GEO series

In vivo CRISPR screens identify Mga as an immunotherapy target in triple-negative breast cancer

GSE249759 Mus musculus (mouse) Single-cell RNA-seq 12 samples Submitted 2024/09/06 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 12 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism: Mus musculus (mouse)
Samples: 12 samples: 3 × WT vs 3 × WT_IFN-g vs 3 × Mga_KO vs 3 × Mga_KO_IFN-gGroups 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: In vivo CRISPR screens identify Mga as an immunotherapy target in triple-negative breast cancerProceedings of the National Academy of Sciences of the United States of America · 2024

Summary (written by the submitters)

Immune evasion is not only critical for tumor initiation and progression, but also determines the efficacy of immunotherapies. Through iterative in vivo CRISPR screens with seven syngeneic tumor models, we identified core and context-dependent immune evasion pathways across cancer types. We also provided a high-confidence valuable dataset for the understanding of tumor intrinsic immunomodulators and the discovery of novel anti-cancer therapeutic targets.

Read the full summary ↓

With a focus on triple-negative breast cancer (TNBC), we found that Mga knock-out significantly enhances anti-tumor immunity and inhibits tumor growth. Transcriptomics and single-cell RNA sequencing analyses revealed that Mga functions through, at least partially, repression of MHC-II genes and related immune responses. Consistently, we observed that low MGA expression in breast cancer patients correlates with a favorable prognosis, particularly in those with active interferon-g signaling. Our findings provide new insights into tumor immune escape and pave the way for further exploration of MGA inhibition for clinical benefits in triple-negative breast cancer.

Published in

In vivo CRISPR screens identify Mga as an immunotherapy target in triple-negative breast cancer

Feng X, Yang C, Huang Y et al. · Proceedings of the National Academy of Sciences of the United States of America 2024 · PMID 39298484 · doi:10.1073/pnas.2406325121

This dataset

Open GSE249759 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 GSE249759_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1050296 and SRA study SRP476737. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39298484) ↗

Study design

3 × WT vs 3 × WT_IFN-g vs 3 × Mga_KO vs 3 × Mga_KO_IFN-g

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

GSM7963787 WT_1 WT
GSM7963788 WT_2 WT
GSM7963789 WT_3 WT
GSM7963790 WT_IFN-g_1 WT_IFN-g
GSM7963791 WT_IFN-g_2 WT_IFN-g
GSM7963792 WT_IFN-g_3 WT_IFN-g
GSM7963793 Mga_KO_1 Mga_KO
GSM7963794 Mga_KO_2 Mga_KO
GSM7963795 Mga_KO_3 Mga_KO
GSM7963796 Mga_KO_IFN-g_1 Mga_KO_IFN-g
GSM7963797 Mga_KO_IFN-g_2 Mga_KO_IFN-g
GSM7963798 Mga_KO_IFN-g_3 Mga_KO_IFN-g

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