← BioTransfer GEO Dataset Finder
GEO series

Large-scale characterisation of cell line xenografts identifies SMAD4 as key regulator of breast cancer morphology and aggressiveness

GSE263474 Homo sapiens (human) RNA-seq (gene expression) 39 samples Submitted 2025/04/03 Platform GPL16791
At a glance

RNA-seq (gene expression) · human · 39 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
Homo sapiens (human)
Topic
Breast cancerThis series is part of our curated briefing on it.
Samples
39 samples: 39 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)
Breast cancer (BC) is a complex and heterogeneous disease with diverse molecular subtypes, tumour growth dynamics, varying morphologies and high metastatic potential. Many advances have progressed our understanding of the disease, yet fundamental understanding of what makes breast cancer an aggressive entity is lacking. While the majority of available studies rely on a limited set of models, failing to recapitulate the full spectrum of breast cancer, an extensive resource of well characterized xenograft models may aid in gaining solid and reproducible insights into BC biology.
Read the full summary ↓
In this study, we generated mouse models from 26 different human BC cell lines, each with unique characteristics, using mammary intraductal (MIND) and fat pad transplantation (FPT) methodologies. Our models faithfully recapitulate human disease and cover the full spectrum of disease progression, from in situ disease to metastatic growth, while retaining distinct growth characteristics and molecular subtype. Comprehensive pathological analysis revealed two distinct tumour growth morphologies: indolent vs aggressive. Transcriptomics analysis identified the TGF-beta pathway as a potential regulator of these two phenotypes. Strikingly, we were able to completely reverse aggressive nodular-growing tumours to a more indolent disease by knockout of SMAD4. This research provides a valuable resource of fully characterized in vivo cell line xenograft models and reveals novel insights into the factors driving BC tumour growth behaviour and aggressiveness. It therefore serves as a basis for understanding the underlying mechanisms of tumour development and progression and enables accurate choice of model for future studies.
This dataset
Open GSE263474 in finder → Find more human RNA-seq datasets → Breast cancer landscape →
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 GSE263474_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 PRJNA1097606 and SRA study SRP500346. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
39 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 39 sample titles: 39 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
More Breast cancer datasets

Search all human 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.