← BioTransfer GEO Dataset Finder
GEO series

Gene context drift identifies drug targets to mitigate cancer treatment resistance

GSE252995 Mus musculus (mouse) Single-cell RNA-seq 14 samples Submitted 2025/06/06 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 14 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
14 samples: 6 × FACS sorted tumor cells from combination (IR + AZD1390) treated tumors vs 3 × FACS sorted tumor cells from IR treated CPC tumors vs 3 × FACS sorted tumor cells from AZD1390 treated CPC tumors vs 2 × FACS sorted tumor cells from untreated CPC tumorsGroups 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
Gene context drift identifies drug targets to mitigate cancer treatment resistanceCancer cell · 2025
Summary (written by the submitters)
Cancer treatment often fails because combinations of different therapies evoke complex resistance mechanisms that are hard to predict. We introduce REsistance through COntext DRift (RECODR): a computational pipeline that combines co-expression graph networks of single-cell RNA sequencing profiles with a graph-embedding approach to measure changes in gene co-expression context during cancer treatment.
Read the full summary ↓
RECODR is based on the idea that gene co-expression context, rather than expression level alone, reveals important information about treatment resistance. Analysis of tumours treated in preclinical and clinical trials using RECODR unmasked resistance mechanisms–invisible to existing computational approaches–enabling the design of highly effective combination treatments for mice with choroid plexus carcinoma, and the prediction of potential new treatments for patients with medulloblastoma and triple negative breast cancer. Thus, RECODR may unravel the complexity of cancer treatment resistance by detecting context-specific changes in gene interactions that determine the resistant phenotype.
Published in
Gene context drift identifies drug targets to mitigate cancer treatment resistance
Jassim A, Nimmervoll BV, Terranova S et al. · Cancer cell 2025 · PMID 40578362 · doi:10.1016/j.ccell.2025.06.005
This dataset
Open GSE252995 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 GSE252995_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 PRJNA1063613 and SRA study SRP483206. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40578362) ↗
Study design
6 × FACS sorted tumor cells from combination (IR + AZD1390) treated tumors vs 3 × FACS sorted tumor cells from IR treated CPC tumors vs 3 × FACS sorted tumor cells from AZD1390 treated CPC tumors vs 2 × FACS sorted tumor cells from untreated CPC tumors

Supports a between-group comparison across 14 samples.

How we read this: 4 replicated groups read from 14 sample titles; they account for 14 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
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.