GEO series

Tumor-derived RAC1A159V mutation promotes an immunosuppressive tumor microenvironment that represses response to immune checkpoint inhibitor [RNA-Seq]

GSE298273 Mus musculus (mouse) Single-cell RNA-seq 12 samples Submitted 2025/09/03 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 12 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: 12 samples: 12 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: Tumor-derived RAC1(A159V) mutation promotes an immunosuppressive microenvironment that represses response to immune checkpoint inhibitorScience advances · 2025

Summary (written by the submitters)

Immune checkpoint inhibitors that activate cytotoxic T lymphocytes to kill cancer cells have seen great success in therapies against various tumor types. However, the overall response rate in patients remains unacceptable at ~15%. RAC1, a member of Rho GTPase family, regulates multiple cellular processes in cancer, including vesicle trafficking, glycolysis, and exocytosis/endocytosis, which may contribute to tumor immune microenvironment modulation.

Read the full summary ↓

RAC1A159V is a hotspot mutation found in several cancer types including colon cancer and is associated with poor prognosis. In this study, we have generated MC38 RAC1A159V cells by using CRISPR/Cas9 and confirmed that RAC1A159V results in upregulated RAC1 activity and downstream PAK1 and mTOR signaling. We have found the RAC1A159V tumors grow at a similar rate as RAC1WT tumors in immunodeficient NSG mice but faster than RAC1WT tumors in syngeneic immunoproficient mice, and the RAC1A159V tumors are highly resistant to anti-PD1 treatment. Flow cytometry and scRNA-seq analyses reveal that RAC1A159V cells form “cold” tumors with an immunosuppressive microenvironment and suppressed tumor-immune cell interactions. Mechanistically, we show RAC1A159V upregulates glycosphingolipid biosynthesis to activate mTORC1 signaling in tumor cells, which in turn increases glycolysis, impairs key chemokine production, and decreases IFNGR1 expression of the tumor cells. mTORC1 inhibition re-sensitizes the RAC1A159V tumors to anti-PD1 treatment by reversing effects by RAC1A159V mutation on tumor cell glycolysis, chemokine production, and IFNGR1 expression. Our results demonstrate that RAC1A159V in tumors drives immune evasion via mTOR mediated glycolysis, chemotaxis, and IFN-γ response, and further suggest an approach of combining targeting of RAC1-mTOR signaling with immune checkpoint inhibitors for the treatment of immune-cold RAC1A159V tumors.

Published in

Tumor-derived RAC1(A159V) mutation promotes an immunosuppressive microenvironment that represses response to immune checkpoint inhibitor

Cai M, Adam M, Duan X et al. · Science advances 2025 · PMID 41160695 · doi:10.1126/sciadv.aea1212

This dataset

Open GSE298273 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 GSE298273_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 PRJNA1268780 and SRA study SRP588427. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41160695) ↗

Study design

12 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 12 sample titles: 12 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

GSM9011081 A159V-IgG-1, RNAseq
GSM9011082 A159V-IgG-2, RNAseq
GSM9011083 A159V-IgG-5, RNAseq
GSM9011084 A159V-aPD1-1, RNAseq
GSM9011085 A159V-aPD1-2, RNAseq
GSM9011086 A159V-aPD1-3, RNAseq
GSM9011087 WT-IgG-1, RNAseq
GSM9011088 WT-IgG-2, RNAseq
GSM9011089 WT-IgG-4, RNAseq
GSM9011090 WT-aPD1-1, RNAseq
GSM9011091 WT-aPD1-2, RNAseq
GSM9011092 WT-aPD1-3, RNAseq

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