EstroGene database reveals diverse temporal, context-dependent and directional estrogen receptor regulomes in breast cancer.
Zheqi LiTianqin LiMegan E YatesYang WuAmanda FerberLyuqin ChenDaniel D BrownJason S CarrollMatthew J SikoraGeorge C TsengSteffi OesterreichAdrian V LeePublished in: bioRxiv : the preprint server for biology (2023)
As one of the most successful cancer therapeutic targets, estrogen receptor-α (ER/ESR1) has been extensively studied in decade-long. Sequencing technological advances have enabled genome-wide analysis of ER action. However, reproducibility is limited by different experimental design. Here, we established the EstroGene database through centralizing 246 experiments from 136 transcriptomic, cistromic and epigenetic datasets focusing on estradiol-treated ER activation across 19 breast cancer cell lines. We generated a user-friendly browser ( https://estrogene.org/ ) for data visualization and gene inquiry under user-defined experimental conditions and statistical thresholds. Notably, documentation-based meta-analysis 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. We defined temporal estrogen response metasignatures and showed the association with specific transcriptional factors, chromatin accessibility and ER heterogeneity. Unexpectedly, harmonizing 146 transcriptomic analyses uncovered a subset of E2-bidirectionally regulated genes, which linked to immune surveillance in the clinical setting. Furthermore, we defined context dependent E2 response programs in MCF7 and T47D cell lines, the two most frequently used models in the field. Collectively, the EstroGene database provides an informative resource to the cancer research community and reveals a diverse mode of ER signaling.
Keyphrases
- estrogen receptor
- single cell
- rna seq
- genome wide
- dna methylation
- high throughput
- systematic review
- electronic health record
- gene expression
- papillary thyroid
- transcription factor
- public health
- healthcare
- adverse drug
- mental health
- dna damage
- young adults
- machine learning
- big data
- randomized controlled trial
- squamous cell carcinoma
- childhood cancer
- circulating tumor cells
- bioinformatics analysis
- data analysis