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CD27 Agonism Enhances Long-Lived CD4⁺ T Cell Vaccine Responses Critical for 2 Anti-Tumor Immunity

GSE305350 Mus musculus (mouse) Single-cell RNA-seq 26 samples Submitted 2026/02/10 Platform GPL34290
At a glance

Single-cell RNA-seq · mouse · 26 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Mus musculus (mouse)
Samples
26 samples: 26 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.
Paper
CD27 agonism enhances long-lived CD4 T cell vaccine responses critical for antitumor immunityScience immunology · 2025
Summary (written by the submitters)
Patients with metastatic breast cancer were vaccinated with dendritic cell (DC) vaccine targeting HER2, and all seven survived >18 years. PBMC analysis revealed HER2-specific CD27⁺ memory CD4⁺ and CD8⁺ T cells, suggesting that CD27 signaling supports durable immune memory. We tested this by combining an anti-CD27 agonist antibody (Varlilumab) with a HER2 vaccine, which enhanced HER2-specific responses, particularly long-lived CD4⁺ memory T cells detectable up to 300 days post-vaccination.
Read the full summary ↓
CD27 agonism improved tumor control (~40% regression) compared to vaccine alone (~6%), and synergy with PD-1 blockade led to complete tumor rejection in ~90% of mice. CD4⁺ T cells were essential for this effect, as shown by depletion and adoptive transfer experiments, while CD8⁺ T cells played a less critical role. We utilized combined scRNA-seq and TCR sequencing to identify expanded clonotypes and increased CD8+ and CD4+ functional subsets with combined CD27 agonism and vaccination as well as vaccination alone. These findings demonstrate that antigen-specific huCD27⁺ CD4⁺ T cells are key effectors of vaccine-induced immunity and support CD27 agonism as a promising strategy to enhance therapeutic cancer vaccination.
Published in
CD27 agonism enhances long-lived CD4 T cell vaccine responses critical for antitumor immunity
Hwang BJ, Crosby EJ, Severson DT et al. · Science immunology 2025 · PMID 41417923 · doi:10.1126/sciimmunol.adz2294
This dataset
Open GSE305350 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 GSE305350_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1305364 and SRA study SRP608674. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41417923) ↗
Study design
26 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 26 sample titles: 26 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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