Quantitative ctDNA Detection in Hepatoblastoma: Implications for Precision Medicine

Simple Summary Driver mutations in CTNNB1 are a hallmark of hepatoblastoma and offer a common biomarker for a liquid biopsy approach that is based on the presence of CTNNB1 circulating tumor DNA (ctDNA). We provide promising evidence for the utility of quantitative CTNNB1 ctDNA detection in hepatoblastoma for dynamic tumor burden and treatment response monitoring, compared with the current clinical indicators and biomarkers for this disease.Abstract Hepatoblastoma is characterized by driver mutations in CTNNB1, making it an attractive biomarker for a liquid biopsy approach utilizing circulating tumor DNA (ctDNA). This prospective observational study sought to ascertain the feasibility of ctDNA detection in patients with hepatoblastoma and explore its associations with established clinical indicators and biomarkers, including serum Alpha-fetoprotein (AFP). We obtained 38 plasma samples and 17 tumor samples from 20 patients with hepatoblastoma. These samples were collected at various stages: 10 at initial diagnosis, 17 during neoadjuvant chemotherapy, 6 post-operatively, and 5 at disease recurrence. Utilizing a bespoke sequencing assay we developed called QUENCH, we identified single nucleotide variants and deletions in CTNNB1 ctDNA. Our study demonstrated the capability to quantitate ctDNA down to a variant allele frequency of 0.3%, achieving a sensitivity of 90% for patients at initial diagnosis, and a specificity of 100% at the patient level. Notably, ctDNA positivity correlated with tumor burden, and ctDNA levels exhibited associations with macroscopic residual disease and treatment response. Our findings provide evidence for the utility of quantitative ctDNA detection in hepatoblastoma management. Given the distinct detection targets, ctDNA and AFP-based stratification and monitoring approaches could synergize to enhance clinical decision-making. Further research is needed to elucidate the interplay between ctDNA and AFP and determine the optimal clinical applications for both methods in risk stratification and residual disease detection.