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CDK4/6 inhibition induces a senescence-associated secretory phenotype via delayed NF-κB activation

GSE295258 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 54 samples Submitted 2026/06/22 Platform GPL24676Platform GPL30882
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 54 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
54 samples: 54 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
CDK4/6 inhibition induces a senescence-associated secretory phenotype via delayed NF-κB activationLife science alliance · 2026
Summary (written by the submitters)
Cellular senescence consists of regulated phenotypes associated with permanent exit from the cell cycle in response to stressors such as genomic instability. The consequences of senescence go beyond individual cells due to the senescence associated secretory phenotype (SASP), which can induce inflammation in neighboring cells. Some cancers respond to CDK4/6 inhibitors (CDK4/6i) with a senescence-like phenotype in the absence of extensive DNA damage.
Read the full summary ↓
We asked how the SASP and the transcriptional regulatory profile triggered by CDK4/6i-driven arrest compares to the canonical NF-κB-regulated SASP triggered by DNA damage. We profiled the dynamics of transcriptional regulation in response to the CDK4/6i, palbociclib, and the DNA damaging agent, doxorubicin. We found upregulation of NF-κB driven-SASP genes was shared across both drugs, although delayed in CDK4/6i, coinciding with slower enhancer activation and epigenetic changes. ATM/ATR inhibition did not affect CDK4/6i-induced NF-κB nuclear localization, suggesting an alternative upstream activator. Inhibiting NF-κB suppressed the expression of SASP genes without reversing stable arrest, pointing to SASP manipulation as a potential therapeutic strategy and providing insights into controversies regarding cell cycle arrest-driven SASP.
Published in
CDK4/6 inhibition induces a senescence-associated secretory phenotype via delayed NF-κB activation
Yeung JL, Rendleman J, Anderson Westcott L et al. · Life science alliance 2026 · PMID 42425764 · doi:10.26508/lsa.202603790
This dataset
Open GSE295258 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 GSE295258_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 PRJNA1253964 and SRA study SRP580397. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42425764) ↗
Study design
54 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 54 sample titles: 54 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

+ 14 more — browse all 54 samples with per-sample file links →

Datasets on a similar topic

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