Non-muscle-invasive bladder cancer (NMIBC) is a heterogeneous disease with widely different outcomes. We performed a comprehensive transcriptional analysis of 460 early-stage urothelial carcinomas and showed that NMIBC can be subgrouped into three major classes with basal- and luminal-like characteristics and different clinical outcomes. Large differences in biological processes such as the cell cycle, epithelial-mesenchymal transition, and differentiation were observed. Analysis of transcript variants revealed frequent mutations in genes encoding proteins involved in chromatin organization and cytoskeletal functions. Furthermore, mutations in well-known cancer driver genes (e.g., TP53 and ERBB2) were primarily found in high-risk tumors, together with APOBEC-related mutational signatures. The identification of subclasses in NMIBC may offer better prognostication and treatment selection based on subclass assignment.

The molecular landscape in non-muscle-invasive bladder cancer (NMIBC) is characterized by large biological heterogeneity with variable clinical outcomes. Here, we perform an integrative multi-omics analysis of patients diagnosed with NMIBC (n = 834). Transcriptomic analysis identifies four classes (1, 2a, 2b and 3) reflecting tumor biology and disease aggressiveness. Both transcriptome-based subtyping and the level of chromosomal instability provide independent prognostic value beyond established prognostic clinicopathological parameters. High chromosomal instability, p53-pathway disruption and APOBEC-related mutations are significantly associated with transcriptomic class 2a and poor outcome. RNA-derived immune cell infiltration is associated with chromosomally unstable tumors and enriched in class 2b. Spatial proteomics analysis confirms the higher infiltration of class 2b tumors and demonstrates an association between higher immune cell infiltration and lower recurrence rates. Finally, the independent prognostic value of the transcriptomic classes is documented in 1228 validation samples using a single sample classification tool. The classifier provides a framework for biomarker discovery and for optimizing treatment and surveillance in next-generation clinical trials.

No abstract