Breast Cancer (BrCa) remains a devastating disease presenting emerging needs for effective management. Recently, epigenetic biomarkers are assessed in liquid biopsy for diagnostic and prognostic applications. This study applies a 3-step data-driven biomarker discovery pipeline to identify robust methylation biomarkers and generate high-performance biosignatures specific for clinically significant BrCa end-points, followed by laboratory validation in patient cell-free DNA (cfDNA).

Publicly available genome-wide methylomes from 520 BrCa and 185 non-diseased breast tissues (discovery dataset) were analyzed via Automated Machine Learning (AutoML, JADBio) to identify BrCa-specifically methylated promoters. Bioinformatic search revealed any BrCa biological relevance. Next, the methylation of identified promoters was experimentally validated in plasma cfDNA from 195 BrCa patients and 135 healthy individuals by Methylation Specific qPCR (qMSP) (validation cohort). Finally, autoML analyzed experimental and clinical data to develop optimized classifying biosignatures for diagnosis, prognosis, and prediction.

AutoML identified 3 BrCa-specific methylated promoters in CLDN15, MRGPRD and ZNF430. Pathway analysis revealed implications with biological processes such as signaling and transcription. Laboratory validation using clinical cfDNA samples confirmed elevated methylation levels in BrCa patients for all 3 promoters, which were correlated with poor prognostic and predictive parameters. Classification analysis by autoML of experimental methylation measurements and patients’ clinical data built 5 specific models: a diagnostic biosignature distinguishing BrCa from health (AUC 0.79, CI: 0.75–0.84), a classification biosignature differentiating BrCa disease status (adjuvant, neoadjuvant, and metastatic group) (AUC 0.68, CI: 0.62–0.72), a prognostic biosignature predicting relapse (AUC 0.79, CI: 0.74–0.83), a biosignature predicting treatment response in metastatic patients (AUC 0.86, CI: 0.67–1.00), and a biosignature differentiating distinct molecular subtypes (AUC of 0.71, CI: 0.64–0.77), underscoring their possible clinical utility.

Our data-driven approach successfully identified 3 BrCa-specifically methylated promoters in genes not previously implicated in BrCa. Their role in pathology needs further attention as they could also represent novel targets. Moreover, the laboratory validation in clinical BrCa cfDNA samples led to the development of 5 biosignatures, some demonstrating strong predictive performance. The low number of features and the minimally invasive nature of liquid biopsy highlight the potential for clinical implementation of great value.