GEO series

AMPK agonism optimizes the in vivo activation and anti-leukemia efficacy of chimeric antigen receptor T cells

GSE313107 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2025/12/13 Platform GPL30173

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)
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: AMPK agonism optimizes the in vivo activation and antileukemic efficacy of chimeric antigen receptor T cellsJournal for immunotherapy of cancer · 2025

Summary (written by the submitters)

BACKGROUND: Chimeric antigen receptor (CAR) T cells have achieved remarkable clinical success. However, up to 50% of CAR T cell-treated leukemia patients relapse and long-term survivor data indicate that CART cell persistence is key to enforcing relapse-free survival. Unfortunately, ex vivo expansion protocols often drive metabolic and functional exhaustion, reducing in vivo efficacy.

Read the full summary ↓

Preclinical models have demonstrated that redirecting metabolism can improve in vivo T cell function. Here, we hypothesized that exposure to an agonist targeting AMP-activated protein kinase (AMPK) would create CARTs capable of increased in vivo function and enhanced leukemia clearance. METHODS: CART cells were generated from healthy human donor T cells via lentiviral transduction, followed by exposure to either Compound 991 or DMSO for 96 hours, followed by a 48-hour washout. During and after agonist treatment, T cells were harvested for metabolic and functional assessments. To test in vivo efficacy, immunodeficient mice were injected with luciferase+ NALM6 leukemia cells, and one week later with 991- or DMSO-expanded CARTs. Leukemia burden and anti-leukemia efficacy was assessed via radiance imaging and overall survival. RESULTS: Compound 991 treatment activated AMPK without limiting cellular expansion, and increased both mitochondrial density and handling of reactive oxygen species (ROS). Mechanistically, 991 treatment mimicked nutrient starvation, with increased autophagic flux and generation of mitochondrially-protective metabolites in treated cells. Importantly, receipt of 991-exposed CARTs significantly improved in vivo leukemia clearance and prolonged recipient survival, likely as a result of elevated activation and increased CD4+ T cell yields at early times post-injection. DISCUSSION: Ex vivo expansion is necessary to generate sufficient cell numbers, but sustained activation and differentiation often negatively impact in vivo persistence and function. Here, we demonstrate that promoting AMPK activity during in vitro CART expansion metabolically reprograms cells without limiting T cell yield, increases early activation marker expression following in vivo transfer, and ultimately enhances anti-leukemia efficacy. Importantly, Compound 991 treatment achieves these results without further modifying the expansion media, changing the CART construct, or genetically altering the cells. Altogether, these data highlight AMPK agonism as a potent and readily translatable approach to improve the metabolic profile and in vivo efficacy of adoptively transferred T cells.

Published in

AMPK agonism optimizes the in vivo activation and antileukemic efficacy of chimeric antigen receptor T cells

Braverman E, Qin M, Ampudia-Mesias E et al. · Journal for immunotherapy of cancer 2025 · PMID 41423271 · doi:10.1136/jitc-2024-010312

This dataset

Open GSE313107 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 GSE313107_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 PRJNA1378540 and SRA study SRP653933. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41423271) ↗

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

GSM9361117 DA_991_CD4_S23
GSM9361118 DA_991_CD8_S24
GSM9361119 DA_DMSO_CD4_S21
GSM9361120 DA_DMSO_CD8_S22
GSM9361121 DC_991_CD4_S27
GSM9361122 DC_991_CD8_S28
GSM9361123 DC_DMSO_CD4_S25
GSM9361124 DC_DMSO_CD8_S26
GSM9361125 DD_991_CD4_S31
GSM9361126 DD_991_CD8_S32
GSM9361127 DD_DMSO_CD4_S29
GSM9361128 DD_DMSO_CD8_S30

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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.