Abstract
Sorafenib is the only standard treatment for unresectable hepatocellular carcinoma (HCC), but it provides modest survival benefits over placebo, necessitating predictive biomarkers of the response to sorafenib. Serum samples were obtained from 115 consecutive patients with HCC before sorafenib treatment and analyzed by multiple reaction monitoring-mass spectrometry (MRM-MS) and ELISA to quantify candidate biomarkers. We verified a triple-marker panel to be predictive of the response to sorafenib by MRM-MS, comprising CD5 antigen-like (CD5L), immunoglobulin J (IGJ), and galectin-3-binding protein (LGALS3BP), in HCC patients. This panel was a significant predictor (AUROC > 0.950) of the response to sorafenib treatment, having the best cut-off value (0.4) by multivariate analysis. In the training set, patients who exceeded this cut-off value had significantly better overall survival (median, 21.4 months) than those with lower values (median, 8.6 months; p = 0.001). Further, a value that was lower than this cutoff was an independent predictor of poor overall survival [hazard ratio (HR), 2.728; 95% confidence interval (CI), 1.312–5.672; p = 0.007] and remained an independent predictive factor of rapid progression (HR, 2.631; 95% CI, 1.448–4.780; p = 0.002). When applied to the independent validation set, levels of the cut-off value for triple-marker panel maintained their prognostic value for poor clinical outcomes. On the contrast, the triple-marker panel was not a prognostic factor for patients who were treated with transarterial chemoembolization (TACE). The discriminatory signature of a triple-marker panel provides new insights into targeted proteomic biomarkers for individualized sorafenib therapy.
Footnotes
Author contributions: H. Kim, S.J. Yu, J.-H. Yoon, and Y. Kim contributed to study concept and design; H. Kim, S.J. Yu, I. Yeo, J.-J. Yoo, D.H. Lee, Y. Cho, E.J. Cho, J.-H. Lee, and Y.J. Kim contributed to acquisition of data; H. Kim, S.J. Yu, S. Lee, J. Jun, and T. Park contributed to statistical analysis; H. Kim, S.J. Yu, J.-H. Yoon, and Y. Kim contributed to drafting of the manuscript.
↵* This work was supported by the Multi-omics Research Program, a National Research Foundation grant (No. 2011-0030740), the Industrial Strategic Technology Development Program (#10045352), the Korea Health Technology R&D Project (No. HI14C2640), and grants from the SNUH Research Fund (No. 04-2013-0830) and the Liver Research Foundation of Korea.
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This article contains supplemental material.
- Received December 23, 2016.
- Revision received May 10, 2017.
- © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.