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PLA2G2D in tumour-draining lymph nodes regulates anti-tumour immunity

GSE333464 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2026/06/23 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 12 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
12 samples: 12 samples, each with a distinct title — no replicated groupsGroups 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)
Current cancer immunotherapies can induce tumour regression, although responses are often not durable. Recent studies have highlighted the importance of enforcing a systemic immune response from tumour-draining lymph nodes (TDLNs). However, mechanisms driving immune suppression in TDLNs remain poorly studied. We applied spatial proteogenomics on melanoma TDLNs and identified regional differences in immune cell composition in lymph nodes from patients with long-term (>5 years) disease control compared to those with early (<2 years) disease recurrence.
Read the full summary ↓
TDLNs from patients with a poor prognosis were characterized by the expansion of regulatory T cells (Tregs), dendritic cells (DCs), and macrophages interacting with cytotoxic CD8+ T cells. Targeted transcriptomics of cell types within this spatial neighbourhood revealed immunoregulatory myeloid cell phenotypes engaging with CD8+ T cells poised for exhaustion. Besides known immune-suppressive molecules such as IDO and PD-L1, paracortex macrophages and DCs in patients with recurrent disease expressed high levels of the secreted phospholipase PLA2G2D across different cancer types. Additionally, PLA2G2D was upregulated in non-small cell lung cancer patients who developed acquired resistance to anti-PD-1 immunotherapy. Importantly, PLA2G2D loss-of-function or therapeutic inhibition markedly improved survival in tumour-bearing mice and increased the efficacy of anti-PD-1 immunotherapy. Collectively, we show that in-depth spatial profiling of cancer patient TDLNs offers novel insights into systemic anti-tumour immunity and identifies PLA2G2D as a target for cancer immunotherapy.
This dataset
Open GSE333464 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 GSE333464_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 PRJNA1471142 and SRA study SRP704151. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
12 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 12 sample titles: 12 distinct titles with little repetition. 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.