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PU.1-Activated Genomic Regions Define Low-risk MDS Subsets Characterized by Immune Dysregulation and Disease Progression [ChIP-seq]

GSE297914 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 112 samples Submitted 2026/01/29 Platform GPL24676
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 112 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
112 samples: 5 conditions, mostly with about 42 replicates eachGroups 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
PU.1-activated genomic regions define low-risk MDS subsets characterized by immune dysregulation and disease progressionBlood · 2026
Summary (written by the submitters)
Myelodysplastic syndromes (MDS) are heterogeneous myeloid neoplasms with an increased risk of progression to secondary acute myeloid leukemia (sAML). This study investigates the genomic correlates of disease progression in MDS by profiling active genomic regulatory regions and their transcriptional impact through H3K27ac ChIP-seq and RNA-seq analysis on CD34+ bone marrow progenitors cells isolated from a prospective cohort of 86 and 357 patients, respectively.
Read the full summary ↓
Our analysis revealed distinct patterns of genomic region activation and transcriptional regulation across different disease stages (low-risk MDS, high-risk MDS and sAML). Unexpectedly, unsupervised clustering revealed a subset of low-risk MDS patients displaying regulatory and transcriptional profiles similar to those of high-risk MDS and sAML, highlighting early molecular events that may predispose patients to disease progression. This subset is characterized by PU.1 genomic occupancy in regions linked to immune and inflammatory responses, increased T-cell and NK activation, and a higher frequency of SRSF2 mutations. Clinically, patients in this group exhibit greater susceptibility to infections and cardiovascular events, along with an elevated risk of disease progression, resulting in a significantly reduced overall survival. Functional studies demonstrate that PU.1 inhibition suppresses MDS cell proliferation and clonogenicity, as impaired PU.1 binding inhibits the activation of key transcriptional programs involved in disease advancement. Collectively, these findings identify epigenetic factors that predispose low-risk MDS patients to progression into high-risk MDS and, ultimately, sAML. Moreover, they provide proof of concept for targeting PU.1 as a potential strategy to prevent disease progression in low-risk MDS.
Published in
PU.1-activated genomic regions define low-risk MDS subsets characterized by immune dysregulation and disease progression
Vallelonga V, Gandolfi F, Zampini M et al. · Blood 2026 · PMID 41538708 · doi:10.1182/blood.2025030760
This dataset
Open GSE297914 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 GSE297914_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 PRJNA1266723. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41538708) ↗
Study design
5 conditions, mostly with about 42 replicates each
H3K27Ac_LR_patient_sample ×42 H3K27Ac_AML_patient_sample ×27 H3K27Ac_HR_patient_sample ×17 Pu1_LR_patient_sample ×15 Pu1_AML_patient_sample ×11

Supports a between-group comparison across 112 samples.

How we read this: 5 replicated groups read from 112 sample titles; they account for 112 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

+ 72 more — browse all 112 samples with per-sample file links →

Datasets on a similar topic

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