GEO series

Tumor suppressors in Sox2-mediated lung cancers promote distinct cell intrinsic and immunologic remodeling [scRNA-seq]

GSE295824 Mus musculus (mouse) Single-cell RNA-seq 16 samples Submitted 2025/05/20 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 16 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: 16 samples: 4 × Tumor, LN2A, vs 4 × Tumor, p53, vs 4 × Tumor, PTEN, vs 4 × Tumor, SH,Groups 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)

Non-small cell lung cancer (NSCLC) largely consists of lung squamous (LUSC) and lung adenocarcinoma (LUAD). While driver mutations are common in LUAD, LUSC has few if any actionable targetable drivers. Despite these distinct differences, alterations in the p53 and Pten tumor suppressors are common in both subtypes. While Sox2 is widely considered an LUSC proto-oncogene, the relationship of p53 and Pten with Sox2 is poorly understood.

Read the full summary ↓

To evaluate the role of these tumor suppressors in Sox2-mediated NSCLC models on disease progression and the tumor microenvironment (TME), we deleted Trp53 or Pten in a C57BL/6J-Sox2hi, Nkx2-1-/-, Lkb1-/- (SNL) genetic background. In parallel, we generated a highly metastatic LUSC cell line (LN2A; derived from a Sox2high mouse model, followed by p53, Pten, and Cdkn2a deletion) that also grows in C57BL/6 mice. Histologic and single cell RNAseq analyses corroborated that SNL mice developed mixed tumors with both LUAD and LUSC histopathology while SNL-Trp53 and SNL-Pten mice developed LUAD and implanted LN2A tumors retained LUSC morphology. Compared with SNL mice, additional loss of p53 or Pten resulted in significantly reduced survival, increased tumor burden and altered tumor mucin composition. As the metastatic potential in the tumor models progressed from the least (SNL) to most (LN2A), we observed a progressive increase in cellular infiltrate, most notably high levels of recruited tumor-associated inflammatory monocytes (TIMs). We identified a sub-cluster of CD38+ TIMs in the LN2A model that significantly enriched for activation of the classical and alternative complement pathways. Importantly, Complement Factor B (Cfb) is associated with poor survival in LUSC patients, and we observed the LN2A model had significantly improved survival on a Cfb-/- background. Taken together, our findings demonstrate a cooperative role of p53 and Pten tumor suppressors in Sox2-mediated NSCLC tumor progression, mucin production, and remodeling of the immune TME.

This dataset

Open GSE295824 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 GSE295824_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 PRJNA1256447 and SRA study SRP581836. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

4 × Tumor, LN2A, vs 4 × Tumor, p53, vs 4 × Tumor, PTEN, vs 4 × Tumor, SH,

Supports a between-group comparison across 16 samples.

How we read this: 4 replicated groups read from 16 sample titles; they account for 16 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

GSM8958231 Tumor, LN2A, rep 1 Tumor, LN2A,
GSM8958232 Tumor, LN2A, rep 2 Tumor, LN2A,
GSM8958233 Tumor, LN2A, rep 3 Tumor, LN2A,
GSM8958234 Tumor, LN2A, rep 4 Tumor, LN2A,
GSM8958235 Tumor, p53, rep 1 Tumor, p53,
GSM8958236 Tumor, p53, rep 2 Tumor, p53,
GSM8958237 Tumor, p53, rep 3 Tumor, p53,
GSM8958238 Tumor, p53, rep 4 Tumor, p53,
GSM8958239 Tumor, PTEN, rep 1 Tumor, PTEN,
GSM8958240 Tumor, PTEN, rep 2 Tumor, PTEN,
GSM8958241 Tumor, PTEN, rep 3 Tumor, PTEN,
GSM8958242 Tumor, PTEN, rep 4 Tumor, PTEN,
GSM8958243 Tumor, SH, rep 1 Tumor, SH,
GSM8958244 Tumor, SH, rep 2 Tumor, SH,
GSM8958245 Tumor, SH, rep 3 Tumor, SH,
GSM8958246 Tumor, SH, rep 4 Tumor, SH,

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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.