Immunohistochemical assays for bladder cancer molecular subtyping: Optimizing parsimony and performance using Lund taxonomy
Bibliographic record
Abstract
Abstract Transcriptomic and proteomic profiling reliably classifies bladder cancers into luminal and basal molecular subtypes. Based on their prognostic and predictive associations, these subtypes may improve clinical management of bladder cancers. However, the complexity of published subtyping algorithms has limited their translation into practice. Here we optimize and validate compact subtyping algorithms based on the Lund taxonomy. We reanalyzed immunohistochemistry (IHC) expression data of muscle-invasive bladder cancer samples from Lund 2017 (n=193) and 2012 (n=76) cohorts. We characterized and quantified IHC expression patterns, and determined the simplest, most accurate decision tree models to identify subtypes. We tested the utility of a previously published algorithm using routine antibody assays commonly available in surgical pathology laboratories (GATA3, KRT5 and p16) to identify basal/luminal subtypes and to distinguish between luminal subtypes, Urothelial-Like (Uro) and Genomically Unstable (GU). We determined the dominant decision tree classifiers using four-fold cross-validation with separate uniformly distributed train (75%) and validation (25%) sets. Using the three-antibody algorithm resulted in 86-95% accuracy across training and validation sets for identifying basal/luminal subtypes, and 67-86% accuracy for basal/Uro/GU subtypes. Although antibody assays for KRT14 and RB1 are not routinely used in pathology practice, these features achieved the simplest and most accurate models to identify basal/luminal and Uro/GU/basal subtypes, achieving 93-96% and 85-86% accuracies, respectively. When translated to a more complex model using eight antibody assays, accuracy was comparable to simplified models, with 86% (train) and 82% (validation). We conclude that a simple immunohistochemical classifier can accurately identify luminal (Uro, GU) and basal subtypes and pave the way for clinical implementation.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".