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Mutant IDH1 inhibition induces dsDNA sensing to activate tumor immunity [ChIP-Seq]

GSE266686 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 32 samples Submitted 2024/05/06 Platform GPL24247
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 32 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
Mus musculus (mouse)
Samples
32 samples: 16 conditions, mostly in duplicateGroups 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)
Isocitrate Dehydrogenase 1 (IDH1) is the most commonly mutated metabolic gene across human cancers. Mutant IDH1 (mIDH1) generates the oncometabolite (R)-2-hydroxyglutarate, disrupting enzymes involved in epigenetics and other processes. A hallmark of IDH1-mutant solid tumors is T cell exclusion, whereas mIDH1 inhibition in preclinical models restores anti-tumor immunity.
Read the full summary ↓
Here, we define a cell-autonomous mechanism of mIDH1-driven immune evasion. IDH1-mutant solid tumors show striking, selective hypermethylation and silencing of the cytoplasmic dsDNA sensor, CGAS, compromising innate immune signaling. mIDH1 inhibition restores DNA demethylation, derepressing CGAS and transposable element (TE) subclasses. dsDNA produced by TE-reverse transcriptase activates cGAS, triggering viral mimicry and stimulating anti-tumor immunity. Thus, we demonstrate that mIDH1 epigenetically suppresses innate immunity and link endogenous reverse transcriptase activity to the mechanism of action of an FDA-approved oncology drug.
This dataset
Open GSE266686 in finder → Find more mouse 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 GSE266686_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 PRJNA1108266 and SRA study SRP505946. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
16 conditions, mostly in duplicate
ChIP-seq, Input, 2205 in vitro, DMSO, ×2 ChIP-seq, H3K4me3, 2205 in vitro, DMSO, ×2 ChIP-seq, H3K9me3, 2205 in vitro, DMSO, ×2 ChIP-seq, H3K27me3, 2205 in vitro, DMSO, ×2 ChIP-seq, Input, 2205 in vitro, IFNg, ×2 ChIP-seq, H3K4me3, 2205 in vitro, IFNg, ×2 ChIP-seq, H3K9me3, 2205 in vitro, IFNg, ×2 ChIP-seq, H3K27me3, 2205 in vitro, IFNg, ×2 +8 more

Supports a between-group comparison across 32 samples.

How we read this: 16 replicated groups read from 32 sample titles; they account for 32 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
Datasets on a similar topic

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