Background/aim: Uniform treatment of hepatocellular carcinoma (HCC) with molecular targeted drugs (e.g., sorafenib) results in a poor overall tumor response when tumor subtyping is absent. Patient stratification based on actionable gene expression is a method that can potentially improve the effectiveness of these drugs. Here we aimed to identify the clinical application of actionable genes in predicting response to sorafenib.
Methods: Through quantitative real-time reverse transcription PCR, we analyzed the expression levels of seven actionable genes (VEGFR2, PDGFRB, c-KIT, c-RAF, EGFR, mTOR, and FGFR1) in tumors versus noncancerous tissues from 220 HCC patients treated with sorafenib. Our analysis found that 9 responders did not have unique clinical features compared to nonresponders. A receiver operating characteristic curve evaluated the predictive performance of the treatment benefit score (TBS) calculated from the actionable genes.
Results: The responders had significantly higher TBS values than the nonresponders. With an area under the curve of 0.779, a TBS combining mTOR with VEGFR2, c-KIT, and c-RAF was the most significant predictor of response to sorafenib. When used alone, sorafenib had a 0.7-3% response rate among HCC patients, but when stratifying the patients with actionable genes, the tumor response rate rose to 15.6%. Furthermore, actionable gene expression is significantly correlated with tumor response.
Conclusions: Our findings on patient stratification based on actionable molecular subtyping potentially provide a therapeutic strategy for improving sorafenib's effectiveness in treating HCC.
Keywords: Biomarker; Gene signature; Hepatocellular carcinoma; Sorafenib.
Copyright © 2020 by S. Karger AG, Basel.