Background: Muscle-invasive bladder cancer (MIBC) is a molecularly diverse disease with heterogeneous clinical outcomes. Several molecular classifications have been proposed, but the diversity of their subtype sets impedes their clinical application. Objective: To achieve an international consensus on MIBC molecular subtypes that reconciles the published classification schemes. Design, setting, and participants: We used 1750 MIBC transcriptomic profiles from 16 published datasets and two additional cohorts. Outcome measurements and statistical analysis: We performed a network-based analysis of six independent MIBC classification systems to identify a consensus set of molecular classes. Association with survival was assessed using multivariable Cox models. Results and limitations: We report the results of an international effort to reach a consensus on MIBC molecular subtypes. We identified a consensus set of six molecular classes: luminal papillary (24%), luminal nonspecified (8%), luminal unstable (15%), stroma-rich (15%), basal/squamous (35%), and neuroendocrine-like (3%). These consensus classes differ regarding underlying oncogenic mechanisms, infiltration by immune and stromal cells, and histological and clinical characteristics, including outcomes. We provide a single-sample classifier that assigns a consensus class label to a tumor sample’s transcriptome. Limitations of the work are retrospective clinical data collection and a lack of complete information regarding patient treatment. Conclusions: This consensus system offers a robust framework that will enable testing and validation of predictive biomarkers in future prospective clinical trials. Patient summary: Bladder cancers are heterogeneous at the molecular level, and scientists have proposed several classifications into sets of molecular classes. While these classifications may be useful to stratify patients for prognosis or response to treatment, a consensus classification would facilitate the clinical use of molecular classes. Conducted by multidisciplinary expert teams in the field, this study proposes such a consensus and provides a tool for applying the consensus classification in the clinical setting.

This cohort study investigates the use of longitudinal data from ultradeep sequencing of cell-free DNA in Danish patients with colorectal cancer.

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.

MicroRNAs (miRNA) are a class of small noncoding RNAs with important posttranscriptional regulatory functions. Recent data suggest that miRNAs are aberrantly expressed in many human cancers and that they may play significant roles in carcinogenesis. Here, we used microarrays to profile the expression of 315 human miRNAs in 10 normal mucosa samples and 49 stage II colon cancers differing with regard to microsatellite status and recurrence of disease. Several miRNAs were differentially expressed between normal tissue and tumor microsatellite subtypes, with miR-145 showing the lowest expression in cancer relative to normal tissue. Microsatellite status for the majority of cancers could be correctly predicted based on miRNA expression profiles. Furthermore, a biomarker based on miRNA expression profiles could predict recurrence of disease with an overall performance accuracy of 81%, indicating a potential role of miRNAs in determining tumor aggressiveness. The expression levels of miR-320 and miR-498, both included in the predictive biomarker, correlated with the probability of recurrence-free survival by multivariate analysis. We successfully verified the expression of selected miRNAs using real-time reverse transcription-PCR assays for mature miRNAs, whereas in situ hybridization was used to detect the accumulation of miR-145 and miR-320 in normal epithelial cells and adenocarcinoma cells. Functional studies showed that miR-145 potently suppressed growth of three different colon carcinoma cell lines. In conclusion, our results suggest that perturbed expression of numerous miRNAs in colon cancer may have a functional effect on tumor cell behavior, and, furthermore, that some miRNAs with prognostic potential could be of clinical importance. [Cancer Res 2008;68(15):6416-24]

Bladder cancer is a common malignant disease characterized by frequent recurrences. The stage of disease at diagnosis and the presence of surrounding carcinoma in situ are important in determining the disease course of an affected individual. Despite considerable effort, no accepted immunohistological or molecular markers have been identified to define clinically relevant subsets of bladder cancer. Here we report the identification of clinically relevant subclasses of bladder carcinoma using expression microarray analysis of 40 well characterized bladder tumors. Hierarchical cluster analysis identified three major stages, Ta, T1 and T2-4, with the Ta tumors further classified into subgroups. We built a 32-gene molecular classifier using a cross-validation approach that was able to classify benign and muscle-invasive tumors with close correlation to pathological staging in an independent test set of 68 tumors. The classifier provided new predictive information on disease progression in Ta tumors compared with conventional staging (P < 0.005). To delineate non-recurring Ta tumors from frequently recurring Ta tumors, we analyzed expression patterns in 31 tumors by applying a supervised learning classification methodology, which classified 75% of the samples correctly (P < 0.006). Furthermore, gene expression profiles characterizing each stage and subtype identified their biological properties, producing new potential targets for therapy.