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Selective Inhibition of Tumor Oncogenes by Disruption of Super-Enhancers [ChIP-seq]

GSE42355 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 13 samples Submitted 2013/01/02 Platform GPL11154
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 13 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
13 samples: 13 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.
Summary (written by the submitters)
Chromatin regulators have become highly attractive targets for cancer therapy, yet many of these regulators are expressed in a broad range of healthy cells and contribute generally to gene expression. An important conundrum has thus emerged: how can inhibition of a general regulator of gene expression produce selective effects at specific oncogenes?
Read the full summary ↓
Here we investigate how inhibition of the transcriptional coactivator BRD4 (Bromodomain containing 4) leads to selective inhibition of disease-critical oncogenes in a highly malignant blood cancer, multiple myeloma (MM). We found that BRD4 generally occupies the promoter elements of active genes together with the Mediator coactivator, but remarkably high levels of these two coactivator proteins were associated with a small set of exceptionally large enhancers. These super-enhancers are associated with genes that feature prominently in MM biology, including the MYC oncogene. Treatment of MM tumor cells with the BET-bromodomain inhibitor JQ1 led to preferential loss of BRD4 at super-enhancers and consequent transcription elongation defects that preferentially impact genes with super-enhancers, including the c-MYC oncogene. Super-enhancers were found at key oncogenic drivers in many other tumor cells. Thus, super-enhancers can regulate oncogenic drivers in tumor cells, which in some cells can be preferentially disrupted by BRD4 inhibition, which in turn contributes to the selective transcriptional effects observed at these oncogenes. These observations have implications for the discovery of novel cancer therapeutics directed at components of super-enhancers in diverse tumor types.
This dataset
Open GSE42355 in finder → Find more human ChIP / ATAC / CUT&Tag datasets →
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.

Series matrix ↓
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 GSE42355_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
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.

Raw reads on ENA ↗

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 PRJNA181142 and SRA study SRP017210. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 23582323) ↗
Study design
13 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 13 sample titles: 13 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

Search all human ChIP / ATAC / CUT&Tag datasets in GEO →

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.