The EstroGene Database Reveals Diverse Temporal, Context-Dependent, and Bidirectional Estrogen Receptor Regulomes in Breast Cancer

Abstract

As one of the most successful cancer therapeutic targets, estrogen receptor-α (ER/ESR1) has been extensively studied over the past few decades. Sequencing technological advances have enabled genome-wide analysis of ER action. However, comparison of individual studies is limited by different experimental designs, and few meta-analyses are available. Here, we established the EstroGene database through unified processing of data from 246 experiments including 136 transcriptomic, cistromic, and epigenetic datasets focusing on estradiol (E2)-triggered ER activation across 19 breast cancer cell lines. A user-friendly browser (https://estrogene.org/) was generated for multi-omic data visualization involving gene inquiry under user-defined experimental conditions and statistical thresholds. Notably, annotation of metadata associated with public datasets revealed a considerable lack of experimental details. Comparison of independent RNA-seq or ER ChIP-seq data with the same design showed large variability and only strong effects could be consistently detected. Temporal estrogen response metasignatures were defined, and the association of E2 response rate with temporal transcriptional factors, chromatin accessibility, and heterogeneity of ER expression was evaluated. Unexpectedly, harmonizing 146 E2-induced transcriptomic datasets uncovered a subset of genes harboring bidirectional E2-regulation, which was linked to unique transcriptional factors and highly associated with immune surveillance in the clinical setting. Furthermore, the context dependent E2 response programs were characterized in MCF7 and T47D cell lines, the two most frequently used models in the EstroGene database. Collectively, the EstroGene database provides an informative and practical resource to the cancer research community to uniformly evaluate key reproducible features of ER regulomes and unravels modes of ER signaling.

ONECUT2 is a druggable driver of luminal to basal breast cancer plasticity

“…A similar result was observed when analyzing another ER activity signature obtained after 6 h treatment with E2 [39] (120/226, P = 8.13e-29 for MCF-7; 117/227, P = 1.63e-24 for BT-474) (Fig. 3C) and temporal estrogen response metasignatures included in The EstroGene Database [40] (Supplementary Fig. 3C).…”

Mutual information for detecting multi-class biomarkers when integrating multiple bulk or single-cell transcriptomic studies

“…In this section, we first applied MICA for simulations to evaluate the type I error and power of multiclass biomarker detection. The method is then applied to two bulk transcriptome applications: mouse metabolism-related studies [Lu et al, 2010], and estrogen treatment expression profiles [Li et al, 2023].…”

“…The EstroGene project [Li et al, 2023] focuses on improving the understanding of the estrogen receptor and its role in the development of breast cancer. It aims to document and integrate the publicly available estrogen-related datasets, including RNA-Seq, microarray, ChIP-Seq, ATAC-Seq, DNase-Seq, ChIA-PET, Hi-C, GRO-Seq and others, to establish a comprehensive database that allows for customized data search and visualization.…”