Abstract P6-04-24: Transcription factor dynamics in endocrine therapy resistance models using a novel chromatin landscape analysis algorithm based on ATAC-Seq

Abstract

Abstract Endocrine therapy resistance is one of the major challenges in breast cancer therapy today. As the causes are not well understood, therapeutic options are limited. To investigate the molecular mechanisms of endocrine therapy resistance, we established in vitro Long-Term Estrogen Deprivation (LTED) models using multiple human ER-positive breast cancer cell lines. These cells showed differential time-dependent patterns of ESR1 expression with upregulation in some cell lines and downregulation in others. Although all cells continued to grow, none acquired mutations in the ESR1 ligand binding domain, reported to be responsible for endocrine therapy resistance. Current data using gene expression and DNA modification profiles yield limited information for molecular mechanisms of resistance and potential for developing novel therapies. The chromatin landscape is a potential identifier of disease state, and can provide specific information on transcriptional regulation by enhancers. Thus, changes in chromatin open/closed sites could provide important information to predict disease progression and can be used to identify potential therapeutic targets. We analyzed the time-course of chromatin landscape transitions during acquisition of endocrine resistance by the Assay for Transposase Accessible Chromatin (ATAC-seq), and combined these studies with RNA-seq and genomic DNA sequence analysis. ATAC-seq permits the analysis of all sites potentially accessible to the transcriptional machinery. Furthermore, digital footprinting of protected transcription factor motifs within the accessible regions can evaluate the occupancy of hundreds of different transcription factors at one time. We performed bioinformatic analysis of open and closed chromatin sites and selected relevant enhancer signatures. Because transcription factors play a primary role in gene expression and in cell-fate changes, including response to therapies, we derived digital footprints from transcription factor occupancy to correlate this information with phenotypes of LTED cells. Comparing changes in the chromatin landscape with RNA-seq, we created a high throughput algorithm that integrates the information from open/closed chromatin sites, transcription factors occupancy, and gene expression. We found key transcription factors for acquisition of LTED cells; Myc-Max is associated with ESR1 overexpressed LTEDs, whereas Oct1 and Hox genes are associated with LTEDs with absence of ESR1 expression. These methods establish novel mechanisms of endocrine therapy resistance in breast cancer that can also be applied to other tumor types. Our studies provide a rationale for using chromatin landscape and transcription factor networks to expand the range of translational research in the search for novel therapies for breast cancer. These studies will potentially have high impact on development of important novel therapeutic approaches in cancer and many other diseases. Citation Format: Saori Fujiwara. Transcription factor dynamics in endocrine therapy resistance models using a novel chromatin landscape analysis algorithm based on ATAC-Seq [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P6-04-24.