GEO series

Immunometabolic determinants of long-term response in leukemia patients receiving CD19 CAR T cell therapy

GSE298663 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2025/06/05 Platform GPL34281

At a glance

RNA-seq (gene expression) · human · 12 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)
Topic: Acute lymphoblastic leukemiaThis series is part of our curated briefing on it.
Samples: 12 samples: 12 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: Immunometabolic determinants of long-term response in leukemia patients receiving CD19 CAR T cell therapyNature communications · 2026

Summary (written by the submitters)

Although most patients with relapsed/refractory B-cell acute lymphoblastic leukemia (B-ALL) receiving CD19-targeted chimeric antigen receptor (CAR) T cell therapy achieve remission, loss of CAR T cell functionality and subsequent relapse remains an unmet therapeutic need. We applied an integrative approach to study the immunometabolism of pre- and post-infusion CD19-CAR T cells of patients with relapsed/refractory B-ALL.

Read the full summary ↓

Pre-infusion CAR T cells of long-term responders (LTR) had increased oxidative phosphorylation, fatty acid oxidation, and pentose phosphate pathway activities, higher mitochondrial mass, tighter cristae, and lower mTOR expression compared to products of short-term responders. Post-infusion CAR T cells in bone marrow (BM) of LTR had high immunometabolic plasticity and mTOR-pS6 expression supported by the BM microenvironment. Transient inhibition of mTOR during manufacture induced metabolic reprogramming and enhanced anti-tumor activity of CAR T cells. Our findings provide insight into immunometabolic determinants of long-term response and suggest a therapeutic strategy to improve long-term remission.

Published in

Immunometabolic determinants of long-term response in leukemia patients receiving CD19 CAR T cell therapy

Goldberg L, Haas ER, Wu J et al. · Nature communications 2026 · PMID 41720802 · doi:10.1038/s41467-026-69857-4

This dataset

Open GSE298663 in finder → Find more human RNA-seq datasets → Acute lymphoblastic leukemia 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 GSE298663_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 PRJNA1270837 and SRA study SRP589357. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41720802) ↗

Study design

12 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 12 sample titles: 12 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

GSM9020501 58176_209W
GSM9020502 58177_209WO
GSM9020503 58178_293W
GSM9020504 58179_293WO
GSM9020505 58180_397W
GSM9020506 58181_397WO
GSM9020507 58182_466W
GSM9020508 58183_466WO
GSM9020509 58184_543W
GSM9020510 58185_543WO
GSM9020511 58186_549W
GSM9020512 58187_549WO

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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.