Gene Methylation and Urine Cytology to Monitor Bladder Cancer - Expert Commentary

Currently, intermediate or high-risk non-muscle-invasive bladder cancer patients (NMIBC) require intensive follow-up. This usually consists of urethrocystoscopy (gold standard) and urine cytology to monitor the recurrence of NMIBC. The current methods are expensive and invasive and have low sensitivity. 

To address this area of clinical need, a recent article published by van der Heijden et al. in the journal Clinical Epigenetics introduced a non-invasive, sensitive method to predict bladder cancer recurrence using the combination of a three-gene (CFTR, SALL3, and TWIST1) methylation classifier and urine cytology. The researchers collected freshly voided urine samples from bladder cancer (BC) patients, controls, and patients in follow-up for bladder cancer (PFBC).  The authors selected a total of seven DNA methylation markers that are known to play a role in bladder cancer (CDH13, CFTR, NID2, SALL3, TMEFF2, TWIST1, and VIM2) for PCR. A logistic regression model showed that the combination of CFTR, SALL3, and TWIST1 hypermethylation classifier has the highest accuracy of detecting bladder cancer from urine samples.

The investigators conducted a comparison between the three-gene methylation classifier and urine cytology showing that the three-gene methylation classifier has higher sensitivity, higher negative predictive value compared to urine cytology only.  In the training set, the three-gene methylation classifier achieved an AUC 0.874 and an AUC 0.741 was achieved in the testing set. The combination of the three-gene methylation classifier with urine cytology in the validation set showed significantly improved the performance with an AUC of 0.86 with a sensitivity of 96%, a specificity of 40%, and a positive and negative predictive value of 56 and 92%, respectively.

This study defines a urine-based test for surveillance of NMIBC. Non-invasive liquid biopsy approaches are expected to play a bigger role in the future.

Written by Bishoy M. Faltas, MD, Weill Cornell Medicine, New York, NY

Read the Abstract: Urine cell-based DNA methylation classifier for monitoring bladder cancer
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