Abstract
Breast cancer is the most commonly diagnosed cancer worldwide, with an estimated 15% of people diagnosed not surviving more than 10 years. 75% of all breast cancer is oestrogen receptor positive (ER+), which is commonly treated with anti-endocrine therapies such as aromatase inhibitors, however, a high proportion of ER+ breast cancers eventually develop resistance to these treatments. Thus, there is a need for better understanding these treatment responses and the improvement of treatments.
In recent years, new drugs known as immune-checkpoint inhibitors (ICIs) have emerged, and specific ICIs like Anti-PD-1 have been successfully used to treat some oestrogen receptor negative (ER-) breast cancers but have had little success in treating those that are ER+. Immune cells within the tumour microenvironment (TME) are known to play a large role in response to ICIs. Therefore, improving understanding of the TME of ER+ breast cancer is a critical step in understanding how treatments like ICIs could be used to treat it.
Eosinophils are a type of immune cell that have been found to play important pro and anti-tumorigenic roles in several cancers. Eosinophils are known to recruit other immune cells like CD8+ T cells, which show promise as a predictive biomarker for Anti-PD-1 response. They have also more recently been shown to do this in a type of ER- breast cancer, known as triple-negative breast cancer (TNBC), that have been treated with ICIs. However, eosinophils’ role in treatment responses has not yet been well studied in the ER+ breast cancer TME.
This study aimed to investigate the role of tumour infiltrating eosinophils in ER+ breast cancer responses to aromatase inhibitor and anti-PD-1 treatment, and whether their presence could potentially be used as a biomarker to select ER+ patients likely to respond to Anti-PD-1 therapy. This was done using an eosinophil gene expression signature and a CD8+ T cell marker gene. This was investigated using an eosinophil gene expression signature in silico, using three human breast cancer datasets, and by analysing tumours from a previous in vivo experiment in a clinically relevant mouse model of ER+ breast cancer treated with an aromatase inhibitor and Anti-PD-1. In silico, I found that during aromatase inhibitor treatment of ER+ breast cancer, CCR3 expression changed differently during treatment in patients based on response and the duration of treatment, however, it is unknown if this expression occurred in tumour or immune cells. During Anti-PD-1 treatment of breast cancer, IL5RA expression also increased in patients with a surrogate measure of response. No significant expression differences were found in the in vivo experiment, though trends in CCR3 and CD8A expression support CCR3’s and CD8+ T cells potential involvement in aromatase inhibitor and Anti-PD-1 response. Collectively, these results suggest a potential role for CCR3 and IL5RA in ER+ breast cancers response to aromatase inhibitors and breast cancers response to anti-PD-1 treatment respectively. Expression of these genes should therefore be investigated further, as they have the potential to be predictive biomarkers for ER+ breast cancers response to these treatments.