Bladder cancer is the fourth-most common cancer in men, one of the most expensive types of cancer to treat, and recurs frequently. While the genomic pathways associated with high-grade, muscle invasive (MI) tumors and low-grade, non-muscle invasive (NMI) tumors are well-established, recent studies have also found that MI and NMI tumors have unique methylation patterns, and that methylation may play a role in the development of early-stage bladder cancer. Interestingly, bladder cancer patients have frequent mutations in chromatin remodeling genes that alter known histone markers of DNA methylation. Since methylation marks are pharmacologically reversible, the identification of methylation biomarkers is a promising avenue for therapeutic treatment. Indeed, several candidate prognostic methylation biomarkers have been identified, but await prospective evaluation.

The Bladder Cancer Biomarker Evaluation Tool (BC-BET) is a web-based resource for rapidly evaluating biomarkers in publicly available genomic datasets. Initially developed for gene expression biomarkers, BC-BET includes 1451 patients across 13 cohorts, and 40 bladder cancer cell lines, with gene expression profiles. In this work, we describe the addition of methylation data to BC-BET, so that candidate methylation biomarkers can be rapidly evaluated across multiple patient cohorts.

BC-BET now contains methylation profiles from four patient cohorts (250 patients) and includes the available bladder cancer data from the Gene Expression Omnibus and The Cancer Genome Atlas. In "evaluation" mode, a user selects a gene of interest, and BC-BET evaluates whether the selected gene is differentially methylated between tumor vs. normal samples, NMI vs. MI tumors, and low- vs. high-grade tumors. Alternatively, the “biomarker discovery” mode allows a user to identify all differentially methylated genes at a desired false discovery rate (FDR). In both cases, graphical summaries of the results are displayed and statistical results can be downloaded to an Excel spreadsheet. The methylation module of BC-BET is implemented using shiny, a web application framework for R for developing interactive web pages.

The addition of methylation data to BC-BET makes it a valuable resource for evaluating methylation biomarkers in bladder cancer. BC-BET is available from the following link: https://gdancik.github.io/bioinformatics/BCBET

Citation Format: Meaghan M. Kennedy, Garrett M. Dancik. A database for evaluating methylation biomarkers in bladder cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2610. doi:10.1158/1538-7445.AM2017-2610