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Transcriptional plasticity promotes primary and acquired resistance to BET bromodomain inhibition

GSE63782 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 103 samples Submitted 2015/09/08 Platform GPL17021Platform GPL18480Platform GPL18460Platform GPL16791Platform GPL13112Platform GPL11154
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 103 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
Mus musculus (mouse)
Topic
Acute myeloid leukemiaThis series is part of our curated briefing on it.
Samples
103 samples: 103 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)
Following the discovery of BRD4 as a non-oncogene addiction target in acute myeloid leukemia (AML), BET inhibitors are being explored as promising therapeutic avenue in numerous cancers. While clinical trials have reported single-agent activity in advanced hematologic malignancies, mechanisms determining the response to BET inhibition remain poorly understood.
Read the full summary ↓
To identify factors involved in primary and acquired BET resistance in leukemia, we performed a chromatin-focused shRNAmir screen in a sensitive MLL/AF9; NrasG12D‑driven AML model, and investigated dynamic transcriptional profiles in sensitive and resistant murine and human leukemias. Our screen reveals that suppression of the PRC2 complex, contrary to effects in other contexts, promotes BET resistance in AML. PRC2 suppression does not directly affect the regulation of Brd4-dependent transcripts, but facilitates the remodeling of regulatory pathways that restore the transcription of key targets such as Myc. Similarly, while BET inhibition triggers acute MYC repression in human leukemias regardless of their sensitivity, resistant leukemias are uniformly characterized by their ability to rapidly restore MYC transcription. This process involves the activation and recruitment of WNT signaling components, which compensate for the loss of BRD4 and drive resistance in various cancer models. Dynamic ChIP- and STARR-seq enhancer profiles reveal that BET-resistant states are characterized by remodeled regulatory landscapes, involving the activation of a focal MYC enhancer that recruits WNT machinery in response to BET inhibition. Together, our results identify and validate WNT signaling as a driver and candidate biomarker of primary and acquired BET resistance in leukemia, and implicate the rewiring of transcriptional programs as an important mechanism promoting resistance to BET inhibitors and, potentially, other chromatin-targeted therapies.
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 GSE63782_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 PRJNA269063 and SRA study SRP050440. Searching any of these in the dataset finder brings you back here.

Study design
103 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 103 sample titles: 103 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

+ 63 more — browse all 103 samples with per-sample file links →

More Acute myeloid leukemia datasets

Search all RNA-seq 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.