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In vivo double knockout CAR-T screen identifies synergistic gene pairs that enhance anti-tumor immunity [bulk RNA-seq]

GSE272885 Homo sapiens (human) Single-cell RNA-seq 48 samples Submitted 2026/06/17 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 48 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
Homo sapiens (human)
Samples
48 samples: 16 conditions, mostly in triplicateGroups 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)
Chimeric antigen receptor (CAR) T cell therapy has revolutionized the treatment of hematological malignancies. However, its efficacy against solid tumors is hindered by multifaceted negative regulatory mechanisms intrinsic to T cells. Although single gene screens have been performed in T cells, such studies cannot provide causality for the complex genetic interactions governing T cell function.
Read the full summary ↓
To systematically discover genetic interactions that are critical for CAR-T cell’s anti-tumor immunity, here, we perform a high-throughput in vivo double-knockout (DKO) CRISPR screen in human CAR-T cells and identify multiple DKO gene pairs that are both effective and synergistic. All five top-scoring DKO hits are validated to promote CAR-T cell activation, cytotoxicity, degranulation, effector cytokine production, and memory formation, and reduce exhaustion. Mechanistically, these DKOs enhance calcium influx, phospho-ERK, NF-κB and NFAT/AP-1 signaling. Among these hits, NR4A1_SOCS3 DKO has multiple favorable immunological features and showcases the most robust phenotypes. NR4A1_SOCS3 DKO CAR-T cells show potent in vivo anti-tumor efficacy and markedly enhanced infiltration as compared to both single gene knockouts, without increase in safety risk. Whole-transcriptome profiling and single cell RNA sequencing of tumor-infiltrating CAR-T cells reveal that NR4A1_SOCS3 DKO specifically enhanced metabolic fitness of CAR-T cells, rendering them exceptionally potent in long-term tumor control in an orthotopic model, superior to both single gene knockouts and the PD1_CTLA4 dual-checkpoint DKO benchmark. These data together demonstrate the efficacy of high-throughput DKO screening to rapidly uncover critical genetic interactions within T cells, and discover NR4A1_SOCS3 as promising joint intracellular checkpoints to engineer high-performance CAR-T therapies against solid tumors.
This dataset
Open GSE272885 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 GSE272885_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 PRJNA1139179 and SRA study SRP521687. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
16 conditions, mostly in triplicate
D_0 ×3 D_6 ×3 D_24 ×3 D_72 ×3 N_0 ×3 N_6 ×3 N_24 ×3 N_72 ×3 +8 more

Supports a between-group comparison across 48 samples.

How we read this: 16 replicated groups read from 48 sample titles; they account for 48 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

+ 8 more — browse all 48 samples with per-sample file links →

Datasets on a similar topic

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