GEO series
Analysis of chromatin accessibility relative to epigenetic features of azacitidine response in high-risk myelodysplastic syndrome
GSE291718
Homo sapiens (human)
ChIP-seq / ATAC-seq (genome binding and open chromatin)
56 samples
Submitted 2025/07/10
Platform GPL24676Platform GPL34281
At a glance
ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 56 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)
- Topic
- Myelodysplastic syndromesThis series is part of our curated briefing on it.
- Samples
- 56 samples: 56 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.
- Paper
- Analysis of chromatin accessibility associated with azacitidine response in higher-risk myelodysplastic neoplasmsiScience · 2025
Summary (written by the submitters)
Azacitidine is used to treat patients with myelodysplastic syndrome, which alters epigenetic regulatory program that changes global gene expression by inhibiting DNA methylation. However, epigenomic factors affecting azacitidine response have not been studied in the context of chromatin. The efficacy of the hypomethylating agent differs by patient, leaving approximately 40% of patients fail to respond.
Read the full summary ↓
Here, we demonstrate that chromatin accessibility features in patient-derived bone marrow cells can distinguish azacitidine responders from non-responders before treatment. Profiling chromatin accessibility from 23 high-risk myelodysplastic syndrome patients discovered that treatment response is strongly associated with distinct hematopoietic cell states. While non-responders showed enrichment of myeloid progenitor signatures, responders were characterized by elevated T cell populations. Notably, CD8+ T cells from non-responders exhibited selective loss of chromatin accessibility at TBX/EOMES binding sites, revealing a previously unrecognized link between T cell differentiation state and azacitidine response. These findings suggest the importance of immune cell differentiation and their activity in hypomethylating agent response for myelodysplastic syndromes, providing insights for patient stratification and treatment optimization based on the chromatin accessibility in high-risk myelodysplastic syndrome patients.
Published in
Kim D, Park S, Kwon YR et al.
· iScience 2025
· PMID 40894874
· doi:10.1016/j.isci.2025.113297
This dataset
Open GSE291718 in finder →
Find more
human ChIP / ATAC / CUT&Tag datasets →
Myelodysplastic syndromes landscape →
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 GSE291718_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 PRJNA1235117. Searching any of these in the dataset finder brings you back here.
Study design
56 samples, each with a distinct title — no replicated groups
Supports a case/control comparison: 50 samples read as cases, 6 as controls.
How we read this: Read from 56 sample titles: 56 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
- GSM8840879 Patient MDS_01, frozen bone marrow, bulk-ATAC-seq
- GSM8840880 Patient MDS_02, frozen bone marrow, bulk-ATAC-seq
- GSM8840881 Patient MDS_03, frozen bone marrow, bulk-ATAC-seq
- GSM8840882 Patient MDS_04, frozen bone marrow, bulk-ATAC-seq
- GSM8840883 Patient MDS_05, frozen bone marrow, bulk-ATAC-seq
- GSM8840884 Patient MDS_06, frozen bone marrow, bulk-ATAC-seq
- GSM8840885 Patient MDS_07, frozen bone marrow, bulk-ATAC-seq
- GSM8840886 Patient MDS_08, frozen bone marrow, bulk-ATAC-seq
- GSM8840887 Patient MDS_09, frozen bone marrow, bulk-ATAC-seq
- GSM8840888 Patient MDS_10, frozen bone marrow, bulk-ATAC-seq
- GSM8840889 Patient MDS_11, frozen bone marrow, bulk-ATAC-seq
