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Updated: Jun 18, 2026
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Integration of multi-omics for precision therapy in breast cancer
Pai-Sheng Chen1, Chiao Lo2, Da-Liang Ou3
1Institute of Basic Medical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan; Department of Medical Laboratory Science and Biotechnology, College of Medicine, National Cheng Kung University, Tainan, Taiwan; Breast Medical Center, National Cheng Kung University Hospital, Tainan, Taiwan; Research Center for Medical Laboratory Science and Biotechnology, National Cheng Kung University, Tainan, Taiwan.
Abstract:
Breast cancer therapy is limited by heterogeneous tumor states and adaptive resistance that may be missed by single-layer biomarkers. This review focuses on the clinically actionable integration of multi-omics in breast cancer, emphasizing therapeutic stratification, response prediction, resistance monitoring, and implementation readiness. We discuss how genomic, epigenomic, transcriptomic, proteomic, metabolomic, spatial, and liquid-biopsy data can be used jointly to infer functional tumor states; how proteogenomics and surfaceomics refine pathway and antibody-drug conjugate (ADC) target interpretation; and how cell-free DNA (cfDNA) methylation, fragmentomics, foundation models, and digital twins could support longitudinal decision-making. We also highlight the key constraints for translation, including assay standardization, external validation, clinical utility, economic feasibility, and the cautious interpretation of fragmentomic and artificial intelligence (AI)-derived predictions.
Insights
Integrating multi-omics data in breast cancer therapy improves treatment by revealing complex tumor states and resistance. This approach enhances therapeutic stratification, response prediction, and monitoring for better patient outcomes.
Area of Science:
- Oncology
- Genomics
- Biomarker Discovery
Background:
- Breast cancer treatment faces challenges due to tumor heterogeneity and adaptive resistance, often undetected by single biomarkers.
- Current diagnostic methods may not fully capture the dynamic nature of tumors, limiting therapeutic precision.
Purpose of the Study:
- To review the clinical integration of multi-omics data for actionable insights in breast cancer therapy.
- To emphasize applications in therapeutic stratification, response prediction, resistance monitoring, and implementation readiness.
Main Methods:
- Integration of diverse omics data: genomic, epigenomic, transcriptomic, proteomic, metabolomic, spatial, and liquid biopsies.
- Utilizing advanced techniques like proteogenomics, surfaceomics, cell-free DNA (cfDNA) analysis, fragmentomics, foundation models, and digital twins.
Main Results:
- Multi-omics data jointly infer functional tumor states, refining pathway and antibody-drug conjugate (ADC) target interpretation.
- Longitudinal decision-making can be supported by cfDNA methylation, fragmentomics, foundation models, and digital twins.
Conclusions:
- Multi-omics integration offers a powerful strategy to overcome limitations of single-layer biomarkers in breast cancer.
- Key translational constraints include assay standardization, validation, clinical utility, economic feasibility, and cautious interpretation of novel predictive methods.
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