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PHIP suppresses NuRD to enable the growth of SWI/SNF-mutant cancers [RNA-seq]

GSE299562 Homo sapiens (human) RNA-seq (gene expression) 42 samples Submitted 2026/02/09 Platform GPL34284
At a glance

RNA-seq (gene expression) · human · 42 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
42 samples: 14 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
PHIP suppresses NuRD to enable the growth of SWI/SNF-mutant cancersNature communications · 2026
Summary (written by the submitters)
SWI/SNF chromatin remodeling complexes are perturbed in 20% of all cancers and several developmental disorders, yet the mechanisms by which these mutations dysregulate transcription and drive disease are poorly understood. To both elucidate these mechanisms and identify vulnerabilities caused by these mutations, we leveraged genome-wide CRISPR-Cas9 screening in hundreds of cancer cell lines and identified the chromatin reader protein PHIP as a specific dependency in cancers with broadly disrupted SWI/SNF function.
Read the full summary ↓
Mechanistically, we reveal that PHIP cooperates with SWI/SNF to facilitate transcriptional activation by ubiquitinating and suppressing subunits of the repressive Nucleosome Remodeling and Deacetylase (NuRD) complex. We demonstrate that loss of SWI/SNF results in NuRD complexes accumulating at promoters where they would otherwise cause widespread transcriptional silencing if not antagonized by PHIP. Collectively, we identify PHIP as a regulator of the interplay between distinct chromatin regulators that function in development and disease and as a targetable vulnerability in cancers with broad SWI/SNF inactivation.
Published in
PHIP suppresses NuRD to enable the growth of SWI/SNF-mutant cancers
Malone HA, Myers JA, Gruss EG et al. · Nature communications 2026 · PMID 41946722 · doi:10.1038/s41467-026-70699-3
This dataset
Open GSE299562 in finder → Find more human RNA-seq 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 GSE299562_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1274985 and SRA study SRP591395. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41946722) ↗
Study design
14 conditions, mostly in triplicate
A204_siCtrl ×3 A204_siPHIP ×3 G401_siCtrl-1 ×3 G401_siCtrl-2 ×3 G401_siPHIP-B ×3 G401_siPHIP-D ×3 HA-PHIPG401cB7_RNAseq_siCtrl_noDOX ×3 HA-PHIPG401cB7_RNAseq_siCtrl_plusDOX ×3 +6 more

Supports a between-group comparison across 42 samples.

How we read this: 14 replicated groups read from 42 sample titles; they account for 42 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

+ 2 more — browse all 42 samples with per-sample file links →

Datasets on a similar topic

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