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Expansion of endogenous T cells in CSF of pediatric and young adult CNS tumor patients undergoing locoregional delivery of IL13R⍺2-targeting CAR T cells: an interim analysis

GSE261235 Homo sapiens (human) Single-cell RNA-seq 37 samples Submitted 2025/03/14 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 37 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
37 samples: 37 samples, each with a distinct title — no replicated groupsGroups 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)
Outcomes for pediatric brain tumor patients remain poor, yet there is optimism that chimeric antigen receptor (CAR) T cell therapy can improve prognosis. Here, we present interim results from the first six patients treated on an ongoing phase I clinical trial (NCT04510051) of IL13BBζ-CAR T cells delivered weekly into the lateral cerebral ventricles.
Read the full summary ↓
Our preliminary clinical findings suggest CAR T cell therapy is safe and well tolerated in both lymphodepleted and nonlymphodepleted patients, and may offer modest clinical benefit. In addition to our clinical findings, we carried out a series of correlative analyses leveraging single cell genomics and orthogonal methods to profile PBMCs, mononuclear cells in the cerebrospinal fluid (CSF) and post-treatment tumors. leveraging both single cell genomics and more traditional approaches. These studies identified important interactions between CAR T infusions and the endogenous immune system. Notably, we identified and characterized a population of expanding CAR- CD8+ T cells identified in the CSF. Furthermore, we found these and other immune changes observed in the CSF were not well captured by PBMCs. Conversely, we provide evidence of clonally expanded TCRs shared between CSF and post-treatment tumor. While these data are limited, our findings suggest T cell dynamics observed in the CSF are reflected in the tumor, but not in PBMCs. Taken together, these initial findings provide support for continued investigation into locoregionally-delivered IL13BBζ-CAR T cells for children and young adults with brain tumors, and provide important insights into endogenous immune response during therapy.
This dataset
Open GSE261235 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 GSE261235_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 PRJNA1086140 and SRA study SRP494334. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
37 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 37 sample titles: 37 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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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.