Molecular characterisation of a clinical model of estrogen receptor-positive breast cancer dormancy
Endocrine therapy has clearly improved outcomes for estrogen receptor alpha positive (ER+) breast cancer patients, however the cumulative incidence of recurrence and death continues at a steady rate . The majority of ER+ breast tumours treated with neoadjuvant letrozole in respond quickly and are generally excised after three months. A minority of tumours maintain a stable size by becoming dormant, these tumours continue to receive extended letrozole treatment and therefore represent the best currently available clinical model to investigate dormancy. We have previously performed a number of dynamic molecular studies of cancer treatment by taking pre- and post-treatment tumour biopsies utilising the ""window of opportunity"". In this study, for the first time, the dormant cancer cells from breast tumour biopsies that have received extended (1-3 years) neoadjuvant endocrine therapy will be studied. This study aims to characterise the ER+ breast cancer dormancy and letrozole resistance using expression profiling technologies of this unique series of breast tumour biopsies. The genome- and proteome-wide expression data will be analysed and compared with data for the same patients at diagnosis, at two weeks and at three months following treatment and with the clinical outcomes. Our study will be the first to characterise extended growth suppression in letrozole-treated dormant-state breast cancer. This knowledge will be very valuable to extend ER+ breast cancer patients' survival and quality of life by preventing metastasis and also contribute to the economy and society by introducing a individualized therapy that is tailored in accordance with cancer patients’ expression profiles. This multidisciplinary project combines both clinical and academic aspects along with exposure to the non-academic sector, in order to provide the fellow with great competence in cancer genomics through training in advanced integrative bioinformatics analysis of high-throughput data.