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Related Concept Videos

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Optimization of a Multiplex RNA-based Expression Assay Using Breast Cancer Archival Material
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Emerging Breast Cancer Subpopulations: Functional Heterogeneity Beyond the Classical Subtypes.

Amalia Kotsifaki1, Georgia Kalouda1, Efthymios Karalexis1

  • 1Physiology Laboratory, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece.

International Journal of Molecular Sciences
|December 11, 2025
PubMed
Summary
This summary is machine-generated.

Breast cancer is more complex than previously thought, with new subtypes identified. These emerging breast cancer (BC) classifications offer better prognosis and guide targeted therapies for aggressive disease.

Keywords:
BRCAnessHER2-lowbreast cancer subtypesclaudin-lowemerging populationsfunctional heterogeneityintratumoral heterogeneityprecision oncologyprognostic biomarkerstumor microenvironment

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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.