GEO series

Priming with DNMT inhibitors potentiates PD-1 immunotherapy by triggering viral mimicry in relapsed/refractory NK/T-cell lymphoma

GSE292179 Mus musculus (mouse) RNA-seq (gene expression) 14 samples Submitted 2025/08/13 Platform GPL20301Platform GPL21103

At a glance

RNA-seq (gene expression) · mouse · 14 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: Mus musculus (mouse)
Samples: 14 samples: 7 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.
Paper: Priming with DNMT Inhibitors Potentiates PD-1 Immunotherapy by Triggering Viral Mimicry in Relapsed/Refractory NK/T-cell LymphomaCancer discovery · 2025

Summary (written by the submitters)

Anti-PD-1 immunotherapy has demonstrated significant anti-tumor efficacy in treating relapsed or refractory natural killer/T cell lymphoma (R/R NKTL). However, both intrinsic and acquired resistance remain a substantial challenge. DNA hypermethylation serves as a critical epigenetic modifier influencing anti-tumor immunity in NKTL. Here, we evaluate the efficacy and safety of combining DNA methyltransferase (DNMT) inhibitors with anti-PD-1 monoclonal antibody (mAb) as salvage therapy in 21 patients with R/R NKTL who had previously failed frontline immunotherapy.

Read the full summary ↓

This combination therapy achieved an objective response rate (ORR) of 66.7% (14/21), with a complete response (CR) rate of 47.6% (10/21), and a 2-year overall survival (OS) rate of 50.2%. To investigate the mechanisms underlying the therapeutic efficacy of this combination, we established a preclinical xenograft model of acquired resistance to anti-PD-1 therapy. Our findings revealed that resistance to anti-PD-1 treatment was characterized by the absence of CD8+ T-cell infiltration and the inactivation of interferon pathways. In contrast, DNMT inhibitors restored CD8+ T-cell infiltration, and sensitized tumors to anti-PD-1 therapy. Mechanistically, DNMT inhibitors induced DNA demethylation of endogenous retroviral elements (ERVs), leading to the upregulation of endogenous nucleic acid expression and the activation of a viral mimicry response. This response subsequently triggered the type I interferon pathway, thereby enhancing anti-tumor immunity in both murine and human T-cell lymphoma models. Collectively, our study highlights the potential of DNMT inhibitors in restoring tumor sensitivity to anti-PD-1 therapy and provides compelling evidence for combining DNMT inhibitors with anti-PD-1 mAb as a promising therapeutic strategy for R/R NKTL.

Published in

Priming with DNMT Inhibitors Potentiates PD-1 Immunotherapy by Triggering Viral Mimicry in Relapsed/Refractory NK/T-cell Lymphoma

Huang C, Gao Y, Chen J et al. · Cancer discovery 2025 · PMID 41088524 · doi:10.1158/2159-8290.CD-25-0587

This dataset

Open GSE292179 in finder → Find more 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 GSE292179_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 PRJNA1237341 and SRA study SRP570941. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41088524) ↗

Study design

7 conditions, mostly in duplicate

EL4 P0 tumor ×2 EL4 P3 tumor ×2 EG7-OVA cells, DMSO, ×2 EG7-OVA cells, AZA, ×2 EG7-OVA cells, DAC, ×2 NKYS cells, DMSO, ×2 NKYS cells, AZA, ×2

Supports a case/control comparison: 4 samples read as cases, 4 as controls.

How we read this: 7 replicated groups read from 14 sample titles; they account for 14 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

GSM8851772 EL4 P0 tumor rep 1 EL4 P0 tumor
GSM8851773 EL4 P0 tumor rep 2 EL4 P0 tumor
GSM8851774 EL4 P3 tumor rep 1 EL4 P3 tumor
GSM8851775 EL4 P3 tumor rep 2 EL4 P3 tumor
GSM8851776 EG7-OVA cells, DMSO, rep 2 EG7-OVA cells, DMSO,
GSM8851777 EG7-OVA cells, DMSO, rep 3 EG7-OVA cells, DMSO,
GSM8851778 EG7-OVA cells, AZA, rep 1 EG7-OVA cells, AZA,
GSM8851779 EG7-OVA cells, AZA, rep 2 EG7-OVA cells, AZA,
GSM8851780 EG7-OVA cells, DAC, rep 1 EG7-OVA cells, DAC,
GSM8851781 EG7-OVA cells, DAC, rep 2 EG7-OVA cells, DAC,
GSM8851782 NKYS cells, DMSO, rep 1 NKYS cells, DMSO,
GSM8851783 NKYS cells, DMSO, rep 2 NKYS cells, DMSO,
GSM8851784 NKYS cells, AZA, rep 1 NKYS cells, AZA,
GSM8851785 NKYS cells, AZA, rep 2 NKYS cells, AZA,

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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.