Background: Breast cancer (BC) remains the second leading cause of cancer-related mortality among women worldwide. Liquid biopsy based on circulating tumor DNA (ctDNA) offers a promising noninvasive approach for early detection; however, differentiating malignant tumors from benign abnormalities remains a significant challenge.
Results: Here, we developed a multimodal approach to analyze cfDNA methylation and fragmentomic patterns in 273 BC patients, 108 individuals with benign breast conditions, and 134 healthy controls. Genome-wide analyses revealed distinct cfDNA copy number alterations and cytosine-enriched cleavage sites in BC patients. Targeted sequencing further revealed unique methylation patterns, including hypermethylation in GPR126, KLF3, and TLR10 and hypomethylation in TOP1 and MAFB. Our machine-learning model achieved an AUC of 0.90, with 93.6% specificity and 62.1-66.3% sensitivity for stage I-II cancers. In symptomatic populations, sensitivities were 50.0%, 68.2%, and 64.7% for BI-RADS categories 3, 4, and 5, respectively, with 96.1% specificity.
Conclusions: These findings underscore the potential of cfDNA biomarkers to enhance BC detection and reduce the rate of unnecessary biopsies.
Keywords: Benign abnormalities; Breast cancer; CfDNA; Methylation and fragmentomic.
© 2025. The Author(s).