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PPP2R1A mutations portend improved survival after cancer immunotherapy

GSE276284 Homo sapiens (human) RNA-seq (gene expression) 43 samples Submitted 2025/05/01 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 43 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)
Samples
43 samples: 29 × Pre-Acc vs 14 × On-AccGroups 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
PPP2R1A mutations portend improved survival after cancer immunotherapyNature · 2025
Summary (written by the submitters)
Immune checkpoint blockade (ICB) therapy is effective against many cancers, though resistance remains a major issue and novel strategies are needed to improve clinical outcomes. We studied ICB response in a cohort of patients with ovarian clear cell carcinoma (OCCC), a cancer type that poses significant clinical challenges and lacks effective therapies.
Read the full summary ↓
Here we observed significantly prolonged overall and progression-free survival in patients with tumors harboring PPP2R1A inactivating mutations (PPP2R1Amut). Importantly, findings were validated in additional ICB-treated patient cohorts across multiple cancer types. Translational analyses from tumor biopsies demonstrated enhanced interferon-gamma signaling, and presence of tertiary lymphoid structures at baseline, as well as enhanced immune infiltration and expansion of CD45RO+ CD8+ T cells in the tumor neighborhood upon ICB treatment, in PPP2R1Amut tumors. Parallel pre-clinical investigations showed that targeting PPP2R1A (via pharmacologic inhibition or knockdown) in in vitro and in vivo models was associated with improved survival in the setting of treatment with several forms of immunotherapy, including chimeric antigen receptor-T cell therapy and ICB. Results from these studies suggest that therapeutic targeting of PPP2R1A may represent an effective strategy to improve patient outcomes following ICB or other forms of immunotherapy, though additional mechanistic and therapeutic insights are needed.
Published in
PPP2R1A mutations portend improved survival after cancer immunotherapy
Dai Y, Knisely A, Yano M et al. · Nature 2025 · PMID 40604275 · doi:10.1038/s41586-025-09203-8
This dataset
Open GSE276284 in finder → Find more human 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 GSE276284_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 PRJNA1156257 and SRA study SRP530328. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40604275) ↗
Study design
29 × Pre-Acc vs 14 × On-Acc

Supports a between-group comparison across 43 samples.

How we read this: 2 replicated groups read from 43 sample titles; they account for 43 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

+ 3 more — browse all 43 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.