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Genomic stratification based on SMAD4 in pancreatic cancer reveals biological implications of aberrant TGFβ signaling and susceptibility to TGFβ inhibition

GSE296094 Mus musculus (mouse) RNA-seq (gene expression) 20 samples Submitted 2025/05/15 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 20 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
20 samples: 7 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
SMAD4 Deficiency Promotes Pancreatic Cancer Progression and Confers Susceptibility to TGFβ InhibitionCancer research · 2025
Summary (written by the submitters)
Only 13% of newly diagnosed pancreatic cancer cases are surgically resectable, emphasizing the importance of systemic therapy. Yet, the 5-year overall survival rate for pancreatic cancer remains ~13%, underscoring the urgent need for new treatment strategies. TGFβ is a promising target due to its significant involvement in the desmoplasia, immune suppression, and chemoresistance characteristic of pancreatic cancer.
Read the full summary ↓
Over 300 clinical trials targeting TGFβ have been conducted in unselected patient cohorts; however, none have gained FDA approval. Nevertheless, TGFβ blockade may hold promise for a subset of cancers with non-functional TGFβ signaling. Greater than 25% of pancreatic cancers carry mutations in SMAD4, a key component of canonical TGFβ signaling. In this study, we investigate the potential for stratifying patients based on SMAD4 mutational status to identify tumors susceptible to TGFβ inhibition. Analysis of SMAD4 expression in human pancreatic tumors reveals that SMAD4 mutation or loss is associated with worse disease-free survival. Intriguingly, we observe heterogeneity in intratumoral SMAD4 expression among human pancreatic cancer samples. To investigate the consequences of SMAD4 mutation, we employed SMAD4-deficient genetically engineered mouse models and orthotopic SMAD4 knockout models, which exhibited reduced survival, increased metastasis, and alterations in the tumor microenvironment compared to SMAD4 wildtype controls, consistent with gene and protein expression changes in the absence of functional SMAD4. Importantly, mice bearing SMAD4-deficient tumors treated with a blocking TGFβ antibody exhibited reduced tumor weight and improved survival. These findings suggest that targeting TGFβ signaling in TGFβ-deficient tumors has potential utility in pancreatic cancer.
Published in
SMAD4 Deficiency Promotes Pancreatic Cancer Progression and Confers Susceptibility to TGFβ Inhibition
Murimwa GZ, Williams NE, Alzhanova D et al. · Cancer research 2025 · PMID 40440097 · doi:10.1158/0008-5472.CAN-24-1970
This dataset
Open GSE296094 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 GSE296094_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 PRJNA1257770 and SRA study SRP582605. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40440097) ↗
Study design
7 conditions, mostly in triplicate
6620c1, empty vector controrol, untreated, ×3 6620c1, empty vector controrol, TGFb-treated, ×3 6620c1, Smad4KO,untreated, ×3 6620c1, Smad4 KO, TGFb-treated, ×3 BMFA3v1, empty vector controrol, untreated, ×2 BMFA3v1, empty vector controrol, TGFb-treated, ×2 BMFA3v1, Smad4KO,untreated, ×2 BMFA3v1, Smad4 KO, TGFb-treated, replicate ×1 +1 more

Supports a between-group comparison across 18 samples.

How we read this: 7 replicated groups read from 20 sample titles; they account for 18 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
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