Simplified Molecular Classification of Urothelial Carcinoma via Multi-Omic Profiling: Implications for Precision Therapy

Urothelial carcinoma (UC) is a biologically heterogeneous disease and current molecular classification frameworks have limited integration into clinical decision-making. To foster precision oncology in UC, we developed a molecular classification framework of UC applicable to transcriptomic and proteomic data of non-muscle invasive bladder cancers (NMIBC), muscle-invasive bladder cancer (MIBC), and urothelial cancer cell lines. Using a whole-transcriptome self-organized map and regularized semi-supervised clustering ofn 4439 bulk NMIBC and MIBC transcriptomes and proteomes, and 33 UC cell lines, we identified three molecular UC clusters. The clusters had distinct signatures of mRNA, proteins, biological processes, metabolism, and essential driver genes. They also differed in prognosis and machine learning-predicted treatment vulnerabilities. High-risk stroma-rich cluster #1 cancers were predicted to respond to selected cytotoxic drugs, ferroptosis inducers, and PARP inhibitors. For the aggressive, fast proliferating and immune-infiltrated cluster #2 tumors with basal/squamous differentiation, cytotoxic agents and EGFR/ERBB- and MEK/ERK-targeting therapy was proposed. Cluster #3 cancers of predominantly luminal papillary phenotype, with scarce stroma and immune infiltration were enriched with NMIBC and low-risk malignancies. For cluster #3 patients, selected epigenetic drugs or EGFR/FGFR inhibitors may pose an attractive treatment option. This molecular taxonomy holds promise as a practical framework for patient stratification and therapeutic guidance in UC. Our molecular classification scheme may facilitate personalized transcriptome- and proteome-based risk assessment and therapy guidance, and the development of new cancer medication.