← BioTransfer GEO Dataset Finder
GEO series

Rational design of immune gene therapy combinations via in vivo CRISPR activation screening of tumor microenvironment modulators

GSE255517 Mus musculus (mouse) RNA-seq (gene expression) 57 samples Submitted 2026/01/23 Platform GPL24676Platform GPL34328Platform GPL21103
At a glance

RNA-seq (gene expression) · mouse · 57 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
57 samples: 39 distinct titles, 8 of them repeatedGroups 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) remains a major challenge for cancer immunotherapy. Here, we performed TME-targeted in vivo CRISPR activation (CRISPRa) screen to identify factors that promote anti-tumor immunity, culminating in rationally designed immune gene therapy combinations. Multiplexed activation of genes encoding antigen presentation, T cell proliferation, co-stimulation, and migration (APCM) leads to enhanced anti-tumor responses.
Read the full summary ↓
An APCM-focused CRISPRa screen in metastatic tumors identified CD80, TNFSF14, CXCL10, TNFSF18, TNFSF9, and IFNG as top immunostimulatory candidates. Further optimization pinpointed TNFSF9 (4-1BBL) + IFNG + IL12B (4II) as a potent therapeutic combination. AAV-4II enhanced antigen presentation, T cell activation, proliferation, cytotoxicity, and tumor infiltration. Preconditioning the TME with AAV-4II synergized with CAR-T and TCR-T cell therapies to suppress primary and metastatic solid tumors in vivo. These findings establish TME-targeted CRISPRa screening as a rapid route to develop immune gene therapy combinations 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 GSE255517 in finder → Find more 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 GSE255517_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 PRJNA1075135 and SRA study SRP670083. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41747251) ↗
Study design
39 distinct titles, 8 of them repeated
4II ×5 E0771-LUNG ×5 Ifng ×3 Vec ×3 41bbl ×3 Il12b ×3 cellPool ×2 E0771 ×2

How we read this: Read from 57 sample titles: 39 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

+ 17 more — browse all 57 samples with per-sample file links →

Datasets on a similar topic

Search all RNA-seq datasets in GEO →

Share this dataset

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.