GEO series

Acquired resistance to immunotherapy by physical barriers with cancer cell–expressing collagens in non–small cell lung cancer

GSE261898 Mus musculus (mouse) RNA-seq (gene expression) 14 samples Submitted 2025/02/01 Platform GPL24247

At a glance

RNA-seq (gene expression) · mouse · 14 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: 14 samples: 14 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)

Drug resistance is the principal challenge of cancer therapies, including recently developed immunotherapy. More and more popular use of immunotherapy, especially treatments with immune checkpoint inhibitors (ICIs), witnesses explosively increasing cases of both primary and acquired immunotherapy resistance. While primary resistance has been extensively studied, mechanisms underlying acquired resistance of immunotherapy are less understood.

Read the full summary ↓

Here we reported that tumor cells could develop acquired resistance to ICI treatment through self-built collagen-containing physical barriers in non-small cell lung cancer (NSCLC). We found that tumor cells expressed high levels of multiple collagen genes, including COL3A1 and COL6A1, and were fully covered with collagen fibers. COL3A1 formed a castle-like structure of a cluster of tumor cells and prevented the infiltration of T cells, while COL6A1 seemed to be an armor-like structure of each tumor cell and protected them from attack by cytotoxic T cells. Genetic or pharmaceutic disruption of these collagens, by warfarin, a commonly used medicine, significantly reversed the acquired resistance. Thus, our data reveal an unprecedented tumor cell-intrinsic mechanism, mediated by collagen-containing physical barriers, of acquired immunotherapy resistance, which immediately suggests a treatment option for patients.

This dataset

Open GSE261898 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 GSE261898_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 PRJNA1089539 and SRA study SRP496401. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

14 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 14 sample titles: 14 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

GSM8153772 BaseLine1_scRNA
GSM8153773 BaseLine2_scRNA
GSM8153774 Resp_ICI1_scRNA
GSM8153775 Resp_ICI2_scRNA
GSM8153776 Resis_Ctrl1_scRNA
GSM8153777 Resis_Ctrl2_scRNA
GSM8153778 Resis_ICI1_scRNA
GSM8153779 Resis_ICI2_scRNA
GSM8153780 dKO_Veh1_scRNA
GSM8153781 dKO_Veh2__scRNA
GSM8153782 dKO_ICI1_scRNA
GSM8153783 dKO_ICI2_scRNA
GSM8153784 Resis_veh1_scRNA
GSM8153785 Resis_Warfarin1_scRNA

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Search all mouse RNA-seq datasets in GEO →

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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.