GEO series

Spatial genomics reveals cholesterol metabolism as a key factor in immunotherapy resistance in colorectal cancer [bulk RNA-seq]

GSE284264 Mus musculus (mouse) RNA-seq (gene expression) 16 samples Submitted 2025/03/10 Platform GPL24247

At a glance

RNA-seq (gene expression) · mouse · 16 samples

What it is: RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism: Mus musculus (mouse)
Samples: 16 samples: 16 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: Spatial genomics reveals cholesterol metabolism as a key factor in colorectal cancer immunotherapy resistanceFrontiers in oncology · 2025

Summary (written by the submitters)

Immune checkpoint inhibitors (ICIs) have transformed the treatment landscape across multiple cancer types achieving durable responses for a significant number of patients. Despite their success, many patients still fail to respond to ICIs or develop resistance soon after treatment. We sought to identify early treatment features associated with ICI outcome.

Read the full summary ↓

We leveraged the MC38 syngeneic tumor model because it has variable response to ICI therapy driven by tumor intrinsic heterogeneity. ICI response was assessed based on the level of immune cell infiltration into the tumor – a well-established clinical hallmark of ICI response. We generated a spatial atlas of 48,636 transcriptome-wide spots across 16 tumors using spatial transcriptomics; given the tumors were difficult to profile, we developed an enhanced transcriptome capture protocol yielding high quality spatial data. In total, we identified 8 tumor cell subsets (e.g., proliferative, inflamed, and vascularized) and 4 stroma subsets (e.g., immune and fibroblast). Each tumor had orthogonal histology and bulk-RNA sequencing data, which served to validate and benchmark observations from the spatial data. Our spatial atlas revealed that increased tumor cell cholesterol regulation, synthesis, and transport were associated with a lack of ICI response. Conversely, inflammation and T cell infiltration were associated with response. We further leveraged spatially aware gene expression analysis, to demonstrate that high cholesterol synthesis by tumor was associated with cytotoxic CD8 T cell exclusion. Finally, we demonstrate that bulk RNA-sequencing was able to detect immune correlates of response but lacked the sensitivity to detect cholesterol synthesis as a feature of resistance.

Published in

Spatial genomics reveals cholesterol metabolism as a key factor in colorectal cancer immunotherapy resistance

Kavran AJ, Bai Y, Rabe B et al. · Frontiers in oncology 2025 · PMID 40171265 · doi:10.3389/fonc.2025.1549237

This dataset

Open GSE284264 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 GSE284264_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1198112 and SRA study SRP551387. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40171265) ↗

Study design

16 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 16 sample titles: 16 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

GSM8681186 MC38_IgG_m01_bulk
GSM8681187 MC38_IgG_m02_bulk
GSM8681188 MC38_IgG_m03_bulk
GSM8681189 MC38_IgG_m04_bulk
GSM8681190 MC38_IgG_m05_bulk
GSM8681191 MC38_IgG_m06_bulk
GSM8681192 MC38_IgG_m07_bulk
GSM8681193 MC38_IgG_m08_bulk
GSM8681194 MC38_aPD1_NR_m09_bulk
GSM8681195 MC38_aPD1_NR_m10_bulk
GSM8681196 MC38_aPD1_NR_m11_bulk
GSM8681197 MC38_aPD1_NR_m12_bulk
GSM8681198 MC38_aPD1_NR_m13_bulk
GSM8681199 MC38_aPD1_NR_m14_bulk
GSM8681200 MC38_aPD1_R_m15_bulk
GSM8681201 MC38_aPD1_R_m16_bulk

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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.