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CXCL16-driven CD4+ T cells orchestrate immunosurveillance against MHC-I-deficient tumors

GSE303349 Mus musculus (mouse) RNA-seq (gene expression) 24 samples Submitted 2026/03/01 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 24 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
24 samples: 7 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
CXCL16-driven CD4(+) T cells orchestrate immunosurveillance against MHC-I-deficient hepatocellular tumorsJournal for immunotherapy of cancer · 2026
Summary (written by the submitters)
MHC-I loss is a prevalent mechanism for immune evasion and resistance to immunotherapy. However, the mechanisms by which MHC-I loss shapes the tumor microenvironment (TME) and influences the interactions between MHC-I-deficient tumors and immune cells, ultimately affecting tumor growth, remain largely unknown. Here, we found that MHC-I/B2M loss increased tumor growth rates in the MC38, AKR and LLC1 tumor models, but surprisingly led to regression of Hepa1-6 tumors.
Read the full summary ↓
The effect of MHC-I/B2M loss on tumor growth was associated with the changes in immune infiltrates. CD4+ T cell, NK cells and macrophages were required to suppress the growth of MHC-I-deficient Hepa1-6 tumors, with CD4+ T cells appearing essential for maintaining of the tumoricidal phenotype of monocytes/macrophages and for recruiting NK cells and monocytes/macrophages. Moreover, the differential infiltration of CD4+ T cells was attributed to the upregulation of CXCL16 expression in Hepa1-6 upon loss of MHC-I/B2M, and the downregulation of CXCL16 due to MHC-I deficiency in other tumor models. CXCL16 exhibited a potent antitumor effect on MHC-I-deficient tumors by recruiting CD4+ T cells. In summary, CXCL16-driven CD4+ T cells are central regulators of antitumor immunity against MHC-I-deficient tumors.
Published in
CXCL16-driven CD4(+) T cells orchestrate immunosurveillance against MHC-I-deficient hepatocellular tumors
Zhou Y, Chen P, Wang Z et al. · Journal for immunotherapy of cancer 2026 · PMID 42120180 · doi:10.1136/jitc-2025-012347
This dataset
Open GSE303349 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 GSE303349_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 PRJNA1294882 and SRA study SRP602749. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42120180) ↗
Study design
7 conditions, mostly in triplicate
MC38 cells, sgB2m, ×3 MC38 cells, sgNT, ×3 LLC1 cells, sgB2m, ×3 AKR cells, sgB2m, ×3 AKR cells, sgNT, ×3 LLC1 cells, sgNT, ×2 Hepa1-6 cells , sgB2m, ×2 LLC1 cells, ssgNT, ×1 +4 more

Supports a between-group comparison across 19 samples.

How we read this: 7 replicated groups read from 24 sample titles; they account for 19 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
Datasets on a similar topic

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