A combined bile and urine proteomic test for cholangiocarcinoma diagnosis in patients with biliary strictures of unknown origin. Issue 5 (August 2017)

• Record Type: Journal Article
• Authors: Voigtländer, Torsten; Metzger, Jochen; Schönemeier, Bastian; Jäger, Mark; Mischak, Harald; Manns, Michael P; Lankisch, Tim O
• Is Part Of: United European Gastroenterology journal. Volume 5:Issue 5(2017)
• Page Start: 668
• Page End: 676
• Publication Date: 2017-08
• Subjects: Cholangiocarcinoma diagnosis -- biliary stricture -- proteomics -- tumor marker -- primary sclerosing cholangitis; Gastroenterology -- Periodicals; Periodicals; 616.33005
• DOI: 10.1177/2050640616687836
• Languages: English
• ISSNs: 2050-6406

Abstract

Background: Detection of cholangiocarcinoma (CC) remains a diagnostic challenge particularly in patients with primary sclerosing cholangitis (PSC). We recently established diagnostic peptide marker models in bile and urine to detect CC. Our aim was to combine both models to reach a higher diagnostic accuracy of CC diagnosis. Methods: Bile (BPA) and urine (UPA) proteome analysis by capillary electrophoresis mass spectrometry was performed in a case-control phase II study on 87 patients (36 CC including 13 with CC on top of PSC, 33 PSC and 18 other benign disorders). A logistic regression model with both analyses was developed and subsequently validated in a prospective cohort of 45 patients. Results: In the retrospective study, single BPA and UPA showed sensitivities of 83 and 89 % and specificities of 80 and 86 % with an area under the curve (AUC) value of 0.85 and 0.93. If CC was defined as positive UPA and BPA the combination resulted in a sensitivity of 72 % and a specificity of 96 %. The logistic regression model resulted in an increase in sensitivity to 92 % at 84 % specificity with an AUC of 0.96. Applied to the prospective study cohort, the logistic regression model was superior in its sensitivity (94%) and specificity (76%) over single BPA (63% sensitivity, 69% specificity) and UPA (81% sensitivity, 72% specificity) with an AUC of 0.84. Conclusion: Our logistic regression model enables CC diagnosis with a higher accuracy than currently available diagnostic tools leading potentially to an earlier diagnosis.