Diagnostic performance of Oncuria™, a urinalysis test for bladder cancer.

Diagnostic performance of Oncuria™, a urinalysis test for bladder cancer.

Due to insufficient accuracy, urine-based assays currently have a limited role in the management of patients with bladder cancer. The identification of multiplex molecular signatures associated with disease has the potential to address this deficiency and to assist with accurate, non-invasive diagnosis and monitoring. To evaluate the performance of Oncuria™, a multiplex immunoassay for bladder detection in voided urine samples. The test was evaluated in a multi-institutional cohort of 362 prospectively collected subjects presenting for bladder cancer evaluation. The parallel measurement of 10 biomarkers (A1AT, APOE, ANG, CA9, IL8, MMP9, MMP10, PAI1, SDC1 and VEGFA) was performed in an independent clinical laboratory. The ability of the test to identify patients harboring bladder cancer was assessed. Bladder cancer status was confirmed by cystoscopy and tissue biopsy. The association of biomarkers and demographic factors was evaluated using linear discriminant analysis (LDA) and predictive models were derived using supervised learning and cross-validation analyses. Diagnostic performance was assessed using ROC curves. The combination of the 10 biomarkers provided an AUROC 0.93 [95% CI 0.87–0.98], outperforming any single biomarker. The addition of demographic data (age, sex, and race) into a hybrid signature improved the diagnostic performance AUROC 0.95 [95% CI 0.90–1.00]. The hybrid signature achieved an overall sensitivity of 0.93, specificity of 0.93, PPV of 0.65 and NPV of 0.99 for bladder cancer classification. Sensitivity values of the diagnostic panel for high-grade bladder cancer, low-grade bladder cancer, MIBC and NMIBC were 0.94, 0.89, 0.97 and 0.93, respectively. Urinary levels of a biomarker panel enabled the accurate discrimination of bladder cancer patients and controls. The multiplex Oncuria™ test can achieve the efficient and accurate detection and monitoring of bladder cancer in a non-invasive patient setting. The online version contains supplementary material available at 10.1186/s12967-021-02796-4.

由于准确性不足，基于尿液的检测方法目前在膀胱癌患者的管理中作用有限。识别与疾病相关的多重分子标志物有可能解决这一缺陷，并有助于进行准确的无创诊断和监测。 为了评估Oncuria™（一种用于检测排尿样本中膀胱癌的多重免疫检测方法）的性能，在一个由362名因膀胱癌评估而前瞻性收集的受试者组成的多机构队列中对该检测进行了评估。在一个独立的临床实验室中对10种生物标志物（A1AT、APOE、ANG、CA9、IL8、MMP9、MMP10、PAI1、SDC1和VEGFA）进行了平行检测。评估了该检测识别膀胱癌患者的能力。膀胱癌的状况通过膀胱镜检查和组织活检得以确认。使用线性判别分析（LDA）评估了生物标志物与人口统计学因素的关联，并通过有监督学习和交叉验证分析得出了预测模型。使用受试者工作特征（ROC）曲线评估了诊断性能。 这10种生物标志物的组合提供了0.93的曲线下面积（AUROC）[95%置信区间为0.87 - 0.98]，优于任何单一生物标志物。将人口统计学数据（年龄、性别和种族）添加到混合特征中提高了诊断性能，AUROC为0.95[95%置信区间为0.90 - 1.00]。对于膀胱癌分类，该混合特征的总体灵敏度为0.93，特异性为0.93，阳性预测值（PPV）为0.65，阴性预测值（NPV）为0.99。诊断组合对于高级别膀胱癌、低级别膀胱癌、肌层浸润性膀胱癌（MIBC）和非肌层浸润性膀胱癌（NMIBC）的灵敏度值分别为0.94、0.89、0.97和0.93。 一组生物标志物的尿液水平能够准确区分膀胱癌患者和对照组。多重Oncuria™检测能够在无创的患者环境中实现对膀胱癌的高效准确检测和监测。 网络版包含补充材料，可在10.1186/s12967 - 021 - 02796 - 4获取。