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EZH1/EZH2 Inhibition Enhances Adoptive T-cell Immunotherapy Against Multiple Cancer Models

GSE285897 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 12 samples Submitted 2025/03/15 Platform GPL30173
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 12 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
12 samples: 3 × SUDHL4-DMSO-UTD vs 3 × SUDHL4-TAZ-UTD vs 3 × SUDHL4-DMSO-CART19 vs 3 × SUDHL4-TAZ-CART19Groups 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
EZH1/EZH2 inhibition enhances adoptive T cell immunotherapy against multiple cancer modelsCancer cell · 2025
Summary (written by the submitters)
Tumor resistance to chimeric antigen receptor T-cell (CART) therapy is a major challenge. Epigenetic modulation, which rewires cancer cells to a more immunogenic phenotype, could improve adoptive T cell (ACT) efficacy. This study hypothesized that inhibiting EZH2 would enhance ACT. In Germinal Center-derived B-cell human lymphoma models, EZH2 inhibition with tazemetostat improved anti-CD19 CART (CART19) tumor control.
Read the full summary ↓
The goal of this experiment is to test is tazemetostat-treated tumors showed chromatin opening at genes related to cell-cell adhesion, B-cell activation, and inflammatory responses.
Published in
EZH1/EZH2 inhibition enhances adoptive T cell immunotherapy against multiple cancer models
Porazzi P, Nason S, Yang Z et al. · Cancer cell 2025 · PMID 39983725 · doi:10.1016/j.ccell.2025.01.013
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 GSE285897_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 PRJNA1206723 and SRA study SRP555374. Searching any of these in the dataset finder brings you back here.

Study design
3 × SUDHL4-DMSO-UTD vs 3 × SUDHL4-TAZ-UTD vs 3 × SUDHL4-DMSO-CART19 vs 3 × SUDHL4-TAZ-CART19

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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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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.