Noninvasive detection and prognostic stratification of biliary tract cancer using cell-free DNA fragmentomics: a model development and validation study

Biliary tract cancer (BTC) is typically diagnosed at an advanced stage due to the lack of effective screening tools, resulting in limited therapeutic options and poor survival outcomes. Therefore, there is a critical need for non-invasive strategies that enable early detection and risk stratification. Fragmentomic profiling of cell-free DNA (cfDNA) captures genome-wide fragmentation patterns reflecting tumor-associated chromatin structure and genomic instability, providing a promising approach for non-invasive cancer detection. In this study, we developed a low-pass whole-genome sequencing (WGS)-based framework for BTC detection and postoperative risk assessment. Plasma samples were analyzed to derive three fragmentomic features, including copy number variation, fragment size distribution, and promoter fragmentation entropy, which were integrated into a machine-learning model trained using five-fold cross-validation. The model demonstrated robust performance across independent validation cohorts, outperforming individual fragmentomic features and conventional serum biomarkers, and accurately distinguished BTC from benign biliary diseases. Longitudinal analyses revealed that cfDNA fragmentomic risk scores dynamically tracked disease burden and treatment response. Importantly, postoperative risk scores were independently associated with disease-free survival, highlighting their prognostic value. Collectively, these findings establish a scalable and cost-effective framework for cfDNA-based BTC detection and monitoring using low-pass WGS data. This approach shows strong potential for targeted screening in high-risk populations and for guiding personalized postoperative surveillance and clinical management for BTC patients.