Abstract
Background: Non-invasive urine-based biomarkers can potentially improve current diagnostic and monitoring protocols for bladder cancer (BC). Here we assess the performance of earlier published biomarker panels for BC detection (BC-116) and monitoring of recurrence (BC-106) in combination with cytology, in two prospectively collected patient cohorts. Methods: Of the 602 patients screened for BC, 551 were found eligible. For the primary setting, 73 patients diagnosed with primary BC (n = 27) and benign urological disorders, including patients with macroscopic haematuria, cystitis and/or nephrolithiasis (n = 46) were included. In total, 478 patients under surveillance were additionally considered (83 BC recurrences; 395 negative for recurrence). Urine samples were analysed with capillary electrophoresis-mass spectrometry. The biomarker score was estimated via support vector machine-based software. Results: Validation of BC-116 biomarker panel resulted in 89% sensitivity and 67% specificity (AUCBC-116 = 0.82). A diagnostic score based on cytology and BC-116 resulted in good (AUCNom116 = 0.85) but not significantly better performance (P = 0.5672). A diagnostic score including BC-106 and cytology was evaluated (AUCNom106 = 0.82), significantly outperforming both cytology (AUCcyt = 0.72; P = 0.0022) and BC-106 (AUCBC-106 = 0.67; P = 0.0012). Conclusions: BC-116 biomarker panel is a useful test for detecting primary BC. BC-106 classifier integrated with cytology showing >95% negative predictive value, might be useful for decreasing the number of cystoscopies during surveillance.
| Original language | English |
|---|---|
| Journal | British Journal of Cancer |
| Volume | 127 |
| Issue number | 11 |
| Pages (from-to) | 2043-2051 |
| Number of pages | 9 |
| ISSN | 0007-0920 |
| DOIs | |
| Publication status | Published - 23.11.2022 |
Funding
This work was supported by BioMedBC (752755; H2020-MSCA-IF-2016) a programme funded by the European Commission (EC) under Marie Sklodowska-Curie actions (MSCA) H2020 Work Programme. The specific role of the funding organisation (EC) was to financially support the acquisition of the data and part of the data analysis.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Research Areas and Centers
- Research Area: Luebeck Integrated Oncology Network (LION)
DFG Research Classification Scheme
- 2.22-14 Hematology, Oncology
- 2.22-23 Reproductive Medicine, Urology
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