High-Precision Prediction of Breast Cancer Recurrence via Blood Test: Visualizing Signs of Treatment Resistance through cfDNA Nucleosome Analysis

A research group led by Specially Appointed Associate Professor Sugiko Watanabe, Specially Appointed Professor Mitsuyoshi Nakao of the Institute of Molecular Embryology and Genetics at Kumamoto University, and Professor Yutaka Yamamoto of Kumamoto University Hospital has developed a new method to predict breast cancer recurrence with high precision by analyzing the nucleosome structure and fragmentation patterns of circulating cell-free DNA (cfDNA). In this study, the team focused on 26 gene regions subject to transcriptional regulation during the acquisition of treatment resistance in breast cancer, conducting cfDNA analysis on a total of 150 samples (105 primary breast cancer cases and 45 recurrent cases). The results showed an increase in the number of mutations in both coding and non-coding regions, as well as shorter cfDNA fragments in recurrent cases. Furthermore, it was revealed that scores derived from nucleosome structures near the RERE and SYNPO2 genes could distinguish between primary and recurrent breast cancer with high precision. Additionally, by integrating multiple characteristic cfDNA factors using machine learning, the team demonstrated that breast cancer recurrence could be predicted with even greater accuracy. This study shows that by capturing not only genetic mutations but also epigenomic information such as transcriptional regulation and chromatin remodeling, cfDNA analysis can potentially monitor breast cancer recurrence and treatment resistance in a minimally invasive manner. Future applications are expected in treatment selection for personalized medicine and early detection of recurrence. The results were published in the journal 'Cancer Research Communications' on May 28, 2026.