BACKGROUND

The noninvasive identification of bladder tumors may improve disease control and prevent disease progression. Aberrant promoter methylation (i.e., hypermethylation) is a major mechanism for silencing tumor suppressor genes and other cancer-associated genes in many human cancers, including bladder cancer.

METHODS

A quantitative fluorogenic real-time polymerase chain reaction (PCR) assay was used to examine primary tumor DNA and urine sediment DNA from 15 patients with bladder cancer and 25 control subjects for promoter hypermethylation of nine genes (APC, ARF, CDH1, GSTP1, MGMT, CDKN2A, RARbeta2, RASSF1A, and TIMP3) to identify potential biomarkers for bladder cancer. We then used these markers to examine urine sediment DNA samples from an additional 160 patients with bladder cancers of various stages and grades and from an additional 69 age-matched control subjects. Data were analyzed on the basis of a prediction model and were internally validated using a jacknife procedure. All statistical tests were two-sided.

RESULTS

For all 15 patients with paired DNA samples, the promoter methylation pattern in urine matched that in the primary tumors. Four genes displayed 100% specificity. Of the 175 bladder cancer patients, 121 (69%, 95% confidence interval [CI] = 62% to 76%) displayed promoter methylation in at least one of these genes (CDKN2A, ARF, MGMT, and GSTP1), whereas all control subjects were negative for such methylation (100% specificity, 95% CI = 96% to 100%). A logistic prediction model using the methylation levels of all remaining five genes was developed and internally validated for subjects who were negative on the four-gene panel. This combined, two-stage predictor produced an internally validated ROC curve with an overall sensitivity of 82% (95% CI = 75 % to 87%) and specificity of 96% (95% CI = 90% to 99%).

CONCLUSION

Testing a small panel of genes with the quantitative methylation-specific PCR assay in urine sediment DNA is a powerful noninvasive approach for the detection of bladder cancer. Larger independent confirmatory cohorts with longitudinal follow-up will be required in future studies to define the impact of this technology on early detection, prognosis, and disease monitoring before clinical application.