Estrogen Receptor-Low Positive (ER-Low) Breast Cancer: A Unique Clinical and Pathological Entity

ER-low breast cancer (1-9% ER expression) represents a biologically and clinically distinct entity at the interface between ER-positive and ER-negative disease. Although traditionally managed as hormone receptor-positive, mounting evidence indicates that ER-low tumors share molecular signatures, aggressive behavior, and chemotherapeutic responsiveness with triple-negative breast cancer. Accurate ER assessment is hindered by methodological variability and interpretative challenges, leading to potential misclassification and suboptimal treatment choices. While the benefit of endocrine therapy remains uncertain, ER-low tumors consistently show sensitivity to chemotherapy and promising responses to neoadjuvant chemo-immunotherapy, paralleling outcomes observed in triple-negative breast cancer cohorts. Emerging artificial intelligence tools, including digital pathology and multimodal deep learning, may enhance ER quantification, reduce observer variability, and enable more precise patient stratification. This review synthesizes current pathological and clinical insights into ER-low breast cancer and highlights evolving therapeutic strategies, with a forward-looking perspective on AI-driven approaches to optimize personalized treatment for this challenging subtype.

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Publication Details

Journal
Current Oncology
Published
2026-02-18
DOI
https://doi.org/10.3390/curroncol33020122
Primary Topic
Breast Cancer Treatment Studies
Type
article
Field-Weighted Citation Impact
0.00

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article

Estrogen Receptor-Low Positive (ER-Low) Breast Cancer: A Unique Clinical and Pathological Entity

Andrea Pretta, Eleonora Lai, Ekta Tiwari, Massimo Dessena et al.
Current Oncology
Breast Cancer Treatment Studies
article

Estrogen Receptor-Low Positive (ER-Low) Breast Cancer: A Unique Clinical and Pathological Entity

Andrea Pretta, Eleonora Lai, Ekta Tiwari, Massimo Dessena, Gavino Faa, Simona Graziano, Pina Ziranu, Claudia Codipietro, Giorgio Saba, C. Solinas, Ma Dessì, Francesco Loi, Ferdinando Coghe, Mario Scartozzi, Luca Saba, Jasjit S. Suri, Laura Ottelio
article en

Abstract

ER-low breast cancer (1-9% ER expression) represents a biologically and clinically distinct entity at the interface between ER-positive and ER-negative disease. Although traditionally managed as hormone receptor-positive, mounting evidence indicates that ER-low tumors share molecular signatures, aggressive behavior, and chemotherapeutic responsiveness with triple-negative breast cancer. Accurate ER assessment is hindered by methodological variability and interpretative challenges, leading to potential misclassification and suboptimal treatment choices. While the benefit of endocrine therapy remains uncertain, ER-low tumors consistently show sensitivity to chemotherapy and promising responses to neoadjuvant chemo-immunotherapy, paralleling outcomes observed in triple-negative breast cancer cohorts. Emerging artificial intelligence tools, including digital pathology and multimodal deep learning, may enhance ER quantification, reduce observer variability, and enable more precise patient stratification. This review synthesizes current pathological and clinical insights into ER-low breast cancer and highlights evolving therapeutic strategies, with a forward-looking perspective on AI-driven approaches to optimize personalized treatment for this challenging subtype.

Current OncologyVol. 33(2)
University of Cagliari (IT), Minneapolis Business College (US), Roshydromet (RU), Azienda Ospedaliero-Universitaria Cagliari (IT), Temple University (US)
Ministero della Salute
Good health and well-being
Openalex Percentile: Top 50%
Breast Cancer Treatment Studies
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