Ability of a urine gene expression classifier to reduce the number of follow-up cystoscopies in bladder cancer patients

Abstract

This study aimed to improve our previous urine gene expression classifiers focusing on the detection of non-high-risk non-muscle-invasive bladder cancer (NMIBC), and develop a new classifier able to decrease the frequency of cystoscopies during bladder cancer (BC) patients' surveillance. A total of 597 urines from BC patients, controls and patients in follow-up for BC (PFBC) were included. The study has 3 phases. In the urinary biomarker discovery phase, 84 urines from BC and control patients were retrospectively included and analyzed by Ribonucleic Acid (RNA) sequencing. In the classifier development phase, a total of 132 selected genes from previous phase were evaluated by nCounter in 214 prospectively collected urines from PFBC (98 with tumor). A diagnostic classifier was generated by logistic regression. Finally, in the classifier validation phase, a multicentric and international cohort of 248 urines (134 BC and 114 nonrecurrent PFBC) was used to validate classifier performance. A total of 521 genes were found differentially expressed between non-high-risk NMIBC samples and all other groups (P < 0.05). An 8-gene diagnostic classifier with an area under curve (AUC) of 0.893 was developed. Validation of this classifier in a cohort of PFBC achieved an overall sensitivity (SN) and a negative predictive value (NPV) of 96% and 97%, respectively (AUC = 0.823). Notably, this accuracy was maintained in non-high-risk NMIBC group (SN = 94%; NPV = 98%). In conclusion, this 8-gene expression classifier has high SN and NPV in a real clinical scenario. The use of this classifier can reduce the number of follow-up cystoscopies in PFBC, although assessing its final place in clinical setting is necessary.