Aromatase inhibitors (AI) are extensively used in the treatment of estrogen receptor-positive breast cancers, however resistance to AI treatment is commonly observed. Apart from Estrogen receptor (ERα) expression, no predictive biomarkers for response to AI treatment are clinically applied. Yet, since other therapeutic options exist in the clinic, such as tamoxifen, there is an urgent medical need for the development of treatment-selective biomarkers, enabling personalized endocrine treatment selection in breast cancer. In the described dataset, ERα chromatin binding and histone marks H3K4me3 and H3K27me3 were assessed in a genome-wide manner by Chromatin Immunoprecipitation (ChIP) combined with massive parallel sequencing (ChIP-seq). These datasets were used to develop a classifier to stratify breast cancer patients on outcome after AI treatment in the metastatic setting. Here we describe in detail the data and quality control metrics, as well as the clinical information associated with the study, published by Jansen et al. . The data is publicly available through the GEO database with accession number GSE40867.