A Urine-Based Liquid Biopsy Method for Detection of Upper Tract Urinary Carcinoma

Yansheng Xu, Xin Ma, Xing Ai, Jiangping Gao, Yiming Liang, Qin Zhang, Tonghui Ma, Kaisheng Mao, Qiaosong Zheng, Sizhen Wang, Yuchen Jiao, Xu Zhang, Hongzhao Li, Yansheng Xu, Xin Ma, Xing Ai, Jiangping Gao, Yiming Liang, Qin Zhang, Tonghui Ma, Kaisheng Mao, Qiaosong Zheng, Sizhen Wang, Yuchen Jiao, Xu Zhang, Hongzhao Li

Abstract

Background: Conventional clinical detection methods such as CT, urine cytology, and ureteroscopy display low sensitivity and/or are invasive in the diagnosis of upper tract urinary carcinoma (UTUC), a factor precluding their use. Previous studies on urine biopsy have not shown satisfactory sensitivity and specificity in the application of both gene mutation or gene methylation panels. Therefore, these unfavorable factors call for an urgent need for a sensitive and non-invasive method for the diagnosis of UTUC.

Methods: In this study, a total of 161 hematuria patients were enrolled with (n = 69) or without (n = 92) UTUC. High-throughput sequencing of 17 genes and methylation analysis for ONECUT2 CpG sites were combined as a liquid biopsy test panel. Further, a logistic regression prediction model that contained several significant features was used to evaluate the risk of UTUC in these patients.

Results: In total, 86 UTUC- and 64 UTUC+ case samples were enrolled for the analysis. A logistic regression analysis of significant features including age, the mutation status of TERT promoter, and ONECUT2 methylation level resulted in an optimal model with a sensitivity of 94.0%, a specificity of 93.1%, the positive predictive value of 92.2% and a negative predictive value of 94.7%. Notably, the area under the curve (AUC) was 0.957 in the training dataset while internal validation produced an AUC of 0.962. It is worth noting that during follow-up, a patient diagnosed with ureteral inflammation at the time of diagnosis exhibiting both positive mutation and methylation test results was diagnosed with ureteral carcinoma 17 months after his enrollment.

Conclusion: This work utilized the epigenetic biomarker ONECUT2 for the first time in the detection of UTUC and discovered its superior performance. To improve its sensitivity, we combined the biomarker with high-throughput sequencing of 17 genes test. It was found that the selected logistic regression model diagnosed with ureteral cancer can evaluate upper tract urinary carcinoma risk of patients with hematuria and outperform other existing panels in providing clinical recommendations for the diagnosis of UTUC. Moreover, its high negative predictive value is conducive to rule to exclude patients without UTUC.

Keywords: hematuria; liquid biopsy; logistic regression model; methylation; next-generation sequencing; upper tract urinary carcinoma.

Conflict of interest statement

YL, QZ, TM, KM, QZ, and SW were employed by the company Genetron Health (Beijing) Technology, Co. Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Xu, Ma, Ai, Gao, Liang, Zhang, Ma, Mao, Zheng, Wang, Jiao, Zhang and Li.

Figures

Figure 1
Figure 1
Sample and data processing work-flow.
Figure 2
Figure 2
Variants detected in 12 paired urine and tissue samples: (A) A heatmap shows variants detected in 12 paired urine and tissue samples. (B) A Venn diagram shows the relationship of these 26 variants from each set of different types of samples.
Figure 3
Figure 3
Heatmap of all variations in urine samples: it demonstrates the variants detected in each case’s urine sample.
Figure 4
Figure 4
Comparison of mutations across different groups profiled in this study. (Pairwise comparison results from Fisher’s exact test). (A) Comparison of mutations across high vs. low grade UTUC. (B) Comparison of mutations across muscle-invasive vs. non-muscle-invasive UTUC.
Figure 5
Figure 5
Performance analysis of ONECUT2 methylation: (A) The ONECUT2 methylation Δct-value distribution of different types of samples. (B) ROC curve of the ONECUT2 methylation (AUC = 0.92) also indicating the optimized Δct value cutoff is at 7.93 in this study.
Figure 6
Figure 6
ROC of the multivariable logistic regression models with different features in the training set.

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