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Regulatory T cells Restrain CD4 T Cell Control of MHC-I-Deficient Metastatic Pancreatic Cancer Emerging After PD-L1 Blockade [scRNA-seq]

GSE330011 Mus musculus (mouse) Single-cell RNA-seq 2 samples Submitted 2026/06/26 Platform GPL34328
At a glance

Single-cell RNA-seq · mouse · 2 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Mus musculus (mouse)
Samples
2 samples
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)
We investigate mechanisms of immunotherapy resistance in pancreatic cancer. PD-L1 blockade selects for metastatic tumor variants with defective IFNγ-inducible MHC-I due to epigenetic silencing of Tap1. Restoring MHC-I limits primary tumor growth but fails to prevent metastasis. In contrast, regulatory T cell (TREG) depletion suppresses metastasis by unleashing CD4 T helper cells.
Read the full summary ↓
Adoptive transfer of tumor-specific CD4 T cells impairs metastatic progression. Tumor-specific CD4 T cells in lymph nodes preferentially adopt a TCF1⁺SLAMF6⁺ precursor state. Intratumoral TREG targeting with anti–CTLA-4 abrogates metastasis, drives accumulation of stem-like, tissue-resident helper CD4 T cells, producing a gene signature associated with improved patient outcomes. Combined CTLA-4 blockade and MHC-I restoration eliminates metastasis and prolongs animal survival. In human tumors, TREG and CD4 helper T cells co-localize and correlate with both tumor MHC-I expression and overall survival. Together, these findings identify actionable mechanisms of immune evasion and metastasis in immunotherapy-resistant cancer.
This dataset
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.

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 GSE330011_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1462379 and SRA study SRP697751. Searching any of these in the dataset finder brings you back here.

Samples in this study

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