- GSM8840890 Patient MDS_12, frozen bone marrow, bulk-ATAC-seq
- GSM8840891 Patient MDS_13, frozen bone marrow, bulk-ATAC-seq
- GSM8840892 Patient MDS_14, frozen bone marrow, bulk-ATAC-seq
- GSM8840893 Patient MDS_15, frozen bone marrow, bulk-ATAC-seq
- GSM8840894 Patient MDS_16, frozen bone marrow, bulk-ATAC-seq
- GSM8840895 Patient MDS_17, frozen bone marrow, bulk-ATAC-seq
- GSM8840896 Patient MDS_18, frozen bone marrow, bulk-ATAC-seq
- GSM8840897 Patient MDS_19, frozen bone marrow, bulk-ATAC-seq
- GSM8840898 Patient MDS_20, frozen bone marrow, bulk-ATAC-seq
- GSM8840899 Patient MDS_21, frozen bone marrow, bulk-ATAC-seq
- GSM8840900 Patient MDS_22, frozen bone marrow, bulk-ATAC-seq
- GSM8840901 Patient MDS_23, frozen bone marrow, bulk-ATAC-seq
- GSM8840902 Patient AML_01, frozen bone marrow, bulk-ATAC-seq
- GSM8840903 Patient AML_02, frozen bone marrow, bulk-ATAC-seq
- GSM8840904 Patient AML_03, frozen bone marrow, bulk-ATAC-seq
- GSM8840905 Patient AML_04, frozen bone marrow, bulk-ATAC-seq
- GSM8840906 Patient AML_05, frozen bone marrow, bulk-ATAC-seq
- GSM8840907 Patient AML_06, frozen bone marrow, bulk-ATAC-seq
- GSM8840908 Patient AML_07, frozen bone marrow, bulk-ATAC-seq
- GSM8840909 Patient AML_08, frozen bone marrow, bulk-ATAC-seq
- GSM8840910 Patient AML_09, frozen bone marrow, bulk-ATAC-seq
- GSM8840911 Patient AML_10, frozen bone marrow, bulk-ATAC-seq
- GSM8840912 Patient AML_11, frozen bone marrow, bulk-ATAC-seq
- GSM8840913 Patient AML_12, frozen bone marrow, bulk-ATAC-seq
- GSM8840914 Patient AML_13, frozen bone marrow, bulk-ATAC-seq
- GSM8840915 Healthy donor HD_01, frozen bone marrow, bulk-ATAC-seq
- GSM8840916 Healthy donor HD_02, frozen bone marrow, bulk-ATAC-seq
- GSM8840917 Healthy donor HD_03, frozen bone marrow, bulk-ATAC-seq
- GSM8840918 Patient MDS_02, CD8+ T cell sorted from bone marrow, bulk-ATAC-seq
+ 16 more — browse all 56 samples with per-sample file links →
More Myelodysplastic syndromes datasets
- GSE248804 MED16 negatively regulates erythropoiesis and myelopoiesis through modulation of chromatin accessibility [ChIP-Seq human] 12 samples same disease briefing · shares accessibility, chromatin
- GSE248802 MED16 negatively regulates erythropoiesis and myelopoiesis through modulation of chromatin accessibility [ATAC-Seq human] 8 samples same disease briefing · shares accessibility, chromatin
- GSE298729 Chromatin accessibility of hematopoietic stem/progenitor cells (HSPCs) derived from MDS patients before and after disease progression 2 samples same disease briefing · shares accessibility, chromatin
- GSE265824 Human iPSCs-based modeling unveils a dominant role of SETBP1 mutation in shaping stable chromatin accessibility landscape in GATA2 deficiency 25 samples same disease briefing · shares accessibility, chromatin
- GSE248808 MED16 negatively regulates erythropoiesis and myelopoiesis through modulation of chromatin accessibility [RNA-Seq human] 16 samples same disease briefing · shares accessibility, chromatin
- GSE248810 MED16 negatively regulates erythropoiesis and myelopoiesis through modulation of chromatin accessibility [scRNA-Seq] 2 samples same disease briefing · shares accessibility, chromatin
- GSE289476 Natural killer cells’ functional impairment drives the immune escape of pre-malignant clones in early-stage myelodysplastic syndromes 16 samples same disease briefing · shares myelodysplastic
- GSE256396 Somatic mutations and DNA methylation identify a subgroup of poor prognosis within lower risk myelodysplastic syndromes 374 samples same disease briefing · shares myelodysplastic
Share this dataset
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.