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ESR1 fusions invoke breast cancer subtype-dependent enrichment of ligand independent oncogenic signatures and phenotypes [ChIP-seq]

GSE266407 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 14 samples Submitted 2024/05/02 Platform GPL30173
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 14 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Homo sapiens (human)
Samples
14 samples: 14 samples, each with a distinct title — no replicated groupsGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
ESR1 Fusions Invoke Breast Cancer Subtype-Dependent Enrichment of Ligand-Independent Oncogenic Signatures and PhenotypesEndocrinology · 2024
Summary (written by the submitters)
Breast cancer is a leading cause of female mortality and despite advancements in personalized therapeutics, metastatic disease largely remains incurable due to drug resistance. The estrogen receptor (ER, ESR1) is expressed in two-thirds of all breast cancer, and under endocrine stress, somatic ESR1 mutations arise in ~30% of cases that result in endocrine resistance.
Read the full summary ↓
We and others reported ESR1 fusions as a mechanism of ER mediated endocrine resistance. ER fusions, which retain the AF1 and DNA binding domains, harbor ESR1 exons 1-6 fused to an in-frame gene partner resulting in loss of the ER ligand binding domain (LBD). We demonstrate that in a no-special type (invasive ductal carcinoma (IDC)- NST) and an invasive lobular carcinoma (ILC) cell line, ER fusions exhibit robust hyperactivation of canonical ER signaling pathways independent of estradiol or anti-endocrine therapies. We employ cell line models stably overexpressing ER fusions with concurrent endogenous ER knockdown to minimize endogenous ER influence. Cell lines exhibited shared transcriptomic enrichment in pathways known to be drivers of metastatic disease, notably MYC signaling. Cells expressing the 3’ fusion partners SOX9 and YAP1 consistently demonstrated enhanced growth and cell survival. ILC cells expressing the DAB2 fusion led to enhanced growth, survival, and migration; phenotypes not appreciated in the IDC- NST DAB2 model. Herein, we report that cell line activity is subtype-, fusion-, and assay-specific suggesting that LBD loss, the fusion partner, and the cellular landscape all influence fusion activities. Therefore, it will be critical to assess fusion frequency, in the context of the tumor clinicopathology.
Published in
ESR1 Fusions Invoke Breast Cancer Subtype-Dependent Enrichment of Ligand-Independent Oncogenic Signatures and Phenotypes
Yates ME, Waltermire H, Mori K et al. · Endocrinology 2024 · PMID 39207954 · doi:10.1210/endocr/bqae111
This dataset
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE266407_RAW.tar (it can run to tens of gigabytes).

3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

What the files usually look like: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1106990 and SRA study SRP505271. Searching any of these in the dataset finder brings you back here.

Study design
14 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 14 sample titles: 14 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
Datasets on a similar topic

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