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Polyclonal interactions overcome fitness barriers in Apc-driven intestinal tumorigenesis

GSE272850 Mus musculus (mouse) RNA-seq (gene expression) 63 samples Submitted 2024/08/30 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 63 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
63 samples: 42 distinct titles, 2 of them repeatedGroups 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
Polyclonality overcomes fitness barriers in Apc-driven tumorigenesisNature · 2024
Summary (written by the submitters)
Loss-of-function mutations in the tumour suppressor APC are an initial step in intestinal tumorigenesis. APC-mutant intestinal stem cells (ISCs) outcompete their wild type neighbours through the secretion of Wnt antagonists, accelerating the fixation and subsequent rapid clonal expansion of mutants. Reports of polyclonal intestinal tumours in patients and mouse models appear at odds with this process.
Read the full summary ↓
Here we combine multicolour lineage tracing with chemical mutagenesis in mice to show that a large proportion of intestinal tumours have a multiancestral origin. Polyclonal tumours retain a structure comprising subclones with distinct Apc mutations and transcriptional states, driven predominantly by differences in KRAS and MYC signalling. These pathway level changes are accompanied by profound cancer stem cell phenotypic differences. Importantly, these findings are confirmed by introducing an oncogenic Kras mutation that results in predominantly monoclonal tumour formation. Further, polyclonal tumours have accelerated growth dynamics suggesting a link between polyclonality and tumour progression. Together, these findings demonstrate the role of interclonal interactions in promoting tumorigenesis through non-cell autonomous pathways dependent on the differential activation of oncogenic pathways between clones.
Published in
Polyclonality overcomes fitness barriers in Apc-driven tumorigenesis
Sadien ID, Adler S, Mehmed S et al. · Nature 2024 · PMID 39478206 · doi:10.1038/s41586-024-08053-0
This dataset
Open GSE272850 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 GSE272850_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 PRJNA1139121 and SRA study SRP521595. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39478206) ↗
Study design
42 distinct titles, 2 of them repeated
dwaj1.3a ×13 dwba2.2d ×10

How we read this: Read from 63 sample titles: 42 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

+ 23 more — browse all 63 samples with per-sample file links →

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.