Immunohistochemical assays for bladder cancer molecular subtyping: Optimizing parsimony and performance using Lund taxonomy

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. ### Competing Interest Statement The authors have declared no competing interest.