In the present investigation, we sought to refine the classification of urothelial carcinoma by combining information on gene expression, genomic, and gene mutation levels. For these purposes, we performed gene expression analysis of 144 carcinomas, and whole genome array-CGH analysis and mutation analyses of FGFR3, PIK3CA, KRAS, HRAS, NRAS, TP53, CDKN2A, and TSC1 in 103 of these cases. Hierarchical cluster analysis identified two intrinsic molecular subtypes, MS1 and MS2, which were validated and defined by the same set of genes in three independent bladder cancer data sets. The two subtypes differed with respect to gene expression and mutation profiles, as well as with the level of genomic instability. The data show that genomic instability was the most distinguishing genomic feature of MS2 tumors, and that this trait was not dependent on TP53/MDM2 alterations. By combining molecular and pathologic data, it was possible to distinguish two molecular subtypes of T a and T 1 tumors, respectively. In addition, we define gene signatures validated in two independent data sets that classify urothelial carcinoma into low-grade (G 1 /G 2 ) and high-grade (G 3 ) tumors as well as non-muscle and muscle-invasive tumors with high precisions and sensitivities, suggesting molecular grading as a relevant complement to standard pathologic grading. We also present a gene expression signature with independent prognostic effect on metastasis and disease-specific survival. We conclude that the combination of molecular and histopathologic classification systems might provide a strong improvement for bladder cancer classification and produce new insights into the development of this tumor type.

We analyzed 34 cases of urothelial carcinomas by miRNA, mRNA and genomic profiling. Unsupervised hierarchical clustering using expression information for 300 miRNAs produced 3 major clusters of tumors corresponding to Ta, T1 and T2-T3 tumors, respectively. A subsequent SAM analysis identified 51 miRNAs that discriminated the 3 pathological subtypes. A score based on the expression levels of the 51 miRNAs, identified muscle invasive tumors with high precision and sensitivity. MiRNAs showing high expression in muscle invasive tumors included miR-222 and miR125b and in Ta tumors miR-10a. A miRNA signature for FGFR3 mutated cases was also identified with miR-7 as an important member. MiR-31, located in 9p21, was found to be homozygously deleted in 3 cases and miR-452 and miR-452* were shown to be over expressed in node positive tumors. In addition, these latter miRNAs were shown to be excellent prognostic markers for death by disease as outcome. The presented data shows that pathological subtypes of urothelial carcinoma show distinct miRNA gene expression signatures.

Non-negative matrix factorization (NMF) is a relatively new approach to analyze gene expression data that models data by additive combinations of non-negative basis vectors (metagenes). The non-negativity constraint makes sense biologically as genes may either be expressed or not, but never show negative expression. We applied NMF to five different microarray data sets. We estimated the appropriate number metagens by comparing the residual error of NMF reconstruction of data to that of NMF reconstruction of permutated data, thus finding when a given solution contained more information than noise. This analysis also revealed that NMF could not factorize one of the data sets in a meaningful way. We used GO categories and pre defined gene sets to evaluate the biological significance of the obtained metagenes. By analyses of metagenes specific for the same GO-categories we could show that individual metagenes activated different aspects of the same biological processes. Several of the obtained metagenes correlated with tumor subtypes and tumors with characteristic chromosomal translocations, indicating that metagenes may correspond to specific disease entities. Hence, NMF extracts biological relevant structures of microarray expression data and may thus contribute to a deeper understanding of tumor behavior.