Original Article
Characterization of genomic clones using circulating tumor DNA in patients with hepatocarcinoma
Abstract
Background: Hepatocarcinoma (HCC) is often diagnosed at an advanced stage with poor prognosis. A non-invasive method concerning circulating tumor DNA (ctDNA) has been recognized as a promising biomarker. ctDNA has been widely studied to monitor tumor dynamics and measure tumor burden. However, the results of previous studies for biomarkers for HCC have generally been inconsistent and limited in clinical application.
Methods: HCC usually represent a mixture of different cancer cell clones differing in mutation content, which can be used to monitor important features such as treatment responses. In this study, we used Bayesian cluster with PyClone to identify clonal population structures based on variations from ctDNA of a 4 HCC patients dataset.
Results: As a result, reductions in cellular prevalences of private mutation clusters were observed between preoperative and postoperative plasma samples, which reflected the treatment responses of surgery. We also identified expansion and sharing sub-clonality of initially minor clones in plasma samples after treatment, and located clusters in patients which might be used as actionable targets.
Conclusions: These results provided a more complete picture of the liver cancer pathogenesis. The comparison of preoperative and postoperative plasma samples showed that ctDNA can be used to real-time sampling of clonal evolution in patients with hepatocarcinoma. In addition to dynamic monitoring of disease progression and response to therapy, characterizing of dynamics of genomic clones can be used to determine the benefit of new therapeutics and guide therapy.
Methods: HCC usually represent a mixture of different cancer cell clones differing in mutation content, which can be used to monitor important features such as treatment responses. In this study, we used Bayesian cluster with PyClone to identify clonal population structures based on variations from ctDNA of a 4 HCC patients dataset.
Results: As a result, reductions in cellular prevalences of private mutation clusters were observed between preoperative and postoperative plasma samples, which reflected the treatment responses of surgery. We also identified expansion and sharing sub-clonality of initially minor clones in plasma samples after treatment, and located clusters in patients which might be used as actionable targets.
Conclusions: These results provided a more complete picture of the liver cancer pathogenesis. The comparison of preoperative and postoperative plasma samples showed that ctDNA can be used to real-time sampling of clonal evolution in patients with hepatocarcinoma. In addition to dynamic monitoring of disease progression and response to therapy, characterizing of dynamics of genomic clones can be used to determine the benefit of new therapeutics and guide therapy.
