Researchers used nucleosome patterns in blood-derived DNA to identify signals associated with treatment resistance and recurrent disease.
Kumamoto University researchers developed a blood-based approach to detect breast cancer recurrence by analyzing how DNA is packaged within cells rather than focusing solely on genetic mutations.
The study, published in Cancer Research Communications, analyzed cell-free DNA (cfDNA) from 150 breast cancer samples, including 105 from primary cases and 45 from recurrent or metastatic disease. The method focuses on nucleosomes, which are structures where DNA wraps around proteins called histones. Because the arrangement of nucleosomes reflects gene regulation and how DNA is packaged, blood-derived DNA can carry clues about changes occurring inside cancer cells.
Identifying Resistance Signals
The research team targeted 26 genomic regions previously identified as undergoing changes when breast cancer cells develop resistance to hormone therapy. They found that recurrent breast cancer was associated with increased genetic variants and shorter cfDNA fragments.
Two specific genomic regions, RERE and SYNPO2, were particularly effective for identification. A nucleosome-based score derived from these regions distinguished primary from recurrent breast cancer with an area under the curve (AUC) of 0.826. The researchers further improved recurrence prediction by combining nucleosome data with other cfDNA features using machine learning models.
Clinical Potential and Limitations
The findings suggest that cfDNA analysis can reveal changes in gene regulation and chromatin structure associated with treatment resistance and recurrence. This low-cost, minimally invasive liquid biopsy approach could eventually support earlier recurrence surveillance, treatment-response assessment, and more personalized clinical decisions, says Kumamoto University in a release.
However, the researchers emphasize that the study was based on a limited retrospective cohort. The team, led by associate professor Sugiko Watanabe and professor Mitsuyoshi Nakao, says that larger, prospective studies are necessary to address differences in breast cancer subtypes, patient backgrounds, and clinical settings before the approach can be broadly applied.
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