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 non-invasive biomarkers for 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 to the currently used biomarker, alpha-feto-protein (AFP), by 4-20%. 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, alpha fetoprotein, 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.
- Received November 28, 2016.
- Revision received January 13, 2017.
- Accepted January 30, 2017.
- Copyright ©2017, American Association for Cancer Research.