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Rb CUT&RUN profiling in ovarian cancer cell lines following CDK2 inhibitor treatment

GSE314300 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 10 samples Submitted 2026/06/29 Platform GPL18573
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 10 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
Ovarian cancerThis series is part of our curated briefing on it.
Samples
10 samples: 10 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.
Summary (written by the submitters)
The retinoblastoma protein (Rb) is a tumour suppressor best known for repressing E2F transcription factors and halting cell cycle progression. In hormone receptor-positive (HR+) breast cancer, CDK4/6 inhibitors activate Rb by preventing its phosphorylation, forming a key component of current endocrine therapy regimens. How pharmacologically activated Rb remodels chromatin and influences transcription beyond cell cycle arrest remains poorly understood.
Read the full summary ↓
Here we show that CDK4/6 inhibition induces redistribution of hypo-phosphorylated Rb to promoters and enhancers. While Rb predictably binds to cell cycle gene promoters to repress transcription, at other sites it unexpectedly promotes expression of oestrogen-responsive genes by integrating into oestrogen receptor (ER)-rich transcriptional hubs. CDK4/6 inhibition enhances ER target gene expression in breast cancer cells, patient-derived xenografts, and clinical HR+ breast cancer samples in an Rb-dependent manner. This reprogramming is mediated in part by KDM5A, whose interaction with Rb contributes to gene regulation at these loci. Critically, components of this Rb-driven ER transcriptional program are pro-proliferative. In endocrine-sensitive tumours, this can be neutralised with anti-oestrogen therapy, explaining therapeutic synergy. In endocrine-resistant settings such as ESR1-mutant breast cancer, the program persists, limiting therapeutic efficacy. These findings reframe Rb as a dual-function transcriptional regulator that, while enforcing cell cycle arrest, can also activate programs that counteract its tumour suppressor function.
This dataset
Open GSE314300 in finder → Find more human ChIP / ATAC / CUT&Tag datasets → Ovarian cancer 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.

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 GSE314300_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 PRJNA1390680 and SRA study SRP656395. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
10 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 10 sample titles: 10 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
More Ovarian cancer datasets

Search all human ChIP / ATAC / CUT&Tag datasets in GEO →

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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.