Machine learning-based cfDNA fragmentation profiling using automated capillary electrophoresis for early detection of...

Early detection of hepatocellular carcinoma (HCC) remains a significant clinical challenge due to the limited sensitivity of current surveillance tools, alpha-fetoprotein (AFP) and ultrasound. Recently, cell-free DNA (cfDNA) fragmentation analysis has shown promise in cancer detection; however, current sequencing-based approaches remain costly and unsuitable for large-scale screening.

Here, we introduce a predictive model for early HCC detection called "CEliver" (CfDNA-based automated capillary Electrophoresis method for Liver cancer screening), a model leveraging high-dimensional fragmentation profiling from the intensity distribution of cfDNA fragment lengths using automated capillary electrophoresis. We developed CF-2D features, a computational framework that extracts over 300 quantitative features from electropherogram data, including cfDNA concentration, dominant fragment sizes, two-dimensional shape descriptors, and short-to-long fragment ratios. We integrated these features with AFP levels to build the CEliver model, developed in 111 individuals and validated in an independent cohort of 69 subjects.

Here we show the CF-2D profiles differ significantly between HCC patients and high-risk individuals. The CEliver model achieves 98% sensitivity across all HCC cases, and 96% sensitivity with 99% specificity for early-stage HCC (stage 0/A), substantially outperforming AFP (60% overall sensitivity, 35% for early-stage). In external validation, CEliver shows 88% sensitivity and 100% specificity.

CEliver provides a practical and accurate strategy for early HCC detection. By enabling high-dimensional cfDNA fragmentomics analysis on a widely accessible electrophoresis platform, it bridges the gap between research-grade cfDNA technologies and real-world clinical implementation. This method represents a simple and scalable approach that could potentially be applied in HCC surveillance.