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Boosting CAR T-cell functionality with oncolytic viruses for the treatment of pediatric diffuse midline gliomas

GSE300244 Homo sapiens (human) RNA-seq (gene expression) 42 samples Submitted 2026/04/30 Platform GPL34281
At a glance

RNA-seq (gene expression) · human · 42 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
42 samples: 42 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.
Paper
Boosting CAR T cell functionality with oncolytic viruses for the treatment of pediatric diffuse midline gliomasMolecular therapy. Oncology · 2026
Summary (written by the submitters)
Although the potential of CAR (chimeric antigen receptor) T-cells has been demonstrated in hematological malignancies, their efficacy remains limited in the treatment of solid tumors such as pediatric diffuse midline gliomas (DMGs). Contributing factors hampering the success of CAR T-cells include the immunosuppression induced by the DMGs and the surrounding tumor microenvironment (TME).
Read the full summary ↓
Oncolytic viruses (OVs) have shown to reverse this immunosuppressive effect, suggesting a potential combinatorial benefit of OV and CAR T-cells. Infection with high concentrations of the Goravir adenovirus and R124 reovirus induced DMG (n=6 cultures) lysis without affecting viability of GD2 or B7H3 directed CAR T-cells. In addition, RNA sequencing of tumor cells showed altered gene expression of cell cycle and antiviral response pathways. When used in combination, cytotoxicity of the CAR T-cells was enhanced and complemented with increased release of pro-inflammatory cytokines and chemokines, especially with R124. Furthermore, CAR T-cells co-cultured with OV-infected DMGs demonstrated a more cytotoxic effector phenotype compared to co-cultures with non-infected DMGs. Finally, these data demonstrate the enhanced benefit of DMG pre-infection with OVs for boosted CAR T-cell activity.
Published in
Boosting CAR T cell functionality with oncolytic viruses for the treatment of pediatric diffuse midline gliomas
Vazaios K, Tallon-Cobos AC, van Oosterhout LPJ et al. · Molecular therapy. Oncology 2026 · PMID 42100144 · doi:10.1016/j.omton.2026.201191
This dataset
Open GSE300244 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 GSE300244_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 PRJNA1279963 and SRA study SRP593329. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42100144) ↗
Study design
42 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 42 sample titles: 42 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

+ 2 more — browse all 42 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.