Changes in the Glycosylation of Kininogen and the Development of a Kininogen-Based Algorithm for the Early Detection of HCC

Background: Hepatocellular carcinoma (HCC) has the greatest increase in mortality among all solids tumors in the United States related to low rates of early tumor detection. Development of noninvasive biomarkers for the early detection of HCC may reduce HCC-related mortality.

Methods: We have developed an algorithm that combines routinely observed clinical values into a single equation that in a study of >3,000 patients from 5 independent sites improved detection of HCC as compared with the currently used biomarker, alpha-fetoprotein (AFP), by 4% to 20%. However, this algorithm had limited benefit in those with AFP <20 ng/mL. To that end, we have developed a secondary algorithm that incorporates a marker, fucosylated kininogen, to improve the detection of HCC, especially in those with AFP <20 ng/mL and early-stage disease.

Results: The ability to detect early-stage AFP-negative (AFP <20 ng/mL) HCC increased from 0% (AFP alone) to 89% (for the new algorithm). Glycan analysis revealed that kininogen has several glycan modifications that have been associated with HCC, but often not with specific proteins, including increased levels of core and outer-arm fucosylation and increased branching.

Conclusions: An algorithm combining fucosylated kininogen, AFP, and clinical characteristics is highly accurate for early HCC detection.

Impact: Our biomarker algorithm could significantly improve early HCC detection and curative treatment eligibility in patients with cirrhosis. Cancer Epidemiol Biomarkers Prev; 26(5); 795–803. ©2017 AACR.

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