Finding Sorafenib-Responding Signature in Hepatocellular Carcinoma (HCC)

Sorafenib has been a cornerstone in hepatocellular carcinoma (HCC) therapy; however, its efficacy is limited, and identifying patients that will respond to sorafenib is challenging. Gene expression data from 33 HCC tumors treated with sorafenib were analyzed to construct a prediction model aimed at identifying patients with greater benefit from sorafenib treatment. The analysis of transcriptome data revealed a 50-gene signature, KUSS50 (Korea University Sorafenib Signature with 50 genes), that exhibited high predictive power in identifying patients responded to sorafenib treatment in a training cohort. Extensive validation across 2 independent cohorts (IMbrave150 and BIOSTORM) given sorafenib demonstrated KUSS50's high specificity in predicting sorafenib response. Genomic analyses identified distinct molecular characteristics linked with KUSS50 subtypes, including an increased mutation rate and activation of ferroptosis, suggesting increased baseline ferroptotic activity in HCCs, which may sensitize them to sorafenib. The benefit subtype also overlapped with those in previously defined genomic HCC subtypes associated with stemness and aggressiveness. Conversely, the non-benefit subtype correlated with ?-catenin mutations and increased tumor purity, underscoring the biological significance of the signature. In conclusion, KUSS50 is a clinically actionable biomarker for optimizing HCC patient selection for treatment with sorafenib, providing an opportunity to improve outcomes. Further exploration of KUSS50's underlying biology, particularly the involvement of ferroptosis in sorafenib sensitivity, could provide additional therapeutic insights. Overall design: All patients underwent liver biopsy analysis prior to sorafenib use, and total RNA was obtained from their formalin-fixed, paraffin-embedded tissue blocks. Total RNA was extracted from slides containing tissue sections from these blocks using a Purigen Biosystems Ionic Purification System, and the DV200 values (representing the percentage of fragments >200 nucleotides) exceeded 15%. mRNA expression data for HCCs in the study patients were subsequently generated on the Illumina NovaSeq 6000 platform, employing S4-xp-200 lanes with a sequencing depth of 50 million reads per sample. The quality of the raw reads was checked using FastQC (v0.11.5) and summarized using MultiQC (v1.7). FASTQ files were mapped to the human reference genome (GRCh38) using STAR (v2.7.10a), and gene expression level was quantified using RSEM (v1.3.3) with default parameter settings.