Artificial Intelligence Analysis of Histological Images Accurately Identifies Luminal Subtype Urothelial Carcinomas Characterized by High Peroxisome Proliferator-Activated Receptor Gamma Expression

Abstract

Background:

Molecular subtyping of urothelial carcinoma (UC) into luminal and nonluminal lineages is clinically relevant for prognosis and emerging targeted therapies. Elevated peroxisome proliferator-activated receptor gamma (PPARG) expression is a defining feature of luminal UC and provides the biological rationale for FX-909, a first-in-class, orally bioavailable, selective PPARG inhibitor currently under clinical evaluation in luminal UC with high PPARG expression. However, RNA sequencing (RNA-seq), the standard approach for molecular subtyping, remains costly and difficult to implement broadly. An artificial intelligence (AI)-based histopathology platform may enable rapid, scalable, and practical identification of luminal tumors for FX-909 patient selection.

Methods:

We developed an AI-based computational pathology model to classify luminal versus nonluminal UC directly from hematoxylin and eosin (H&E)–stained whole-slide images (WSIs). An additive multiple-instance learning (aMIL) architecture with a lightweight ShuffleNet backbone was trained and evaluated across The Cancer Genome Atlas-derived UC specimens and independent validation UC cohorts, including an advanced-stage dataset. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC). Reproducibility was measured across technical replicates and multiple tissue sections per case, and interpretability was evaluated using attention heatmaps highlighting regions contributing to model predictions.

Results:

The model achieved robust discrimination of luminal and nonluminal UC, with AUROC values exceeding 0.95 across validation cohorts and strong generalization to an independent advanced-stage cohort. Predictions were highly reproducible, with 95.2% concordance across technical replicates and 91.3% agreement in the classification of luminal/nonluminal subtypes across multiple tissue sections, indicating that a single representative section is sufficient for subtype assignment in most cases. Model probability scores were largely polarized, with most cases assigned high-confidence luminal or nonluminal predictions and a smaller subset showing intermediate probabilities consistent with mixed or heterogeneous features. Attention heatmaps provided interpretable visualization of histological regions driving predictions, supporting pathologist review and hypothesis generation.

Conclusion:

AI-driven analysis of routine H&E WSIs enables accurate, reproducible, and interpretable classification of UC molecular subtypes without RNA-seq. This scalable approach supports identification of PPARG-driven luminal tumors and provides a foundation for development of a novel companion diagnostic assay to potentially enable precision patient selection for FX-909, a first-in-class PPARG-targeting drug.

Fig. 1.

Overview of the automated attention-based multiple-instance learning (aMIL) pipeline for whole-slide classification. A hematoxylin and eosin (H&E) stained whole-slide image (WSI) is used as input (1). A tissue segmentation model identifies cancer and stromal regions (2). Image patches (tiles) are extracted from regions containing cancer and stroma (3). The selected patches are processed by an aMIL model to generate patch-level contributions toward a luminal versus nonluminal prediction (4). Patch-level scores are aggregated to produce a whole-slide–level class probability (5). Model-derived heatmaps visualize the relative contribution of individual patches to the final slide-level prediction, enabling interpretation of spatially informative regions (6).