Proteomic Biomarker Discovery in Breast Cancer: Advances, Challenges, and Translational Prospects

Breast cancer remains a leading cause of cancer-related morbidity and mortality among women worldwide. Despite major progress in genomic classification, molecular pathology, and targeted therapy, the identification of clinically reliable proteomics-derived biomarkers for early detection, prognostic stratification, treatment response prediction, and longitudinal monitoring remains an unresolved translational challenge. Recent studies have applied proteomic profiling to breast cancer tissues and clinically accessible biofluids, including serum, plasma, saliva, nipple aspirate fluid, urine, and extracellular vesicle-enriched fractions, to identify protein signatures with potential diagnostic and therapeutic relevance. Contemporary platforms, including LC-MS/MS, data-independent acquisition mass spectrometry, multiplexed quantitative proteomics, targeted proteomics using multiple reaction monitoring and parallel reaction monitoring, spatial proteomics, and affinity-based high-throughput assays such as Olink and SomaScan, have expanded the analytical depth and clinical scalability of breast cancer biomarker research. However, many candidate biomarkers remain confined to discovery or early verification stages because of limited analytical sensitivity, insufficient specificity, inter-platform variability, small and heterogeneous cohorts, incomplete external validation, and uncertainty regarding clinical utility beyond established pathological markers. This review critically evaluates current proteomic technologies and proteomics-derived biomarker candidates in breast cancer, distinguishes discovery-level findings from clinically validated evidence, and discusses the major methodological, regulatory, and implementation barriers that continue to limit translation into routine oncology practice. Greater emphasis on standardized workflows, targeted verification, multi-center validation, and clinically actionable biomarker panels will be essential for integrating proteomics into precision breast cancer care.

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