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4-1BB+ Tregs and inhibitory progenitor exhausted T cells confer resistance to anti-PD-L1 and anti-CTLA-4 combination therapy

GSE286827 Homo sapiens (human) Single-cell RNA-seq 87 samples Submitted 2025/09/02 Platform GPL20795
At a glance

Single-cell RNA-seq · human · 87 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)
Samples
87 samples: 87 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
4-1BB(+) Tregs and inhibitory progenitor exhausted T cells confer resistance to anti-PD-L1 and anti-CTLA-4 combination therapyCell reports. Medicine · 2025
Summary (written by the submitters)
Predictors of immune checkpoint inhibitor response in cancer remain elusive. From a previous phase 2 neoadjuvant immunotherapy window-of-opportunity study, we present the single-cell RNA and T cell receptor (TCR) sequencing analysis of 57 pre- and post-treatment tumor biopsies from head and neck cancer patients treated with durvalumab (anti-PD-L1) alone or with tremelimumab (anti-CTLA-4), identifying key cellular and molecular predictors of immune checkpoint inhibitor (ICI) response.
Read the full summary ↓
Malignant cells and neutrophil senescence promote ICI response. While CXCL13+ exhausted T (Tex) cells enhance response through 4-1BB signaling, anti-CTLA-4 induces 4-1BB+ regulatory T cells (Tregs) restricting ICI efficacy. These opposing roles of 4-1BB in different cellular contexts may explain the limited benefit of combinatorial immunotherapy observed in clinical trials. We identify two subsets of tumor-reactive progenitor Tex (Tpex): ICI-responsive Tpex1 and ICI-resistant Tpex2, a subset characterized by KLRB1 and IL17R. The balance of Tpex1 and Tpex2 associates with ICI response across multiple cancers, offering insights into sustaining response. This study was registered at ClinicalTrials.gov (NCT03737968).
Published in
4-1BB(+) Tregs and inhibitory progenitor exhausted T cells confer resistance to anti-PD-L1 and anti-CTLA-4 combination therapy
Cha J, Kim CG, Sim NS et al. · Cell reports. Medicine 2025 · PMID 41045934 · doi:10.1016/j.xcrm.2025.102408
This dataset
Open GSE286827 in finder → Find more human 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 GSE286827_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 PRJNA1209997 and SRA study SRP557129. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41045934) ↗
Study design
87 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 87 sample titles: 87 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

+ 47 more — browse all 87 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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