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Rational design of immune gene therapy combinations via in vivo CRISPR activation screening of tumor microenvironment modulators [Bulk RNA-seq]

GSE255514 Mus musculus (mouse) RNA-seq (gene expression) 15 samples Submitted 2026/01/26 Platform GPL21103
At a glance

RNA-seq (gene expression) · mouse · 15 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
15 samples: 15 samples, each with a distinct title — no replicated groupsGroups 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
Rational Design of Immune Gene Therapy Combinations via In Vivo CRISPR Activation Screen of Tumor Microenvironment ModulatorsCancer discovery · 2026
Summary (written by the submitters)
The hostile tumor microenvironment (TME) is major challenge for cancer immunotherapies. Here, we design and perform TME-targeted in vivo CRISPR activation (CRISPRa) screens to uncover factors that promote anti-tumor immunity, culminating in rationally designed immune gene therapy combinations. Through adeno-associated virus (AAV) delivery, we find that multiplexed activation of pooled immunoregulatory genes encoding antigen presentation, cytokine, and co-stimulation molecules (APCM) leads to enhanced anti-tumor immunity.
Read the full summary ↓
A CRISPRa screen targeting APCM genes in metastatic tumors identifies CD80, TNFSF14, CXCL10, TNFSF18, TNFSF9, and IFNG as the top immunostimulatory candidates. Further optimization pinpoints 4-1BBL(TNFSF9) + IFNG + IL12B (4II) as a potent streamlined therapeutic combination. AAV delivered 4II has potent in vivo anti-tumor efficacy in multiple tumor models, by enhancing tumor antigen presentation while simultaneously promoting the infiltration, activation, and expansion of anti-tumor T cells. We further demonstrate that APCM therapy synergizes with CAR-T therapy against human solid tumors in vivo. CRISPRa screens and gene activation systems targeting APCM factors thus represent a powerful approach for rapid development of off-the-shelf immune gene therapies against solid tumors.
Published in
Rational Design of Immune Gene Therapy Combinations via In Vivo CRISPR Activation Screen of Tumor Microenvironment Modulators
Zhang F, Dong C, Chow RD et al. · Cancer discovery 2026 · PMID 41747251 · doi:10.1158/2159-8290.CD-25-0545
This dataset
Open GSE255514 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 GSE255514_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 PRJNA1075139 and SRA study SRP489197. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41747251) ↗
Study design
15 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 15 sample titles: 15 distinct titles with little repetition. 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
Datasets on a similar topic

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