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Differential gene expression in leukemia is driven by enhancer heterogeneity [TOPmentation]

GSE283173 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 80 samples Submitted 2025/08/15 Platform GPL18573
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 80 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
80 samples: 66 distinct titles, 12 of them repeatedGroups 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
Enhancer heterogeneity in acute lymphoblastic leukemia drives differential gene expression in patientsBlood · 2025
Summary (written by the submitters)
Genetic alterations alone cannot account for the diverse phenotypes of cancer cells. Even cancers with the same driver mutation show significant transcriptional heterogeneity and varied responses to therapy. However, the mechanisms underpinning this heterogeneity remain under-explored. Here, we find that novel enhancer usage is a common feature in acute lymphoblastic leukemia (ALL).
Read the full summary ↓
In particular, KMT2A::AFF1 ALL, an aggressive leukemia subtype with a poor prognosis and a low mutational burden, exhibits substantial transcriptional heterogeneity between individuals. Using single cell multiome analysis and extensive chromatin profiling, we reveal that much transcriptional heterogeneity in KMT2A::AFF1 ALL is driven by novel enhancer usage. Using high resolution Micro Capture-C in primary patient samples, we also identify patient-specific enhancer activity at key oncogenes such as MEIS1 and RUNX2, driving high levels of expression of both oncogenes in a patient-specific manner. Overall, our data show that enhancer heterogeneity is highly prevalent in KMT2A::AFF1 ALL and may also represent a mechanism for driving transcriptional heterogeneity in cancer more generally.
Published in
Enhancer heterogeneity in acute lymphoblastic leukemia drives differential gene expression in patients
Smith AL, Denny N, Chahrour C et al. · Blood 2025 · PMID 40729681 · doi:10.1182/blood.2024028019
This dataset
Open GSE283173 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 GSE283173_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 PRJNA1192277 and SRA study SRP548413. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40729681) ↗
Study design
66 distinct titles, 12 of them repeated
iALL3 H3K27ac ×3 iALL5 H3K27ac ×3 chALL1 H3K4me ×2 chALL2 H3K4me ×2 chALL3 H3K4me ×2 chALL4 H3K4me ×2 chALL7 H3K4me ×2 iALL2 H3K4me ×2 +4 more

How we read this: Read from 80 sample titles: 66 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

+ 40 more — browse all 80 samples with per-sample file links →

Datasets on a similar topic

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