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CDK9 is a Dependency in GATA-3 driven and MCL-1 independent T-cell Lymphomas [ChIP-seq]

GSE296537 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 33 samples Submitted 2025/11/28 Platform GPL34284
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 33 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
33 samples: 33 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
CDK9 is a dependency in GATA-3 driven and MCL-1 independent T-cell LymphomasBlood cancer journal · 2025
Summary (written by the submitters)
The transcription factor GATA-3 is a proto-oncogene in subsets of mature T cell lymphomas (TCL). CDK9 is a transcriptional regulator that, by phosphorylating paused RNA polymerase II (RNAPII), enables efficient transcriptional elongation. Therefore, we were motivated to examine the extent to which CDK9 is required for controlling transcription and expression of GATA-3 and its target genes.
Read the full summary ↓
A pharmacologic strategy, using the selective CDK9 inhibitor AZD4573, was utilized to identify CDK9 dependent transcripts. Traveling ratios, defined as the ratio of RNAPII bound at promoter to RNAPII bound within the gene body, were determined by RNAPII ChIP-seq in cell treated with AZD4573 (or vehicle control). CDK9 dependent genes were defined as those with increased traveling ratio and decreased transcripts. MCL-1 dependency was determined in these cell lines by BH3 profiling. The extent to which CDK9 inhibition transcriptionally reprograms TCL was further examined in complementary ex vivo and in vivo studies using primary TCL specimens and GEM and PDX models
Published in
CDK9 is a dependency in GATA-3 driven and MCL-1 independent T-cell Lymphomas
Wang C, Abdelrahman S, Geng X et al. · Blood cancer journal 2025 · PMID 41309546 · doi:10.1038/s41408-025-01427-1
This dataset
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 GSE296537_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 PRJNA1259917 and SRA study SRP583580. Searching any of these in the dataset finder brings you back here.

Study design
33 samples, each with a distinct title — no replicated groups

Supports a case/control comparison: 10 samples read as cases, 15 as controls.

How we read this: Read from 33 sample titles: 33 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.