Journal
JOURNAL OF UROLOGY
Volume 197, Issue 3, Pages 590-595Publisher
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1016/j.juro.2016.09.118
Keywords
urinary bladder neoplasms; hematuria; biomarkers; tumor; urinalysis; methylation
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Funding
- European FP7 project - Grant FP7 [201663]
- Collaboration Agreement with MDxHealth (Project Agreement IV) with work at Spanish National Cancer Research Centre - Instituto de Salud Carlos III, Spain Red Tematica de Investigacion Cooperativa en Cancer, Spain [RD12/0036/0034, RD12/0036/0050]
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Purpose: Only 3% to 28% of patients referred to the urology clinic for hematuria are diagnosed with bladder cancer. Cystoscopy leads to high diagnostic costs and a high patient burden. Therefore, to improve the selection of patients for cystoscopy and reduce costs and over testing we aimed to validate a recently developed diagnostic urine assay. Materials and Methods: Included in study were 200 patients from a total of 3 European countries who underwent cystoscopy for hematuria, including 97 with bladder cancer and 103 with nonmalignant findings. Voided urine samples were collected prior to cystoscopy. DNA was extracted and analyzed for mutations in FGFR3, TERT and HRAS, and methylation of OTX1, ONECUT2 and TWIST1. Logistic regression was used to analyze the association between predictor variables and bladder cancer. Results: Combining the methylation and mutation markers with age led to an AUC of 0.96 (95% CI 0.92e0.99) with 93% sensitivity and 86% specificity, and an optimism corrected AUC of 0.95. The AUC was higher for T1 or greater tumors compared to Ta tumors (0.99 vs 0.93). The AUC was also higher for high grade tumors compared to low grade tumors (1.00 vs 0.93). Overall negative predictive value was 99% based on the 5% to 10% prevalence of bladder cancer in patients with hematuria. This would lead to a 77% reduction in diagnostic cystoscopy. Conclusions: Analyzing hematuria patients for the risk of bladder cancer using novel molecular markers may lead to a reduction in diagnostic cystoscopy. Combining methylation analysis (OTX1, ONECUT2 and TWIST1) with mutation analysis (FGFR3, TERT and HRAS) and patient age resulted in a validated accurate prediction model.
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