National Science and Technology Major Project of China
National Natural Science Foundation of China
Scientific Foundation of Fujian Province
Regional development project of Fujian province
Science and Technology development project of central government
Science and Technology major project of Fuzhou
ARTICLE ABSTRACTCirculating 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.