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Degradation of Ikaros prevents epigenetic progression of T cell exhaustion in novel antigen-specific assay [bulk ATAC-seq]

GSE244183 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 11 samples Submitted 2024/09/02 Platform GPL24676
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 11 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)
Samples
11 samples: 6 × TT_bulkATAC_untreated vs 5 × TT_bulkATAC_iberdomideGroups 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)
Epigenetic regulation drives the differentiation of T cells into phenotypically stable subsets including a dysfunctional state termed exhaustion, the presence of these epigenetically distinct T cells in tumors make them non-responsive to checkpoint blockade. Cancer immunotherapies restoring T cell function have transformed cancer treatment by preventing immune evasion of tumor cells allowed by T cell exhaustion.
Read the full summary ↓
However, some patients develop resistance to immunotherapy or do not respond. Recent literature has demonstrated that epigenetic regulation governs the transition from effector to exhausted T cells. Here, we describe a novel antigen-specific assay for T-cell exhaustion that produces T cells biologically and epigenetically similar to their in vivo exhausted counterpart. We perform a CRISPR KO screen for epigenetic regulators of the human genome and validate Ikaros as a driver of T-cell exhaustion. We determined that the Ikaros degrader iberdomide prevents exhaustion by blocking chromatin remodeling at T cell effector enhancers by preserving activity of key transcription factors AP-1, NF-κB and NFAT using transcription factor footprinting analysis. Thus, our study uncovers a role for Ikaros, degraded by the drug iberdomide, as a driver of T-cell exhaustion.
This dataset
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.

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 GSE244183_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1021618 and SRA study SRP463527. Searching any of these in the dataset finder brings you back here.

Study design
6 × TT_bulkATAC_untreated vs 5 × TT_bulkATAC_iberdomide

Supports a between-group comparison across 11 samples.

How we read this: 2 replicated groups read from 11 sample titles; they account for 11 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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