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Robust T cell activation kills tumors regardless of T cell specificity

GSE324375 Mus musculus (mouse) RNA-seq (gene expression) 39 samples Submitted 2026/08/05 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 39 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
39 samples: 39 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)
Immunotherapies putatively depend upon tumor-specific T cells. Here, we show how T cells can eliminate tumors without tumor-specificity via paracrine signaling. Activating unexhausted bystander non-tumor-specific T cells within tumors resulted in tumor elimination without conventional recognition-dependent mechanisms and in the absence of any tumor-specific TCRαâ T cell.
Read the full summary ↓
Robust T cell activation recruited immune cells, utilized innate leukocytes, and triggered a tumoricidal combination of effector molecules and panoptotic pathways. Mechanistically, IFN-γ, TNF, and NO induced caspase-dependent death, recapitulating melanoma clearance in mice or human melanoma cell death in vitro. Gene expression signatures associated with this response in mice predicted survival among human melanoma patients. Thus, triggering productive T cell activation within tumors can be sufficient for immunotherapy, without needing to induce or rescue cancer-specific responses.
This dataset
Open GSE324375 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 GSE324375_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 PRJNA1434514 and SRA study SRP682386. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
39 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 39 sample titles: 39 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.