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STAG2 loss amplifies EWS-FLI1-driven microsatellite enhancer activity promoting Ewing's sarcoma aggressiveness [ATAC-seq]

GSE314150 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 9 samples Submitted 2026/04/15 Platform GPL16791
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 9 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
9 samples: 3 × A673_shNC_d10 vs 3 × A673_shSTAG2_16_d10 vs 3 × A673_shSTAG2_48_d10Groups 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
STAG2 loss amplifies EWS-FLI1-driven microsatellite enhancer activity promoting Ewing sarcoma aggressivenessProceedings of the National Academy of Sciences of the United States of America · 2026
Summary (written by the submitters)
Ewing sarcomas are driven by chromosomal translocations that fuse a FET RNA‑binding protein to an ETS transcription factor, most commonly generating the EWS-FLI1 fusion oncoprotein. EWS-FLI1 engages GGAA microsatellite repeats to create de novo enhancers and activate oncogenic transcriptional programs—a neomorphic gain-of-function essential for Ewing sarcoma pathogenesis.
Read the full summary ↓
In addition to the truncal fusion, recurrent loss‑of‑function alterations in the cohesin subunit STAG2 occur in approximately 10–15% of Ewing sarcomas and are associated with adverse clinical outcomes. Yet, how STAG2-cohesin deficiency remodels EWS-FLI1 chromatin occupancy and gene regulatory network remains incompletely understood. Here, using genetic STAG2 loss‑of‑function models combined with functional multi-omic profiling, we show that STAG2-cohesin loss in Ewing sarcoma cells reprograms the EWS-FLI1 cistrome by shifting its binding preference at GGAA microsatellite repeats. Despite increased EWS–FLI1 protein abundance, disruption of STAG2 eliminates more than 40% of EWS-FLI1 binding sites. The lost sites are enriched for elements harboring 1–4 GGAA repeat motifs, with a simultaneous gain in EWS-FLI1 binding at multimeric enhancers containing ≥5 GGAA repeat sequences. Notably, reprogrammed EWS-FLI1 sites show concomitant changes in chromatin accessibility and H3K27ac abundance, which preferentially amplify EWS-FLI1 activity at multimeric enhancers and drive marked up‑regulation of canonical microsatellite‑regulated target genes in STAG2‑null cells. By integrating Hi‑C–derived chromatin interaction maps with altered EWS-FLI1 occupancy, we derive distinct monomeric (1xGGAA) and multimeric (≥10xGGAA) EWS-FLI1 transcriptional signatures and show that STAG2 inactivation selectively augments the multimeric signature while attenuating monomeric activity. We further define a prognostic signature of GGAA-repeat enhancers that is significantly upregulated in patient tumors with aggressive clinical features and deleterious STAG2 alterations. Together, these findings reveal that loss of STAG2–cohesin does not simply attenuate EWS-FLI1 function but reprograms its cistrome toward microsatellite multimeric GGAA neo‑enhancers, thereby amplifying a high‑risk EWS–FLI1 transcriptional state in Ewing sarcoma.
Published in
STAG2 loss amplifies EWS-FLI1-driven microsatellite enhancer activity promoting Ewing sarcoma aggressiveness
Eyunni S, Chu SC, Guan ML et al. · Proceedings of the National Academy of Sciences of the United States of America 2026 · PMID 41950086 · doi:10.1073/pnas.2537425123
This dataset
Open GSE314150 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 GSE314150_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 PRJNA1390167 and SRA study SRP656061. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41950086) ↗
Study design
3 × A673_shNC_d10 vs 3 × A673_shSTAG2_16_d10 vs 3 × A673_shSTAG2_48_d10

Supports a between-group comparison across 9 samples.

How we read this: 3 replicated groups read from 9 sample titles; they account for 9 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
Datasets on a similar topic

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