Development and validation of circulating protein signatures as diagnostic biomarkers for biliary tract cancer
JHEP Reports, 2022
Christensen T., Maag E., Larsen O., Feltoft C., Nielsen K., Jensen L., Leerhøy B., Hansen C., Chen I., Nielsen D., Johansen J.
| Disease area | Application area | Sample type | Products |
|---|---|---|---|
Oncology | Patient Stratification | Plasma Serum |  Olink Target 96 |
Abstract
Background and aims
Biliary tract cancer (BTC) has a dismal prognosis partly due to late diagnosis, and diagnostic biomarkers are needed. The purpose of this project was to identify and validate multiprotein signatures that can discriminate patients with BTC from non-cancer controls.
Methods
The study included treatment-naïve patients with BTC, healthy controls, and patients with benign conditions including benign biliary tract disease. Participants were divided into 3 non-overlapping cohorts: a case-control-based discovery cohort (BTC = 186, controls = 249), a case-control-based validation cohort 1 (BTC = 113, controls = 241), and a cohort study-based validation cohort 2 including participants (BTC = 8, controls = 132) referred for diagnostic work-up for suspected cancer.
Immuno-Oncology (I-O)-related proteins were measured in serum and plasma using a proximity extension assay (Olink Proteomics). Lasso and Ridge regressions were used to generate protein signatures of I-O-related proteins and carbohydrate antigen 19-9 (CA19-9) in the discovery cohort.
Results
Sixteen protein signatures, including 2 to 82 proteins, were generated. All signatures included CA19-9 and chemokine C-C motif ligand 20. Signatures discriminated between patients with BTC vs. controls, with the area under receiver operating characteristic curve (AUC) ranging from 0.95 to 0.99 in the discovery cohort and 0.94 to 0.97 in the validation cohort 1. In validation cohort 2, the AUC ranged from 0.84 to 0.94. Nine signatures achieved a specificity of 82% to 84% while keeping a sensitivity of 100% in validation cohort 2. All signatures performed better than CA19-9, and signatures including >15 proteins showed the best performance.
Conclusion
The study demonstrated that it is possible to generate protein signatures that can successfully discriminate BTC from non-cancer controls.