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CEBPA repression by MECOM blocks differentiation to drive aggressive leukemias [ATAC-Seq]

GSE284553 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 22 samples Submitted 2025/09/02 Platform GPL21697Platform GPL24676
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 22 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
22 samples: 8 conditions, mostly in triplicateGroups 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
CEBPA repression by MECOM blocks differentiation to drive aggressive leukemiasBlood · 2025
Summary (written by the submitters)
Acute myeloid leukemias (AMLs) have an overall poor prognosis and many high-risk cases co-opt stem cell gene regulatory programs, yet the mechanisms through which this occurs remain poorly understood. Increased expression of the stem cell transcription factor, MECOM, underlies one key driver mechanism in largely incurable AMLs. How MECOM results in such aggressive AML phenotypes remains unknown.
Read the full summary ↓
To address existing experimental limitations, we engineered and applied targeted protein degradation with functional genomic readouts to demonstrate that MECOM promotes malignant stem cell-like states by directly repressing pro-differentiation gene regulatory programs. Remarkably and unexpectedly, a single node in this network, a MECOM-interacting regulatory element located 42 kb downstream of the myeloid differentiation regulator CEBPA is both necessary and sufficient for MECOM-driven leukemogenesis. Importantly, targeted activation of this regulatory element promotes differentiation of these aggressive AMLs and reduces leukemia burden in vivo, suggesting a broadly applicable differentiation-based approach for improving therapy.
Published in
CEBPA repression by MECOM blocks differentiation to drive aggressive leukemias
Fleming TJ, Antoszewski M, Lambo S et al. · Blood 2025 · PMID 40991835 · doi:10.1182/blood.2025028954
This dataset
Open GSE284553 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 GSE284553_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 PRJNA1199523 and SRA study SRP552096. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40991835) ↗
Study design
8 conditions, mostly in triplicate
MUTZ-3 dTAGV-1 6hr ×3 MUTZ-3 DMSO 24hr ×3 HNT-34 dTAGV-1 6hr ×3 HNT-34 DMSO 6hr ×3 OCI-AML4 dTAGV-1 6hr ×3 OCI-AML4 DMSO 6hr ×3 UCSD-AML1 dTAGV-1 6hr ×2 UCSD-AML1 DMSO 6hr ×2

Supports a between-group comparison across 22 samples.

How we read this: 8 replicated groups read from 22 sample titles; they account for 22 of them. 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
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