What question did this study set out to answer?

The aim is to explore the shift from traditional breast cancer diagnostics to multi-omic approaches for better patient outcomes.

April 7, 2026Open Access

The multi-omic transformation of breast cancer diagnostics: a comprehensive narrative of the transition from immunohistochemistry to liquid biopsy and next-generation sequencing

FMFarnam Gholipour MaralanUniversidad Complutense de Madrid

Key Points

The aim is to explore the shift from traditional breast cancer diagnostics to multi-omic approaches for better patient outcomes.
Review of existing literature on breast cancer diagnostic methodologies.
Analysis of multi-gene expression signatures for clinical decision-making.
Examination of the role of liquid biopsy and ctDNA in detecting molecular residual disease.
High-throughput genomic tools outperform traditional IHC methods for tumor characterization.
Multi-gene expression signatures show significant influence on treatment decisions.
Integration of AI in bioinformatics aids in analyzing complex genomic datasets.

Abstract

Abstract Background Breast cancer (BC) diagnostics are undergoing a transformative shift from traditional morphological assessment to a complex multi-omic characterization. While Immunohistochemistry (IHC) remains the clinical bedrock for subtyping, its inherent inability to account for intra-tumoral heterogeneity and the dynamic nature of clonal evolution has necessitated the integration of high-throughput genomic and transcriptomic tools. Main body This review examines the technological evolution from “gold standard” protein-based assays to precision oncology enabled by Next-Generation Sequencing (NGS) and liquid biopsy. We provide a rigorous analysis of multi-gene expression signatures (Oncotype DX, MammaPrint, Prosigna, EndoPredict) and their impact on clinical decision-making. Furthermore, we explore the clinical utility of circulating tumor DNA (ctDNA) for molecular residual disease (MRD) detection and the identification of acquired resistance mechanisms involving ESR1, PIK3CA, AKT1, PTEN and ERBB2 mutations. We further address the complex bioinformatics challenges, including variant interpretation, the implementation of Unique Molecular Identifiers (UMIs), and the integration of artificial intelligence in analyzing massive multi-omic datasets. Conclusion The integration of IHC with longitudinal genomic profiling is essential for the future of breast cancer management. This multi-modal approach ensures that therapeutic strategies evolve alongside the tumor's molecular landscape. Future clinical trials must focus on MRD-guided interventions to validate the clinical benefit of early genomic detection.

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