← BioTransfer GEO Dataset Finder
GEO series

Single-cell multi-omics reveal coordinated neoplastic and immune remodeling following KRAS inhibition

GSE295974 Mus musculus (mouse) Single-cell RNA-seq 64 samples Submitted 2026/06/26 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 64 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Mus musculus (mouse)
Samples
64 samples: 64 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.
Summary (written by the submitters)
Allele-specific KRAS inhibitors are approved for KRAS-mutant cancers, and efforts to maximize patient responses are ongoing. Previous work suggests that KRAS inhibition can modulate anti-tumor immunity, but the critical cellular players and molecular pathways have not been clearly defined. Here we perform multi-omic profiling using single-cell RNA-seq, spatial transcriptomics, and flow cytometry to profile intratumoral changes after KRASG12C inhibition (KRAS(G12C)i), MEK inhibition (MEKi), and combination therapy with KRAS(G12C)i and PD-1 blockade.
Read the full summary ↓
Treatment with KRAS(G12C)i but not MEKi drove neoplastic adaptation toward increased oxidative stress, integrin signaling, and upregulation of extracellular matrix remodeling factors. These changes were accompanied by enhanced intercellular communication with classical dendritic cells, leading to robust cDC maturation and CD8 effector T-cell activation. Combination treatment with anti-PD-1 further expands T-cell responses, as well as macrophage states associated with increased cancer survival. Our findings connect KRAS inhibition with the coordination of subsequent immune activation and reveal molecular mechanisms that promote and sustain tumor control.
This dataset
Open GSE295974 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 GSE295974_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 PRJNA1256901 and SRA study SRP582230. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
64 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 64 sample titles: 64 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

+ 24 more — browse all 64 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

Share this dataset

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.