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Targeting CD11b+ regulatory B cells via RAS/RAF/PI3K pathway inhibition augments response to immune checkpoint blockade or CD40 agonism in metastatic melanoma

GSE291447 Mus musculus (mouse) Single-cell RNA-seq 60 samples Submitted 2025/10/23 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 60 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
Mus musculus (mouse)
Samples
60 samples: 10 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
RAS/MEK/PI3K pathway inhibition augments response to CD40 agonism by targeting CD11b(+) Bregs thereby overcoming melanoma PD1-resistanceNature communications · 2026
Summary (written by the submitters)
Abstract Development of effective second-line treatment options for immune checkpoint blockade (ICB) resistant patients with BRAFWTNRASWT or BRAFWTNRASmut melanoma is crucial. While systemic delivery of agonist CD40 (aCD40) plus anti-PD1 (αPD1) showed activity in patients with ICB-resistant melanoma, the objective response rate was modest (15%), in part due to induction of B regulatory cells (Bregs) which suppress CD8+ effector T cells.
Read the full summary ↓
We previously reported that RAS/RAF/PI3K-inhibition elevated CD40 expression in melanoma cells and sensitized tumors to ICB. Here, we show that treatment of ICB-resistant BRAFWTNRASWT and BRAFWTNRASmut melanoma tumors with the combination of rigosertib (RGS), a RAS/PI3K/AKT-pathway inhibitor, and/or trametinib (T), a MEK1/2 inhibitor, plus aCD40 overcomes ICB resistance. Overexpression of CD40 in melanoma cells effectively reverses ICB-resistance and induces tumor regression upon aCD40+αPD1 treatment. Mechanistically, RGS+T suppressed aCD40-associated CD11b+PD-L1+ B regulatory cells (B11regs), which promoted CD8+ T-cell killing in melanoma. scRNA-Seq analyses confirmed CD40-associated B11regs across cancer types in patients. Statement of Significance Addition of aCD40 treatment to PD1 fails to overcome ICB resistance due to aCD40-induction of B11regs and low expression of CD40 in melanoma cells. Our data demonstrate that addition of RAS/RAF/PI3K inhibitors to aCD40 resolves these issues and provides alternative therapeutic options for ICB-resistant BRAFWTNRASWT or BRAFWTNRASmut metastatic melanoma.
Published in
RAS/MEK/PI3K pathway inhibition augments response to CD40 agonism by targeting CD11b(+) Bregs thereby overcoming melanoma PD1-resistance
Yan C, Luo W, Yang J et al. · Nature communications 2026 · PMID 41526341 · doi:10.1038/s41467-025-67315-1
This dataset
Open GSE291447 in finder → Find more mouse 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 GSE291447_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 PRJNA1233776 and SRA study SRP568962. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41526341) ↗
Study design
10 conditions, mostly in triplicate
1014 Virol ctl, Vehicle+IgG# ×3 1014 CD40-OE, Vehicle+IgG# ×3 1014 Virol ctl, Vehicle+aCD40# ×3 1014 CD40-OE, Vehicle+aCD40# ×3 1014 Virol ctl, Vehicle+PD1# ×3 1014 CD40-OE, Vehicle+PD1# ×3 1014 Virol ctl, aCD40+PD1# ×3 1014 CD40-OE, aCD40+PD1# ×3 +32 more

Supports a between-group comparison across 30 samples.

How we read this: 10 replicated groups read from 60 sample titles; they account for 30 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

+ 20 more — browse all 60 samples with per-sample file links →

Datasets on a similar topic

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