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PRMT5 Inhibition Triggers Functional ATM Deficiency and Sensitizes Pancreatic Cancer to CHK1 Blockade

GSE310927 Homo sapiens (human) RNA-seq (gene expression) 36 samples Submitted 2026/02/26 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 36 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
Homo sapiens (human)
Topic
Pancreatic cancerThis series is part of our curated briefing on it.
Samples
36 samples: 12 conditions, mostly in triplicateGroups 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
PRMT5 inhibition triggers functional ATM deficiency and sensitizes pancreatic cancer to CHK1 blockadeFrontiers in cell and developmental biology · 2026
Summary (written by the submitters)
PRMT5 inhibitors are under clinical investigation for pancreatic ductal adenocarcinoma (PDAC), but strategies to maximize their therapeutic efficacy remain undefined. Here, we report that pharmacologic inhibition of PRMT5 markedly reduces levels of the DNA damage response kinase ATM across multiple PDAC cell lines and different PRMT5 inhibitors, leading to a state of functional ATM haploinsufficiency.
Read the full summary ↓
We find that this functional deficiency rewires checkpoint signaling, rendering PDAC cells reliant on the ATR-CHK1 pathway for survival. Consequently, we tested combined PRMT5 and CHK1 inhibition and found synergistic suppression of PDAC growth, accompanied by enhanced Caspase 3/7 activation, Annexin V staining, and DNA damage accumulation. Congruent with these results, RNA-seq demonstrated downregulation of cell-cycle and DNA repair genes along with the upregulation of cell death pathways, providing mechanistic insight into the cooperative effect. Moreover, in subcutaneous xenografts, the combination substantially reduced tumor volume, prolonged median survival, and did not affect overall health or body weight. Treated tumors showed reduced ATM and Ki67 with elevated γH2A.X. Together, these findings identify PRMT5 inhibition as a trigger of functional ATM haploinsufficiency that exposes a therapeutically actionable vulnerability to CHK1 inhibition, offering a rational, mechanistically-based combination strategy for this dismal disease.
Published in
PRMT5 inhibition triggers functional ATM deficiency and sensitizes pancreatic cancer to CHK1 blockade
Dzikowski M, Pollin G, Gunia V et al. · Frontiers in cell and developmental biology 2026 · PMID 41890907 · doi:10.3389/fcell.2026.1748541
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 GSE310927_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: 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 PRJNA1367546 and SRA study SRP647537. Searching any of these in the dataset finder brings you back here.

Study design
12 conditions, mostly in triplicate
D5_DMSO ×3 D5_EPZ ×3 D5_Prex1 ×3 D5_Prex2 ×3 D5_Combo1 ×3 D5_Combo2 ×3 D7_DMSO ×3 D7_EPZ ×3 +4 more

Supports a between-group comparison across 36 samples.

How we read this: 12 replicated groups read from 36 sample titles; they account for 36 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
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