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In vivo genome-wide CRISPR screens in human T cells to enhance T cell therapy for solid tumors

GSE330227 Homo sapiens (human) RNA-seq (gene expression) 30 samples Submitted 2026/06/15 Platform GPL34284
At a glance

RNA-seq (gene expression) · human · 30 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
Homo sapiens (human)
Samples
30 samples: 24 distinct titles, 6 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.
Summary (written by the submitters)
Large-scale CRISPR screening in human T cells holds significant promise for identifying genetic modifications that can enhance cellular immunotherapy. However, many genetic regulators of T cell performance in solid tumors may not be readily revealed in vitro. In vivo screening in tumor-bearing mice offers greater physiological relevance but has historically been limited by low intratumoral T cell recovery.
Read the full summary ↓
Here, we developed a new model system that achieves substantially higher human T cell recovery from tumors, enabling genome-wide in vivo screens with relatively small numbers of mice. Tumor-infiltrating T cells in this model exhibit hallmarks of dysfunction compared to matched splenic T cells, creating an ideal context for screening for genetic modifiers of T cell activity in the tumor microenvironment. Using this platform, we performed two genome-wide CRISPR knockout screens to identify genes regulating T cell intratumoral abundance and effector function, as measured by IFN-γ production. The intratumoral abundance screen uncovered the P2RY8-Gα13 GPCR signaling axis as a negative regulator of human T cell infiltration into tumors. The effector function screen identified GNAS as a key regulator of T cell dysfunction in tumors. Its protein product, Gαs, acts as a convergent signaling node downstream of multiple GPCRs that sense different suppressive ligands. Targeted GNAS knockout rendered T cells resistant to multiple suppressive cues and significantly improved therapeutic performance across diverse solid tumor models in both CAR and TCR systems. Moreover, combinatorial knockout of P2RY8 and GNAS further enhanced overall tumor control, demonstrating that complementary screens with distinct in vivo readouts can nominate gene targets addressing orthogonal barriers, and that combining such edits can improve therapeutic potency. This flexible and scalable platform can be adapted to diverse tumor models and screening modalities, enabling systematic discovery of genetic strategies to equip T cell therapies for solid tumors.
This dataset
Open GSE330227 in finder → Find more human 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 GSE330227_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 PRJNA1463189 and SRA study SRP698317. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
24 distinct titles, 6 of them repeated
Treat_D1_sg ×2 Treat_D2_sg ×2 Treat_D3_sg ×2 Untreat_D1_sg ×2 Untreat_D2_sg ×2 Untreat_D3_sg ×2

How we read this: Read from 30 sample titles: 24 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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