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Lymph node macrophages drive immune tolerance and resistance to cancer therapy by expression of the immune-regulatory cytokine IL-33 [RNA-seq]

GSE268774 Mus musculus (mouse) RNA-seq (gene expression) 48 samples Submitted 2025/01/28 Platform GPL19057
At a glance

RNA-seq (gene expression) · mouse · 48 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
48 samples: 16 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
Lymph node macrophages drive immune tolerance and resistance to cancer therapy by induction of the immune-regulatory cytokine IL-33Cancer cell · 2025
Summary (written by the submitters)
Apoptotic cells are immunosuppressive, creating a potential therapeutic barrier in cancer therapy. In this reportwe investigated responses to apoptotic tumor cell phagocytosis (i.e. efferocytosis) after therapy in the tumor draining lymph node (TDLN). Treatment with cisplatin or the BRAF inhibitor PLX4720 caused a significant increase in accumulation of apoptotic tumor material in the TDLN.
Read the full summary ↓
We identified the primary phagocyte population responsible for clearing dying tumors in the TDLN was medullary sinus macrophages (MSMs). Tumor cell efferocytosis by MSMs induced an immune suppressive transcriptional program distinct from other macrophage or dendritic cell populations in the TDLN. One of the most differentially induced mRNAs in MSMs wasIl33,coding fora potent immune-regulatory cytokine. Administration of neutralizing anti-IL-33 receptor antibodies or deletion ofIl33in MSMs altered tumor responses to therapy with a significant enhancement of anti-tumor activity compared to controls. Mechanistically, IL-33 activated Treg cells in the TDLN with subsequent suppression of CD8+T cell responses. Importantly, combining IL-33 receptor blockade, BRAF inhibitor, and PD-1 blockade significantly improved tumor regression with enhanced, CD8+T cell dependent immunity. Finally, apoptotic tumor cells induced IL-33 expression in human macrophages, IL-33 expression in sentinel lymph nodes positively corresponded with disease stage and negatively correlated with survival in melanoma and breast cancer. Thus, the data identifies an immune response to therapy that abrogates nascent anti-tumor immunity, providing a previously undescribed functional interaction with broad implications for cancer therapy.
Published in
Lymph node macrophages drive immune tolerance and resistance to cancer therapy by induction of the immune-regulatory cytokine IL-33
Lamorte S, Quevedo R, Jin R et al. · Cancer cell 2025 · PMID 40054466 · doi:10.1016/j.ccell.2025.02.017
This dataset
Open GSE268774 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 GSE268774_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 PRJNA1118727 and SRA study SRP511035. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40054466) ↗
Study design
16 conditions, mostly in triplicate
LN-CIS MSM ×3 LN-CIS SSM ×3 LN-PBS MSM ×3 LN-PBS SSM ×3 TDLN-CIS MSM ×3 TDLN-CIS SSM ×3 TDLN-PBS MSM ×3 TDLN-PBS SSM ×3 +8 more

Supports a between-group comparison across 48 samples.

How we read this: 16 replicated groups read from 48 sample titles; they account for 48 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

+ 8 more — browse all 48 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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