Genomic landscape of resectable hepatocellular carcinoma defined by tissue based whole-exome sequencing

Introduction: Hepatocellular carcinoma (HCC) is the seventh most common malignancy worldwide. Recent advances, including immune checkpoint inhibitors and anti-VEGFR therapies, have improved outcomes for HCC patients, especially in the adjuvant setting after surgical resection. However, many patients still experience disease recurrence and develop treatment resistance, and the molecular biomarkers underlying these events remain poorly defined. Here, we report a retrospective translational study using tissue-based whole-exome sequencing (WES) to investigate molecular biomarkers in patients with resectable HCC. Methods: In this retrospective study, 88 patients with surgically resected HCC were enrolled. Tumor tissue samples collected at the time of surgery were submitted for molecular profiling using PredicineWES+, a whole-exome sequencing (WES) assay with boosted sequencing in 600 tumor-relevant genes. Matched normal tissue samples also underwent WES to rule out germline variants. Results: Across 88 patients, PredicineWES+ identified 14,042 somatic mutations and 951 copy number variations. The median tumor mutation burden was 2.41 muts/Mb (range, 0.48-9.74 muts/Mb). The most frequently altered genes were TP53 (70%), TERT (41%), RB1 (25%), and CTNNB1 (22%), with TP53 and RB1 predominantly affected by copy number loss and most TERT variants occurring in the promoter (including 30 C228T events). TP53 alterations were slightly more prevalent in Barcelona Clinic Liver Cancer (BCLC) stage C than in earlier stages, but without statistical significance. No significant associations between TP53, TERT, RB1, or CTNNB1 alterations and prognosis were observed. Conclusion: Comprehensive tissue-based WES profiling in resectable HCC revealed a characteristic genomic landscape, highlighting the complexity of molecular drivers underlying recurrence and resistance in HCC and underscore the need for integrative biomarker strategies in tumor genomics to refine risk stratification and guide adjuvant therapy.