Detection of the ADGRG6 hotspot mutations in urine for bladder cancer early screening by ARMS-qPCR.

Abstract

In bladder cancer, recurrent ADGRG6 enhancer hotspot mutations (chr. 6: 142,706,206 G>A, chr. 6:142,706,209 C>T) were reported at a high mutation rate of approximately 50%. Thus, ADGRG6 enhancer mutation status might be a candidate for diagnostic biomarker. To improve test efficacy, an amplification refractory mutation system combined with quantitative real-time PCR (ARMS-qPCR) assay was developed to detect the ADGRG6 mutations in a patient as a clinical diagnostic test. To validate the performance of the ARMS-qPCR assay, artificial plasmids, cell DNA reference standard were used as templates, respectively. To test the clinical diagnostic ability, we detected the cell free DNA (cfDNA) and sediment DNA (sDNA) of 30 bladder cancer patients' urine by ARMS-qPCR comparing with Sanger sequencing, followed by the droplet digital PCR to confirm the results. We also tested the urine of 100 healthy individuals and 90 patients whose diagnoses urinary tract infections or urinary stones but not bladder cancer. Sensitivity of 100% and specificity of 96.7% were achieved when the mutation rate of the artificial plasmid was 1%, and sensitivity of 96.7% and specificity of 100% were achieved when the mutation frequency of the reference standard was 0.5%. Sanger sequencing and ARMS-qPCR both detected 30 cases of bladder cancer with 93.3% agreement. For the remaining unmatched sites, ARMS-qPCR results were consistent with droplet digital PCR. Among 100 healthy individuals, three of them carried hotspot mutations by way of ARMS-qPCR. Of 90 patients with urinary tract infections or urinary stones, no mutations were found by ARMS-qPCR. Based on clinical detection, the ARMS-qPCR assay's sensitivity is 83.3%, specificity is 98.4%. We here present a novel urine test for ADGRG6 hotspot mutations with high accuracy and sensitivity, which may potentially serve as a rapid and non-invasive tool for bladder cancer early screening and follow-up relapse monitoring.