Predictive biomarkers for systemic therapy of hepatocellular carcinoma

Introduction: Hepatocellular carcinoma (HCC) is the most common primary liver cancer and the third cancer-related cause of death worldwide. In recent years, several systemic therapy drugs including sorafenib, lenvatinib, regorafenib, cabozantinib, ramucicurab, nivilumab, and pembrolizumab have been approved by FDA for advanced HCC. However, their insufficient efficacy, toxicity, and drug resistance require clinically applicable and validated predictive biomarkers. Areas covered: Our review covers the recent advancements in the identification of proteomic/genomic/epigenomic/transcriptomic biomarkers for predicting HCC treatment efficacy with the use of multi-kinase inhibitors (MKIs), CDK4/6 inhibitors, and immune checkpoint inhibitors (ICIs). Alpha-fetoprotein, des-carboxyprothrombin, vascular endothelial growth factor, angiopoietin-2, and dysregulated MTOR, VEGFR2, c-KIT, RAF1, PDGFR beta have the potential of proteomic/genomic biomarkers for sorafenib treatment. Alanine aminotransferase, aspartate aminotransferase, and albumin-bilirubin grade can predict the efficacy of other MKIs. Rb, p16, and Ki-67, and genes involved in cell cycle regulation, CDK1-4, CCND1, CDKN1A, and CDKN2A have been proposed for CD4/6 inhibitors, while dysregulated TERT, CTNNB1, TP53 FGF19, and TP53 are found to be predictors for ICI efficacy. Expert opinion: There are still limited clinically applicable and validated predictive biomarkers to identify HCC patients who benefit from systemic therapy. Further prospective biomarker validation studies for HCC personalized systemic therapy are required.

Automated summary

This review discusses recent advancements in identifying proteomic/genomic/epigenomic/transcriptomic biomarkers for predicting HCC treatment efficacy with the use of multi-kinase inhibitors (MKIs), CDK4/6 inhibitors, and immune checkpoint inhibitors (ICIs). Various biomarkers such as alpha-fetoprotein, des-carboxyprothrombin, and vascular endothelial growth factor have the potential to predict the efficacy of sorafenib treatment.