours termed Uro-KRT5 had increased risk of death compared to the Uro subtype. Altogether, this work suggests that a simple IHC-based test may serve as an effective alternative for identifying prognostically relevant subtypes previously discovered using complex transcriptomic methods. Additional validation and investigation of these IHC-based subtypes may provide opportunities for clinical use and optimizing patient stratification according to risk of death.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".