Around 80% of breast cancer patients have tumors expressing estrogen receptor alpha (ER) and are offered endocrine treatment for 5-10 years, aiming to either regulate the receptor or removing its ligand, estrogen. However, approximately 30% of patients with ER positive primary tumors will later develop endocrine resistant recurrences despite adjuvant therapy. In the majority of recurrent tumors, ER expression is retained; the tumor has now progressed into a hormone refractory state.
Suggested mechanisms involve different aspects of ER signaling through genomic variations, unbalanced receptor signaling, and co-regulator activity and also tumor morphology in resistant tumors, though the resistance mechanism remains elusive. This calls for a broader understanding of the development of endocrine resistance in order to develop novel tools for therapy prediction and treatment alternatives.
The project “Endoresist” aims to improve our understanding of the underlying mechanism behind endocrine resistance and to identify biomarkers for an individualized approach to cancer therapy.
Supporting the overall aim, the different aspects of the project involve investigating transcriptional and genomic profiles of patients resistant to versus responding to endocrine therapy, and during tumor progression, as well as histologic patterns through computerized image analysis.
A cohort of ER positive, HER2 negative breast cancer patients treated with endocrine therapy and with recurrent ER positive tumors have been selected from the pathology registry database. FFPE tumor material has undergone RNA and DNA extraction and subsequent RNA gene expression analysis and DNA panel sequencing with ongoing bioinformatics analyses. Furthermore, machine learning models will be trained to distinguish endocrine resistant tumors based on digitized whole slide images.