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SETD2 loss-of-function uniquely sensitizes cells to epigenetic targeting of NSD1-directed H3K36 methylation.

GSE279619 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 124 samples Submitted 2025/01/17 Platform GPL20301
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 124 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
Kidney cancerThis series is part of our curated briefing on it.
Samples
124 samples: 124 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
SETD2 loss-of-function uniquely sensitizes cells to epigenetic targeting of NSD1-directed H3K36 methylationGenome biology · 2025
Summary (written by the submitters)
SETD2 is the sole epigenetic factor responsible for catalyzing histone 3, lysine 36, tri-methylation (H3K36me3) in mammals. Its role in regulating diverse cellular processes such as RNA splicing, DNA repair, and spurious transcription underlie its broader tumor suppressor function, which has been detailed in a multitude of cancer types.
Read the full summary ↓
SETD2 mutation, and by extension loss of H3K36me3, promotes the epithelial-mesenchymal transition (EMT) and is clinically associated with adverse outcomes highlighting a therapeutic need to develop targeted therapies against this dangerous mutation. To this end we employed an unbiased genome-wide synthetic lethal screen, which identified another H3K36me writer, NSD1, as a synthetic lethal modifier in SETD2-mutant cells. Confirmation of this synthetic lethal interaction was performed via modeling in isogenic clear cell renal cell carcinoma (ccRCC ) cell lines. Depletion of NSD1 using a CRISPRi targeting approach promoted the loss of SETD2-mutant cells coincident with elevated DNA damage and apoptosis. Surprisingly, only suppression of NSD1, and not the related H3K36-methyltransferases NSD2 or NSD3, promoted synthetic lethality (SL) in these models. Further investigation of H3K36me2 targeting by NSD1 and NSD2 respectively highlight the independent functions of these epigenetic writers. Furthermore, as a proof-of-principle we demonstrate the therapeutic feasibility of targeting this synthetic lethal interaction by recapitulating the phenotype using a first-of-its kind pharmacologic inhibitor against NSD1 (BT5). Notably, these findings unify unbiased genome-wide screening approaches with the latest genetic and pharmacologic modeling methodologies to reveal an entirely novel epigenetic approach in the advancement of individualized therapies for the treatment of SETD2-mutant cancer.
Published in
SETD2 loss-of-function uniquely sensitizes cells to epigenetic targeting of NSD1-directed H3K36 methylation
Wagner RT, Hlady RA, Pan X et al. · Genome biology 2025 · PMID 39910618 · doi:10.1186/s13059-025-03483-z
This dataset
Open GSE279619 in finder → Find more human ChIP / ATAC / CUT&Tag datasets → Kidney 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 GSE279619_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 PRJNA1173660 and SRA study SRP538934. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39910618) ↗
Study design
124 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 124 sample titles: 124 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

+ 84 more — browse all 124 samples with per-sample file links →

More Kidney cancer datasets

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