A hidden Markov model to identify combinatorial epigenetic regulation patterns for estrogen receptor α target genes

Motivation: Many studies have shown that epigenetic changes, such as altered DNA methylation and histone modifications, are linked to estrogen receptor α (ERα)-positive tumors and disease prognoses. Several recent studies have applied high-throughput technologies such as ChIP-seq and MBD-seq to interrogate the altered architectures of ERα regulation in tamoxifen (Tam)-resistant breast cancer cells. However, the details of combinatorial epigenetic regulation of ERα target genes in breast cancers with acquired Tam resistance have not yet been fully examined.Results: We developed a computational approach to identify and analyze epigenetic patterns associated with Tam resistance in the MCF7-T cell line as opposed to the Tam-sensitive MCF7 cell line, with the goal of understanding the underlying mechanisms of epigenetic regulatory influence on resistance to Tam treatment in breast cancer. In this study, we used ChIP-seq of ERα, RNA polymerase II, three histone modifications and MBD-seq data of DNA methylation in MCF7 and MCF7-T cells to train hidden Markov models (HMMs). We applied the Bayesian information criterion to determine that a 20-state HMM was best, which was reduced to a 14-state HMM with a Bayesian information criterion score of 1.21291 × 10⁷. We further identified four classes of biologically meaningful states in this breast cancer cell model system, and a set of ERα combinatorial epigenetic regulated target genes. The correlated gene expression level and gene ontology analyses showed that different gene ontology terms were enriched with Tam-resistant versus sensitive breast cancer cells. Our study illustrates the applicability of HMM-based analysis of genome-wide high-throughput genomic data to study epigenetic influences on E2/ERα regulation in breast cancer.