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MTA-cooperative PRMT5 inhibitors enhance T cell-mediated antitumor activity in MTAP-loss tumors

GSE276571 Mus musculus (mouse) RNA-seq (gene expression) 26 samples Submitted 2025/09/05 Platform GPL34290
At a glance

RNA-seq (gene expression) · mouse · 26 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
26 samples: 9 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.
Summary (written by the submitters)
Background: Hyperactivated protein arginine methyltransferases (PRMTs) are implicated in human cancers. Inhibiting tumor intrinsic PRMT5 was reported to potentiate antitumor immune responses, highlighting the possibility of combining PRMT5 inhibitors (PRMT5i) with cancer immunotherapy. However, global suppression of PRMT5 activity impairs the effector functions of immune cells.
Read the full summary ↓
Here, we sought to identify strategies to specifically inhibit PRMT5 activity in tumor tissues and develop effective PRMT5i-based immuno-oncology (IO) combinations for cancer treatment, particularly for methylthioadenosine phosphorylase (MTAP)-loss cancer. Methods: Isogeneic tumor lines with and without MTAP loss were generated by CRISPR/Cas9 knockout. The effects of two PRMT5 inhibitors (GSK3326595 and MRTX1719) were evaluated in these isogenic tumor lines and T cells in vitro and in vivo. Transcriptomic and proteomic changes in tumors and T cells were characterized in response to PRMT5i treatment. Results: transcriptomic and proteomic profiling analysis reveals that MRTX1719 successfully reduces activation of the PI3K pathway, a well-documented immune-resistant pathway. It highlights the potential of MRTX1719 to overcome immune resistance in MTAP-loss tumors. Conclusion: Collectively, our results provide a strong rationale and mechanistic insights for the clinical development of MRTX1719-based IO combinations in MTAP-loss tumors.
This dataset
Open GSE276571 in finder → Find more mouse 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 GSE276571_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 PRJNA1157863 and SRA study SRP531166. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
9 conditions, mostly in triplicate
MC38/gp100 DMSO ×3 MC38/gp100 0.5 μM GSK3326595 ×3 MC38/gp100 Mtap KO DMSO ×3 MC38/gp100 Mtap KO 0.5 μM GSK3326595 ×3 MC38/gp100 Mtap KO 0.5 μM MRTX1719 ×3 T cell DMSO ×3 T cell 0.5 μM GSK3326595 ×3 T cell 0.5 μM MRTX1719 ×3 +1 more

Supports a between-group comparison across 26 samples.

How we read this: 9 replicated groups read from 26 sample titles; they account for 26 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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