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Molecular mechanisms of PD-1 immune checkpoint inhibitors in mouse mammary tumours

GSE268752 Mus musculus (mouse) RNA-seq (gene expression) 20 samples Submitted 2024/08/01 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: 5 × Control vs 5 × Control + anti-PD-1 vs 5 × Letrozole vs 5 × Letrozole + anti-PD-1Groups 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)
Understanding the molecular actions of anti-PD-1 immune checkpoint inhibitors in ER+ mouse mammary tumours is crucial to aid in understanding of how this treatment can be used in ER+ breast cancer. Here, we demonstrate that treatment of mouse mammary tumours with anti-PD-1 alone results in limited genome wide transcriptional changes.
Read the full summary ↓
Treatment with the endocrine therapy, letrozole, results in large genome wide transcriptional changes with predominant influence on oestrogen responsive genes and genes involved in cell cycle related processes. Further, we report that treatment with a combination of both letrozole and anti-PD-1 results again produced large genome wide transcriptional changes, all of which were observed upon letrozole treatment alone.
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 GSE268752_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 PRJNA1118676 and SRA study SRP510989. Searching any of these in the dataset finder brings you back here.

Study design
5 × Control vs 5 × Control + anti-PD-1 vs 5 × Letrozole vs 5 × Letrozole + anti-PD-1

Supports a between-group comparison across 20 samples.

How we read this: 4 replicated groups read from 20 sample titles; they account for 20 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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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.