Comprehensive Liquid Profiling of Circulating Tumor DNA and Protein Biomarkers in Long-Term Follow-Up Patients with Hepatocellular Carcinoma

TLDR

Circulating tumor DNA (ctDNA) offers a novel means to detect tumor burden and predict clinical outcomes in hepatocellular carcinoma (HCC). This study evaluates HCC circulating genetic features and integrates them into a robust strategy for monitoring and prognostic assessment. The authors performed target sequencing and low‑coverage whole‑genome sequencing of plasma from 34 long‑term follow‑up HCC patients to capture somatic SNVs and CNVs, collected clinical data, and integrated comprehensive ctDNA mutation profiles to assess tumor burden and prognostic performance against imaging and protein biomarkers. During follow‑up, ctDNA SNVs and CNVs dynamically mirrored tumor burden, allowing detection of recurrence up to 4.6 months before imaging, outperforming AFP, AFP‑L3%, and DCP, and accurately predicting relapse‑free and overall survival, with combined ctDNA‑DCP further enhancing MRD sensitivity.

Abstract

Circulating tumor DNA (ctDNA) provides a novel approach for detecting tumor burden and predicting clinical outcomes of hepatocellular carcinoma (HCC). Here, we performed a thorough evaluation of HCC circulating genetic features and further fully integrated them to build a robust strategy for HCC monitoring and prognostic outcome assessment.We performed target sequencing and low-coverage whole-genome sequencing on plasma samples collected from 34 long-term follow-up patients with HCC to capture tumor somatic SNVs and CNVs, respectively. Clinical information was also obtained to evaluate the prognostic performance of ctDNA comparing with clinically applied protein biomarkers.All plasma samples before surgery showed somatic genetic variations resembling corresponding tumor tissues. During follow-up, SNVs and CNVs dynamically changed correlating to patients' tumor burden. We integrated the comprehensive ctDNA mutation profiles to provide a robust strategy to accurately assess patients' tumor burden with high consistence comparing with imaging results. This strategy could discover tumor occurrence in advance of imaging for an average of 4.6 months, and showed superior performance than serum biomarkers AFP, AFP-L3%, and Des-Gamma-Carboxy Prothrombin (DCP). Furthermore, our strategy could precisely detect minimal residual disease (MRD) in advance and predict patients' prognostic outcomes for both relapse-free survival (P = 0.001) and overall survival (P = 0.001); further combining ctDNA with DCP could increase the sensitivity for MRD detection.We demonstrated that plasma CNV and SNV levels dynamically correlated with patients' tumor burden in HCC. Our strategy of comprehensive mutation profile integration could accurately and better evaluate patients' prognostic risk and detect tumor occurrence in advance than traditional strategies.