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EZH2 inhibitor SHR2554 enhances the anti-tumor efficacy of HDAC inhibitor Chidamide through STAT1 in T-cell lymphoma

GSE293546 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 34 samples Submitted 2026/04/30 Platform GPL20301
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 34 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
34 samples: 25 distinct titles, 6 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.
Summary (written by the submitters)
T-cell lymphoma (TCL) is a rare subtype of non-Hodgkin lymphoma (NHL) that is associated with a poor prognosis. Although HDAC inhibitors have been approved for TCL treatment for several years, their expected therapeutic efficacy remains unmet in some patients. In this study, we discovered that TCL tumor cells develop resistance to HDAC inhibitor treatment by upregulating the methylation of lysine 27 on histone H3 (H3K27me3) levels.
Read the full summary ↓
Furthermore, we confirmed the pharmacological efficacy of the EZH2 inhibitor SHR2554 and demonstrated a synergistic effect when combined with the HDAC inhibitor Chidamide through commercial T-cell lymphoma cell lines, in vivo cell-derived xenograft, and patient-derived xenograft cancer models. We inferred that STAT1 was the key driver of the synergy effect using RNA-seq and ChIP-seq analysis. Our findings provide sufficient preclinical evidence in support of a potential combination therapy strategy for TCL patients.
This dataset
Open GSE293546 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 GSE293546_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 PRJNA1245163 and SRA study SRP575907. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
25 distinct titles, 6 of them repeated
H9_CT ×3 H9 ×3 H9_SHR5 ×3 H9_CHI0.2 ×2 H9_SHR8 ×2 H9_COMBO8-0_2 ×2

How we read this: Read from 34 sample titles: 25 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
Datasets on a similar topic

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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.