Differential oestrogen receptor binding is associated with clinical outcome in breast cancer

By Rory Stark. Nature, 2012

1,986 citations recorded by the source corpus.

Oestrogen receptor-α (ER) is the defining and driving transcription factor in the majority of breast cancers and its target genes dictate cell growth and endocrine response, yet genomic understanding of ER function has been restricted to model systems. Here we map genome-wide ER-binding events, by chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq), in primary breast cancers from patients with different clinical outcomes and in distant ER-positive metastases. We find that drug-resistant cancers still recruit ER to the chromatin, but that ER binding is a dynamic process, with the acquisition of unique ER-binding regions in tumours from patients that are likely to relapse. The acquired ER regulatory regions associated with poor clinical outcome observed in primary tumours reveal gene signatures that predict clinical outcome in ER-positive disease exclusively. We find that the differential ER-binding programme observed in tumours from patients with poor outcome is not due to the selection of a rare subpopulation of cells, but is due to the FOXA1-mediated reprogramming of ER binding on a rapid timescale. The parallel redistribution of ER and FOXA1 binding events in drug-resistant cellular contexts is supported by histological co-expression of ER and FOXA1 in metastatic samples. By establishing transcription-factor mapping in primary tumour material, we show that there is plasticity in ER-binding capacity, with distinct combinations of cis-regulatory elements linked with the different clinical outcomes.

Subfields: Genetics, Molecular Biology, Pharmacology

View Paper: https://www.semanticscholar.org/paper/dd9a985d2dd868589d230572de5b1ae8906c1285

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csaw: a Bioconductor package for differential binding analysis of ChIP-seq data using sliding windows (2015, background): “An alternative strategy that is beginning to receive more attention is to identify quantitative changes in the binding profile between experimental conditions, i.e. to analyze differential binding (DB) (4–7).”
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 (2014, neutral): “, [39]; see also the DiffBind package [40, 41]), barcode-based assays (e.”