Low-Coverage Sequencing of Urine Sediment DNA for Detection of Copy Number Aberrations in Bladder Cancer.

Abstract

PurposeChromosomal copy number aberrations (CNAs) are a hallmark of bladder cancer and a useful target for diagnostic explorations. Here we constructed a low-coverage whole-genome sequencing method for the detection of CNAs in urine sediment DNA from patients with bladder cancer.Patients and MethodsWe conducted a prospective study using urine sediment samples from 65 patients with bladder tumors, including 54 patients with bladder cancer and 11 patients with benign bladder tumors. Forty-three healthy individuals were included as normal controls. DNA was extracted from urine sediments and analyzed by low-coverage whole-genome sequencing to compare differences in CNAs among these three groups. CNAs are defined by arbitrary R values (normal range ± 2). When these values exceed ± 0.2 of normal range, gain/duplication or loss/deletion are suspected.ResultsWith this method, CNAs were detected in 39 of 51 patients with bladder cancer, 2 of 10 patients with benign bladder tumors, and 8 of 39 normal controls. The lengths of DNA deletion and duplication were significantly larger in patients with bladder cancer than in patients with benign tumors or normal controls (P < 0.05). Bladder cancer duplicate CNAs mainly occurred on chromosomes 1q, 5p, 6p, 7p, 8q, and 13q, while deletions mainly occurred on 2q, 8p, 9q, 9p, and 11p. Those regions contained bladder cancer tumor-related genes, such as STK3, COX6C, SPAG1, CDKAL1, C9orf53, CDKN2A, CDKN2B, MIR31, and IFNA1. The number of CNAs detected in urine sediment DNA during the follow-up period was significantly reduced.ConclusionOur sequencing method is highly sensitive and can detect a minimal chromosome repeat/microdeletion change of 0.15 Mb. The use of 0.1~0.3× low-coverage whole-genome sequencing can be used to detect bladder cancer CNAs in urine sediment DNA. This method provides a promising method for noninvasive diagnosis of bladder cancer, but still needs further verification in a larger sample size.