GEO series

CD137 agonism enhances anti-PD1 induced activation of clonally expanded CD8+ T cells in a neoadjuvant pancreatic cancer clinical trial

GSE279781 Homo sapiens (human) Single-cell RNA-seq 30 samples Submitted 2024/12/16 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 30 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: Homo sapiens (human)
Topic: Pancreatic cancerThis series is part of our curated briefing on it.
Samples: 30 samples: 30 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: CD137 agonism enhances anti-PD1 induced activation of expanded CD8(+) T cell clones in a neoadjuvant pancreatic cancer clinical trialiScience · 2025

Summary (written by the submitters)

Successful pancreatic ductal adenocarcinoma (PDAC) immunotherapy requires therapeutic combinations that induce quality T cells. Tumor microenvironment (TME) analysis following therapeutic interventions can identify response mechanisms to guide design of more effective combinations.

Read the full summary ↓

We provide a reference single-cell dataset from PDAC-infiltrating T cell and monocyte subsets from a human neoadjuvant clinical trial comparing the granulocyte-macrophage colony-stimulating factor (GM-CSF)-secreting allogeneic PDAC vaccine GVAX alone, in combination with anti-PD1, or with both anti-PD1 and a CD137 agonist. Tumor infiltrating leukocytes analyzed two weeks post neoadjuvant therapy revealed shifts in CD8+ T cell activation as well as cytoskeletal and extracellular matrix (ECM)-interacting components with Cy/GVAX and anti-PD1. Addition of a CD137 agonist was associated with increased abundance of activated and clonally expanded CD8+ T cells with distinct perturbations in cytoskeletal and ECM components. Furthermore, a new computational method to compare ligand-receptor networks between patients from different treatment arms found that CD137 agonism is associated with immunosuppressive TREM2 signaling in tumor associated macrophages (TAMs) that corresponds to changes in cell metabolism, function, and ECM interactions. Altogether these findings associate therapy with GVAX, anti-PD1, and CD137 agonist with enhanced CD8+ T cell function while inducing alternative immunosuppressive pathways in patients with PDAC.

Published in

CD137 agonism enhances anti-PD1 induced activation of expanded CD8(+) T cell clones in a neoadjuvant pancreatic cancer clinical trial

Montagne JM, Mitchell JT, Tandurella JA et al. · iScience 2025 · PMID 39811671 · doi:10.1016/j.isci.2024.111569

This dataset

Open GSE279781 in finder → Find more human RNA-seq datasets → Pancreatic cancer landscape →

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 GSE279781_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 PRJNA1174340. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39811671) ↗

Study design

30 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 30 sample titles: 30 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

GSM8580060 14_P-45_TIL (scTCR-seq)
GSM8580061 11_P-44_TIL (scTCR-seq)
GSM8580062 12_P-2_TIL (scTCR-seq)
GSM8580063 13_P-42_TIL (scTCR-seq)
GSM8580064 3_P-6_TIL (scTCR-seq)
GSM8580065 4_P-20_TIL (scTCR-seq)
GSM8580066 5_P-33_TIL (scTCR-seq)
GSM8580067 1_P-24_TIL (scTCR-seq)
GSM8580068 10_P-32_TIL (scTCR-seq)
GSM8580069 2_P-17_TIL (scTCR-seq)
GSM8580070 6_P-3_TIL (scTCR-seq)
GSM8580071 7_P-25_TIL (scTCR-seq)
GSM8580072 8_P-23_TIL (scTCR-seq)
GSM8580073 15_P-46_TIL (scTCR-seq)
GSM8580074 16_P-40_TIL (scTCR-seq)
GSM8580075 14_P-45_TIL (scRNA-seq)
GSM8580076 11_P-44_TIL (scRNA-seq)
GSM8580077 12_P-2_TIL (scRNA-seq)
GSM8580078 13_P-42_TIL (scRNA-seq)
GSM8580079 3_P-6_TIL (scRNA-seq)
GSM8580080 4_P-20_TIL (scRNA-seq)
GSM8580081 5_P-33_TIL (scRNA-seq)
GSM8580082 1_P-24_TIL (scRNA-seq)
GSM8580083 10_P-32_TIL (scRNA-seq)
GSM8580084 2_P-17_TIL (scRNA-seq)
GSM8580085 6_P-3_TIL (scRNA-seq)
GSM8580086 7_P-25_TIL (scRNA-seq)
GSM8580087 8_P-23_TIL (scRNA-seq)
GSM8580088 15_P-46_TIL (scRNA-seq)
GSM8580089 16_P-40_TIL (scRNA-seq)

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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.