GEO series

KEAP1-mutant lung cancers weaken anti-tumor immunity and promote an M2-like macrophage phenotype.

GSE250166 Mus musculus (mouse) Single-cell RNA-seq 15 samples Submitted 2024/03/20 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 15 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: 15 samples: 15 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: KEAP1-Mutant Lung Cancers Weaken Anti-Tumor Immunity and Promote an M2-like Macrophage PhenotypeInternational journal of molecular sciences · 2024

Summary (written by the submitters)

Considerable advances have been made in lung cancer therapies, but there is still an unmet clinical need to improve survival for lung cancer patients. Immunotherapies have improved survival, although only 20-30% of patients respond to these treatments. Interestingly, cancers with mutations in KEAP1, the negative regulator of the NRF2 cytoprotective pathway, are resistant to immune checkpoint inhibition and correlate with decreased immune cell infiltration.

Read the full summary ↓

NRF2 is known for promoting an anti-inflammatory phenotype when activated in immune cells, but the study of NRF2 activation in cancer cells has not been adequately assessed. The objective of this study was to determine how lung cancer cells with constitutive NRF2 activity interact with the immune microenvironment to promote cancer progression. To assess this, we generated CRISPR-edited mouse lung cancer cell lines by knocking out KEAP1 or NFE2L2 genes. We also utilized publicly available single cell data through the Gene Expression Omnibus to investigate tumor/immune cell interactions. We show here that KEAP1-mutant cancers promote immunosuppression of the tumor microenvironment. Our data suggests KEAP1-deletion is sufficient to alter the secretion of cytokines, increase expression of immune checkpoint markers on cancer cells, and alter recruitment and differential polarization of immunosuppressive macrophages that ultimately lead to T cell suppression.

Published in

KEAP1-Mutant Lung Cancers Weaken Anti-Tumor Immunity and Promote an M2-like Macrophage Phenotype

Occhiuto CJ, Liby KT · International journal of molecular sciences 2024 · PMID 38542481 · doi:10.3390/ijms25063510

This dataset

Open GSE250166 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 GSE250166_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 PRJNA1052294 and SRA study SRP477880. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38542481) ↗

Study design

15 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 15 sample titles: 15 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

GSM7974911 LL2_WT.1 [aWT.1]
GSM7974912 LL2_WT.3 [aWT.3]
GSM7974913 LL2_WT.2 [aWT.2]
GSM7974914 LL2_KEAPKO3.2 [K3F10.2]
GSM7974915 LL2_KEAPKO3.3 [K3F10.3]
GSM7974916 LL2_KEAPKO3.1 [K3F10.1]
GSM7974917 LL2_KEAPKO2.2 [K3A3.2]
GSM7974918 LL2_KEAPKO2.3 [K3A3.3]
GSM7974919 LL2_KEAPKO2.1 [K3A3.1]
GSM7974920 LL2_KEAPKO1.3 [K2B12.3]
GSM7974921 LL2_KEAPKO1.2 [K2B12.2]
GSM7974922 LL2_KEAPKO1.1 [K2B12.1]
GSM7974923 LL2_NRF2KO.3 [N1D8.3]
GSM7974924 LL2_NRF2KO.2 [N1D8.2]
GSM7974925 LL2_NRF2KO.1 [N1D8.1]

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