Transcriptionally informed nucleosome profiling of circulating cell-free DNA predicts breast cancer recurrence

Abstract Cell-free DNA (cfDNA) offers a minimally invasive approach to capture genomic and epigenetic dynamics during cancer progression. We performed targeted sequencing of 26 gene loci transcriptionally regulated during the acquisition of therapy resistance in breast cancer and analyzed blood-derived cfDNA from 150 breast cancer samples (105 primary and 45 recurrent). Recurrent samples exhibited increased genomic variant counts in both coding and non-coding regions, accompanied by shorter cfDNA fragment lengths. Further, cfDNA fragmentation profiles were variable in recurrent samples, with frequently amplified loci such as ERBB2 and concurrent reductions at loci including RERE and SYNPO2. Notably, nucleosome occupancy-derived scores from RERE and SYNPO2 distinguished recurrent from primary cancer with high accuracy (area under the curve = 0.826). Using a machine-learning approach, integration of these cfDNA features accurately predicted breast cancer relapse. Collectively, these findings demonstrate that cfDNA-based profiling focused on transcriptional alterations provides a sensitive strategy for detecting breast cancer recurrence.