Latent class analysis of bladder urothelial carcinoma to reveal sub-classes defined by alterations to chromatin and signal transduction networks.

Abstract

469 Background: 70,000 new cases of bladder urothelial carcinoma are diagnosed in the United States each year. Previous studies of this disease suggest two divergent pathways of carcinogenesis, with ~70% of cases being driven by alterations in signal transduction pathways (FGFR3, HRAS) and the remainder featuring alterations in cell cycle genes (TP53, RB1). Other studies focus on the high prevalence of alterations in chromatin remodeling genes (ARID1A, CREBBP, EP300) in this tumor type. The relationship between chromatin modifier alterations and the two bladder TCC sub-classes is not yet understood. Methods: Comprehensive genomic profiling (CGP) on 638 clinically confirmed cases of advanced bladder urothelial carcinoma was performed using the FoundationOne platform. The coding exons of 287 cancer-related and 47 introns of 19 genes frequently rearranged in cancer were sequenced and analyzed for base substitutions, insertions, deletions, copy number alterations and select gene fusions. The resultant alteration profiles were analyzed for their information content with an entropy-based approach and the profiles were subjected to hierarchical latent class analysis (LCA). Results: CGP of 488 male and 150 female UCs of median age 66 (min: 29, max: 87) resulted in 3241 detected alterations across 302 unique genes, for a mean of ~5.1 known or likely somatic alterations per sample. The most commonly altered genes were TP53, CDKN2A/B, ARID1A, KDM6A, and MLL2. Information was concentrated in the 33 most frequently altered genes. Multiple high level clusters were defined by combinatorial patterns of alterations in TP53, CDKN2A/B, RB1, and FGFR3. Sub-classes were defined by mutually exclusive alterations in the chromatin modifiers KDM6A, MLL2, and ARID1A. Conclusions: We present a global picture of UC sub-classes that encompasses previously identified local relationships including the mutual exclusivities between FGFR3 and TP53, RB1 and CDKN2A/B, and KDM6A and MLL2. In the context of clinically relevant genomic alterations that auger response to targeted therapies, identification of sub-classes of UC may further delineate response to therapy.