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

## TL;DR

ER-low breast cancer is a distinct subtype with features similar to triple-negative breast cancer and may respond better to chemotherapy than hormone therapy.

## Contribution

ER-low breast cancer is identified as a unique entity with distinct clinical behavior and treatment responses.

## Key findings

- ER-low tumors share molecular and pathological features with triple-negative breast cancer.
- ER-low tumors show limited benefit from endocrine therapy but respond well to chemotherapy and chemo-immunotherapy.
- AI tools in digital pathology may improve ER quantification and patient stratification.

## Abstract

Estrogen receptor-low positive (ER-low) breast cancer, defined by 1–9% ER expression, is increasingly recognized as a distinct subtype with clinical behavior that differs from both classic ER-positive and ER-negative disease. Although historically managed as hormone-responsive, ER-low tumors often display molecular and pathological features closer to triple-negative breast cancer, including a higher grade, increased proliferation, and early relapse risk. These characteristics help explain the limited and inconsistent benefit of endocrine therapy observed in multiple studies. In contrast, ER-low tumors typically show strong responses to chemotherapy and encouraging results with neoadjuvant chemo-immunotherapy, aligning with treatment patterns used for triple-negative disease. Accurate determination of ER levels remains challenging due to technical and interpretive variability, which can influence therapeutic decisions. Emerging artificial intelligence tools in digital pathology may improve the precision of ER quantification and help identify which patients may still benefit from endocrine therapy. This review summarizes current evidence and highlights future research directions.

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.