title: Accurate prediction of response to endocrine therapy in breast cancer patients: current and future biomarkers.
abstract: Approximately 70% of patients have breast cancers that are oestrogen receptor alpha positive (ER+) and are therefore candidates for endocrine treatment. Many of these patients relapse in the years during or following completion of adjuvant endocrine therapy. Thus, many ER+ cancers have primary resistance or develop resistance to endocrine therapy during treatment. Recent improvements in our understanding of how tumours evolve during treatment with endocrine agents have identified both changes in gene expression and mutational profiles, in the primary cancer as well as in circulating tumour cells. Analysing these changes has the potential to improve the prediction of which specific patients will respond to endocrine treatment. Serially profiled biopsies during treatment in the neoadjuvant setting offer promise for accurate and early prediction of response to both current and novel drugs and allow investigation of mechanisms of resistance. In addition, recent advances in monitoring tumour evolution through non-invasive (liquid) sampling of circulating tumour cells and cell-free tumour DNA may provide a method to detect resistant clones and allow implementation of personalized treatments for metastatic breast cancer patients. This review summarises current and future biomarkers and signatures for predicting response to endocrine treatment, and discusses the potential for using approved drugs and novel agents to improve outcomes. Increased prediction accuracy is likely to require sequential sampling, utilising preoperative or neoadjuvant treatment and/or liquid biopsies and an improved understanding of both the dynamics and heterogeneity of breast cancer.
authors.affiliation: Applied Bioinformatics of Cancer, Edinburgh Cancer Research Centre, Institute of Genetics and Molecular Medicine, Edinburgh, EH4 2XR, UK.|Department of Pharmacology, Faculty of Pharmacy, Ege University, Izmir, 35050, Turkey.
authors.forename: Cigdem
authors.initials: C
authors.lastname: Selli
authors.affiliation: Edinburgh Breast Unit, Western General Hospital, Edinburgh, EH4 2XR, UK.
authors.forename: J Michael
authors.initials: JM
authors.lastname: Dixon
authors.affiliation: Applied Bioinformatics of Cancer, Edinburgh Cancer Research Centre, Institute of Genetics and Molecular Medicine, Edinburgh, EH4 2XR, UK. andrew.sims@ed.ac.uk.
authors.forename: Andrew H
authors.initials: AH
authors.lastname: Sims
mesh_terms: D018931:Antineoplastic Agents, Hormonal / Q000627:therapeutic use*; D001943:Breast Neoplasms / Q000188:drug therapy* / Q000378:metabolism / Q000473:pathology; D059186:Chemoradiotherapy, Adjuvant; D017024:Chemotherapy, Adjuvant; D019008:Drug Resistance, Neoplasm; D005260:Female; D006801:Humans; D058990:Molecular Targeted Therapy; D020360:Neoadjuvant Therapy; D011379:Prognosis; D011960:Receptors, Estrogen / Q000378:metabolism; D016896:Treatment Outcome
chemical_list: D018931:Antineoplastic Agents, Hormonal; D011960:Receptors, Estrogen
keywords: Endocrine therapy; Liquid biopsy; Response prediction; Sequential sampling