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Pre-Treatment peripheral Immune composition can accurately predict CAR-T outcomes in patients with Large B-Cell Lymphoma

GSE267097 Homo sapiens (human) Single-cell RNA-seq 49 samples Submitted 2025/09/14 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 49 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)
Topic
Diffuse large B-cell lymphomaThis series is part of our curated briefing on it.
Samples
49 samples: 31 distinct titles, 3 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)
Chimeric Antigen Receptor (CAR)-T cell therapy has revolutionized the treatment landscape for relapsed/refractory B-cell malignancies. Despite its success, approximately 60% of patients experience treatment failure, underscoring the need to better understand the determinants of response and resistance. We performed single-cell RNA-sequencing of pre-treatment peripheral blood samples and anti-CD19 CAR-T products from 57 diffuse large B-cell lymphomas (DLBCL), correlating molecular and cellular features with clinical outcomes.
Read the full summary ↓
At the time of leukapheresis, responders presented elevated levels of CD16+ monocytes and CD4+ effector memory T cells. In contrast, non-responders showed an inflammation-driven gene expression signature across T-cell and myeloid compartments, marked by upregulation of TNF-α response signaling pathways. Notably, the presence of malignant or healthy B cells (13 of 57 patients) was strongly associated with a favorable response. These findings shed light on the immune landscape conducive to successful CAR-T therapy and offer a molecular framework for developing personalized tools to improve patient selection, stratification, and the design of next-generation CAR-T treatments.
This dataset
Open GSE267097 in finder → Find more human RNA-seq datasets → Diffuse large B-cell lymphoma landscape →
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 GSE267097_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 PRJNA1109756 and SRA study SRP506786. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
31 distinct titles, 3 of them repeated
Bulk_Poc ×10 Bulk_Healthy ×7 Bulk_Axi ×4

How we read this: Read from 49 sample titles: 31 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

+ 9 more — browse all 49 samples with per-sample file links →

More Diffuse large B-cell lymphoma datasets

Search all human 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.