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Coordinated immune activation following KRAS inhibition reveals molecular pathways that potentiate and limit anti-tumor immunity

GSE322775 Mus musculus (mouse) Single-cell RNA-seq 47 samples Submitted 2026/06/27 Platform GPL34328
At a glance

Single-cell RNA-seq · mouse · 47 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
47 samples: 8 conditions, mostly with about 5 replicates eachGroups 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.
Summary (written by the submitters)
While mutant-specific KRAS inhibitors are approved to treat cancer, a deeper understanding of intratumoral changes driven specifically by KRAS inhibition is needed to maximize therapeutic responses. Here we apply single-cell RNA-seq, flow cytometry, and spatial transcriptomics to distinguish mechanisms of tumor control after KRASG12C inhibition (KRAS(G12C)i), MEK inhibition (MEKi) and following combination therapy with KRAS(G12C)i and PD-1 blockade.
Read the full summary ↓
Despite inhibiting overlapping pathways, KRAS(G12C)i drives the adaptation of distinct neoplastic cell fates affecting metabolism and cell cycle regulation, leading to synergistic tumor suppression after co-administration with MEKi. In turn, this modulates intercellular communication patterns and induces robust dendritic cell maturation and PD-1+ macrophage activation mediated through non-immune mediators. Both KRAS(G12C)i and MEKi elicit a similar magnitude of cytotoxic T-cell infiltration despite a reduced capacity for T-cell proliferation after MEKi, implicating distinct adaptive immune activation mechanisms. Combination treatment of KRAS(G12C)i with anti-PD-1 overcomes immune activation barriers by prolonging the activation window and clonal persistence for T cells and by promoting the re-wiring of pro-inflammatory macrophages associated with higher survival, Furthermore, combination treatment amplifies intercellular communication among non-PD-1+ expressing cells to perpetuate dendritic cell activation. Our findings connect neoplastic KRAS inhibition with the coordination of distinct levers for immune activation and reveal pro- and anti-tumor mechanisms that can be modulated following specific treatment types to contain malignant growth.
This dataset
Open GSE322775 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 GSE322775_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 PRJNA1431466 and SRA study SRP680700. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
8 conditions, mostly with about 5 replicates each
dendritic_veh sample ×12 dendritic_AMG510 sample ×6 dendritic_MEKi_3 sample ×6 CD45_veh sample ×5 CD45_AMG510 sample ×5 CD45_MEKi_3 sample ×5 CD45_MEKi_10 sample ×5 dendritic_MEKi_10 sample ×3

Supports a between-group comparison across 47 samples.

How we read this: 8 replicated groups read from 47 sample titles; they account for 47 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

+ 7 more — browse all 47 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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