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TGFβ signaling promotes cell cycle progression and resistance to the CDK4/6 inhibitor palbociclib through SOX4 transcriptional modulation in breast cancer cells

GSE300358 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 40 samples Submitted 2026/02/04 Platform GPL24676
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 40 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
Breast cancerThis series is part of our curated briefing on it.
Samples
40 samples: 12 conditions, mostly in triplicateGroups 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
TGFβ signaling promotes cell cycle progression and resistance to the CDK4/6 inhibitor palbociclib through SOX4 transcriptional modulation in breast cancer cellsCell death & disease · 2026
Summary (written by the submitters)
During pre-malignant hyperplastic growth, TGFβ restricts cell proliferation and inflammation, while on the other hand, TGFβ promotes migration and distal metastasis of cancer cells. To dissect the temporal chromatin-based transcriptional response to TGFβ, we employed 3D culture models of isogenic human breast epithelial cells, exemplified by non-oncogenic MCF-10A (MI) and its HRAS-transformed counterpart (MII).
Read the full summary ↓
Genome-wide chromatin accessibility profiling revealed the extensive chromatin opening induced by TGFβ at transcription start sites and enhancer elements in both models, with a marked enrichment of SOX4 binding motifs in oncogenic cells.
Published in
TGFβ signaling promotes cell cycle progression and resistance to the CDK4/6 inhibitor palbociclib through SOX4 transcriptional modulation in breast cancer cells
Ali MM, Itoh Y, Badji AMP et al. · Cell death & disease 2026 · PMID 41639049 · doi:10.1038/s41419-026-08435-4
This dataset
Open GSE300358 in finder → Find more human RNA-seq datasets → Breast 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 GSE300358_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 PRJNA1280147 and SRA study SRP593491. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41639049) ↗
Study design
12 conditions, mostly in triplicate
ATAC_MI-Ctrl ×4 ATAC_MI-TGF1 ×4 ATAC_MII-Ctrl ×4 ATAC_MII-TGF1 ×4 MDA-MB-231_Csi ×3 MDA-MB-231_SMAD_KD ×3 MI-Ctrl ×3 MI-TGFb ×3 +4 more

Supports a between-group comparison across 40 samples.

How we read this: 12 replicated groups read from 40 sample titles; they account for 40 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
More Breast cancer datasets

Search all human RNA-seq 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.