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CXCR4-modification boosts CAR-T cell efficacy by improving tumor tracking and bone marrow homing in B cell malignancies [In vitro]

GSE305732 Homo sapiens (human) RNA-seq (gene expression) 20 samples Submitted 2025/09/17 Platform GPL11154
At a glance

RNA-seq (gene expression) · human · 20 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
20 samples: 7 conditions, mostly in duplicateGroups 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
CXCR4-modification enhances CAR-T efficacy by improving tumor tracking and bone marrow homing in B-cell malignanciesSignal transduction and targeted therapy · 2026
Summary (written by the submitters)
Hematological malignancies of B-cell origin are characterized by frequent expressions of CXCR4 and CXCL12. The CXCR4-CXCL12 axis facilitates the metastasis of B-cell lymphoma and multiple myeloma (MM). It is also a pivotal regulator in the migration and bone marrow homing of T-cells. Herein, we hypothesized that engineering CAR-T cells to overexpress CXCR4 could utilize CXCR4-CXCL12 axis to enhance their therapeutic efficacy by increasing tumor tracking and bone marrow accumulation.
Published in
CXCR4-modification enhances CAR-T efficacy by improving tumor tracking and bone marrow homing in B-cell malignancies
Shu P, Guo F, Qin D et al. · Signal transduction and targeted therapy 2026 · PMID 41605906 · doi:10.1038/s41392-025-02522-2
This dataset
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.

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 GSE305732_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1305189 and SRA study SRP608613. Searching any of these in the dataset finder brings you back here.

Study design
7 conditions, mostly in duplicate
NT ×4 CD19_CART ×4 NT_RS ×2 NT_S ×2 CD19_CART_RS ×2 CD19_CART_S ×2 CD19_C_CART_S ×2 CD19_C_CART ×1 +1 more

Supports a between-group comparison across 18 samples.

How we read this: 7 replicated groups read from 20 sample titles; they account for 18 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
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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.