Abstract 1970P
Background
Compared with bladder cancer, there are few studies on the diagnosis of upper tract urinary carcinoma (UTUC) using urine biopsy. Several methods based on detecting the genetic and epigenetic changes were limited in clinical practice as the poor sensitivity and specificity. An optimized detection method to distinguish UTUC from benign diseases via urine is in urgent need. In the present work, we established a logistic regression model, which combing clinical factors and genomic alterations, to improve the performance to predict the risk of UTUC.
Methods
We retrospectively assessed 159 patients with hematuria in which 70 were pathologically confirmed with UTUC and other 89 with benign urinary diseases. All their urine samples were collected before surgery. We combined high-throughput sequencing of 17 genes and methylation analysis for ONECUT2 CpG sites as a liquid biopsy test panel.
Results
Univariable analysis was performed on each of these variants to assess the efficiency of each feature on evaluating UTUC risk by calculating the odds ratios. Mutated or methylated Gene including FGFR3, TERT, TP53, ONECUT2, and age older than 50 showed a significant impact on UTUC risks(P-value<0.01). And the panel integrated all the genomic markers (≥ 1 of 17 genes mutated or ONECUT2 CpG methylated) showed its superiority (odd ratio=84.27). Based on these results of the univariable analysis, multivariate logistic regression models constructed with different feature combinations were established. The optimized model which contained Age and the panel results showed the highest AUC of 0.936 compared with that only contained age and ONECUT2 methylation results (AUC=0.902) or another contained age and mutation of 17 genes (AUC=0.892). The cutoff of this optimized model was 0.4 according to the highest Youden Index with which we obtained a sensitivity of 87.5% and specificity of 89.6% in the diagnosis of UTUC.
Conclusions
Analyzing hematuria patients for their risk of UTUC may prevent them from overtreatment and lead to a reduction of endoscopies. Combining the methylation test of ONECUT2 gene with several pivotal gene mutation analysis and age resulted in an accurate prediction model which need to be further verified in a larger validation set.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
Xu Yansheng.
Funding
Has not received any funding.
Disclosure
All authors have declared no conflicts of interest.