Abstract
Background: Breast cancer (BC) with increased expression of human epidermal growth factor receptor 2 with tyrosine kinase activity (HER2+) is a clinically and biologically heterogeneous disease. In terms of gene expression, there are four major molecular subtypes - Luminal A, Luminal B, HER2-enriched (HER2-E), and Basal-like.
The most common subtype is HER2-E (50- 60%). In hormone-dependent (HR+) HER2-positive tumors, the subgroup HER2-E represents 40- 50% of cases; others are luminal A and B subtypes.
Purpose: The aim of this review is to provide information on the significance of the distribution of HER2-positive tumors according to subtype, which is considered a predictive parameter for guiding treatment decisions. For example, HER2-E subtype is characterized by a higher probability of achieving complete pathological remission when treated with chemotherapy and antiHER2 therapy, and it is thought that it could be treated using a dual HER2 blockade without chemotherapy.
Currently, triple-positive tumors, a specific subtype of breast cancer characterized by HER2+ and HR+, are more often subjects of interest. Their unique biological properties are due to complex interactions between HER2 and estrogen receptor (ER) signalling, which result in lower effectiveness of endocrine therapy in these patients than in HR+ and HER2-negative patients and, at the same time, the ER positivity in HER2+ tumors can result in resistance to antiHER2 therapy.
This type of BC is a non-homogeneous group where the impacts of HER2 positivity on tumor malignant behavior and activity of the estrogen-driven signaling pathway are inconsistent. Current studies focus on testing new treatments such as dual HER2 blocking or immunotherapy, in combination with antiHER2 targeted therapy with fulvestrant, aromatase inhibitors, cyclin dependent kinase 4/ 6 inhibitors, or inhibitors of the PI3K (phosphatidylinositol-3-kinase) pathway.
Conclusion: The distribution of HER2+ BC according to individual subtype provides information that can contribute to achieving more accurate decisions about the most appropriate therapy.