← BioTransfer GEO Dataset Finder
GEO series

Characterization of a panel of mouse primary cell lines from Myc-driven mammary gland tumours and lung metastasis derived from them

GSE294562 Mus musculus (mouse) RNA-seq (gene expression) 45 samples Submitted 2026/04/14 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 45 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
45 samples: 15 conditions, mostly in triplicateGroups 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
Targeting Cystine Addiction Suppresses Breast Cancer Lung MetastasisCancer research · 2026
Summary (written by the submitters)
Triple negative breast cancer (TNBC) represents 15% of all breast cancer subtypes. It is the most aggressive type, with limited treatment options and is often associated with metastasis. TNBC are associated with high expression of an oncogene, c-Myc, driving metabolic to promote tumour growth and metastasis. To study the metabolic changes that allow cancer cells to become metastatic, we used MMTV-Myc mouse model (mouse mammary tumour virus – Myc) of mammary gland tumorigenesis to generate tumour cell lines either from primary mammary gland tumours or lung metastasis.
Read the full summary ↓
“MYAZ16.1e”, “MYAZ6.6g”, and “MYAZ12.3i” are cell lines generated from primary MMTV-Myc mammary gland tumours (the number represents the I.D. of a tumour-bearing mouse). These cells have low metastatic capacity. “Myc310” is also a tumour cell line generated from one of the MMTV-Myc tumours, but which has higher metastatic capacity and forms visible metastatic lesions in the lung. To generate models with high metastatic capacity, tumour cells were isolated from the lungs of mice bearing mammary gland tumours generated by ectopic injection of Myc310 cells into the fat pad. These cell lines were named “LM1”, “LM2” and “LM3”. LMs cells were further injected in the fat pads of mice to generate mammary gland tumours. LMs cells showed higher metastatic capacity than Myc310 cells. Tumour cell lines isolated from the lungs of mice bearing LM1, LM2 or LM3 mammary gland tumours were named “LM1-LM”, “LM2-LM”, “LM3-LM”. We have demonstrated that that the metastatic cell lines, Myc310, LM and LM-LM are more dependent on extracellular cysteine/cystine to survive than non-metastatic MYAZ cell lines. We therefore performed transcriptomics of some of the generated cell lines in control and cysteine/cystine deprived conditions. To generate a non-tumourigenic control for in vitro experiments and analysis, normal mammary gland epithelial cells were immortalized by ectopic expression of p53DN and E1A and named “iMMEC” cell line (immortalized mouse mammary epithelial cells). To model the event during acute induction of c-MYC overexpression, we generated the “iMMEC+MYC”, iMMECs via transducing with a doxycycline inducible c-MYC construct.
Published in
Targeting Cystine Addiction Suppresses Breast Cancer Lung Metastasis
Tarragó-Celada J, Mahmood AS, Panina Y et al. · Cancer research 2026 · PMID 42412619 · doi:10.1158/0008-5472.CAN-25-3883
This dataset
Open GSE294562 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 GSE294562_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 PRJNA1250506 and SRA study SRP578539. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42412619) ↗
Study design
15 conditions, mostly in triplicate
Cell line, LM1_LM, Control, ×3 Cell line, LM1_LM, Cys deprived, ×3 Cell line, LM2_LM, Control, ×3 Cell line, LM3_LM, Control, ×3 Cell line, LM1, Control, ×3 Cell line, LM1, Cys deprived, ×3 Cell line, LM2, Control, ×3 Cell line, LM3, Control, ×3 +8 more

Supports a between-group comparison across 44 samples.

How we read this: 15 replicated groups read from 45 sample titles; they account for 44 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

+ 5 more — browse all 45 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.