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Reversing myeloid-derived suppressor cells mediated immunosuppression via p38α inhibition enhances immunotherapy efficacy

GSE228550 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2025/12/02 Platform GPL24247Platform GPL24676
At a glance

RNA-seq (gene expression) · mouse · 12 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
12 samples: 3 × MDA-MB-231_DMSO vs 3 × MDA-MB-231_Ponatinib vs 3 × 4T1_DMSO vs 3 × 4T1_PonatinibGroups 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
Gene Signature-Based Drug Screening Reveals Ponatinib Enhances Immunotherapy Efficacy in Triple-Negative Breast Cancer by Reversing MDSC-Mediated Immunosuppressive Tumor MicroenvironmentResearch (Washington, D.C.) · 2025
Summary (written by the submitters)
By combining the tumor immunological phenotype (TIP) gene signature and high throughput sequencing based high throughput screening (HTS2), we identified that ponatinib significantly inhibits the expression of “cold” tumor associated chemokines CXCL1 and CXCL2 in cancer cells. To investigate how ponatinib regulates the expression of “cold”-tumor related genes CXCL1 and CXCL2, we conducted RNA-seq study to examine transcriptome-wide effects of ponatinib with both MDA-MB-231 cells and 4T1 cells.
Published in
Gene Signature-Based Drug Screening Reveals Ponatinib Enhances Immunotherapy Efficacy in Triple-Negative Breast Cancer by Reversing MDSC-Mediated Immunosuppressive Tumor Microenvironment
Wang Q, Li S, Wu Y et al. · Research (Washington, D.C.) 2025 · PMID 41079672 · doi:10.34133/research.0915
This dataset
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.

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 GSE228550_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA950332 and SRA study SRP430118. Searching any of these in the dataset finder brings you back here.

Study design
3 × MDA-MB-231_DMSO vs 3 × MDA-MB-231_Ponatinib vs 3 × 4T1_DMSO vs 3 × 4T1_Ponatinib

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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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