Emerging Breast Cancer Subpopulations: Functional Heterogeneity Beyond the Classical Subtypes

Area of Science:

• Oncology
• Genomics
• Biomarkers

Background:

• Breast cancer (BC) is recognized as a heterogeneous disease, exceeding traditional classifications like luminal A/B, HER2-enriched, and triple-negative (TNBC).
• Emerging subpopulations such as HER2-low, claudin-low, BRCA-deficient (BRCAness), and refined TNBC subsets (e.g., luminal AR, basal-like immune variants) present new complexities.
• These novel classifications offer enhanced prognostic insight and therapeutic avenues.

Purpose of the Study:

• To comprehensively review emerging breast cancer subtypes beyond classical taxonomies.
• To integrate evidence from multi-omic and liquid biopsy biomarkers in defining these subgroups.
• To highlight the predictive value of these biomarkers for targeted therapies and the role of the tumor microenvironment (TME).

Main Methods:

• Integration of evidence from genomic, epigenetic, and proteomic profiling.
• Analysis of immune-related biomarkers and liquid biopsy data.
• Review of studies investigating the tumor microenvironment (TME) and intratumoral heterogeneity.

Main Results:

• Novel breast cancer classifications, including HER2-low, claudin-low, BRCAness, and refined TNBC subsets, provide greater biological resolution.
• These emerging subtypes are associated with distinct prognoses and predict responses to targeted therapies like antibody-drug conjugates, PARP inhibitors, and immune checkpoint inhibitors.
• The tumor microenvironment (TME) and intratumoral heterogeneity significantly influence the biology and clinical behavior of these emerging breast cancer entities.

Conclusions:

• Recognition of emerging breast cancer subtypes represents a paradigm shift towards dynamic, biomarker-driven precision oncology.
• These clinically actionable groups enable better patient stratification and refined prognosis for aggressive breast cancer.
• A move from static receptor-based models to dynamic, biomarker-driven frameworks is crucial for advancing breast cancer treatment.