GEO series

Multi-omics analysis identifies intrinsic Trp53 driven metastatic breast cancer subtypes [RNA-seq]

GSE301299 Mus musculus (mouse) RNA-seq (gene expression) 51 samples Submitted 2025/12/19 Platform GPL24247

At a glance

RNA-seq (gene expression) · mouse · 51 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: 51 samples: 51 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)

Metastatic breast cancer is the leading cause of death amongst patients that develop breast cancer. The molecular drivers of metastatic breast cancer are largely unknown. Treatment of metastatic breast cancer is limited to systemic cytotoxic chemotherapies, which often deteriorate patient quality of life, and tumors often become refractory, resulting in incurable disease.

Read the full summary ↓

The most frequently mutated gene associated with metastatic breast cancer is TP53. The TP53R248W hotspot missense mutation is associated with poor prognosis in breast cancer. To understand the intrinsic transcriptomic and genomic landscape of metastatic breast cancer driven by mutant p53, we generated a somatic mouse model driven by mammary epithelial specific expression of Trp53R248W. Primary tumors were highly metastatic and reflected the human molecular subtypes of luminal A, luminal B, HER2, and TNBC, with TNBC comprising the majority (79%) of these tumors. Transcriptomic profiling revealed primary tumor heterogeneity characterized by three intrinsic subtypes (1) stem-cell like; (2) well-differentiated metabolic like; and (3) immune suppressed. Stem-cell like tumors activate ribosome biosynthesis and E2F signaling. These tumors harbor amplifications in Met, Birc3, Yap1 and deletions in Nf1, Pik3r1, and Rad17. Well-differentiated metabolic like mammary tumors activate cytochrome P450 enzymes, estrogen signaling and branched chain amino acid degradation and harbor mutations in the Pten pathway. Immune suppressed tumors enrich for processes related to immune suppression. These tumors have high mutation burden, frequently mutate Traf7 and delete Cdkn2a. This is the most comprehensive transcriptomic and genomic profiling of mutant p53 driven breast tumors and sheds light on their heterogenous nature and distinct intrinsic programs that may inform targeted therapy for metastatic breast cancer.

This dataset

Open GSE301299 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 GSE301299_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 PRJNA1284770 and SRA study SRP596592. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

51 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 51 sample titles: 51 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

GSM9080193 YZ-14 RNA-seq
GSM9080194 YZ-17 RNA-seq
GSM9080195 YZ-20 RNA-seq
GSM9080196 YZ-24 RNA-seq
GSM9080197 YZ-4 RNA-seq
GSM9080198 JM1173-T1 RNA-seq
GSM9080199 JM1173-T2 RNA-seq
GSM9080200 JM1381 RNA-seq
GSM9080201 JM1558 RNA-seq
GSM9080202 JM1566-T1 RNA-seq
GSM9080203 JM566-T2 RNA-seq
GSM9080204 JM583 RNA-seq
GSM9080205 RM0008-T1-Cut 1 RNA-seq
GSM9080206 RM0008-T1-Cut 2 RNA-seq
GSM9080207 RM0058 RNA-seq
GSM9080208 RM247 RNA-seq
GSM9080209 RM249 RNA-seq
GSM9080210 RM0087 RNA-seq
GSM9080211 YZ-5 RNA-seq
GSM9080212 YZ-1 RNA-seq
GSM9080213 YZ-21 RNA-seq
GSM9080214 JM1275 RNA-seq
GSM9080215 JM590-T1 RNA-seq
GSM9080216 JM733 RNA-seq
GSM9080217 JM885 RNA-seq
GSM9080218 JM1039 RNA-seq
GSM9080219 JM1193 RNA-seq
GSM9080220 JM714 RNA-seq
GSM9080221 JM1491-T1 RNA-seq
GSM9080222 JM1491-T2 RNA-seq
GSM9080223 JM1262 RNA-seq
GSM9080224 JM1384 RNA-seq
GSM9080225 JM1561 RNA-seq
GSM9080226 JM594 RNA-seq
GSM9080227 JM596 RNA-seq
GSM9080228 JM747 RNA-seq
GSM9080229 JM756 RNA-seq
GSM9080230 JM884 RNA-seq
GSM9080231 JM964 RNA-seq
GSM9080232 JM967 RNA-seq

+ 11 more — browse all 51 samples with per-sample file links →

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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.