← BioTransfer GEO Dataset Finder
GEO series

RESET: A reversible TCR-coupled antigen receptor with superior targeting sensitivity and pharmacologically controlled anti-tumor activity

GSE284664 Homo sapiens (human) RNA-seq (gene expression) 62 samples Submitted 2025/04/23 Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 62 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
62 samples: 62 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
RESET: A TCR-coupled antigen receptor with superior targeting sensitivity and reversible drug-regulated anti-tumor activityMolecular therapy : the journal of the American Society of Gene Therapy · 2025
Summary (written by the submitters)
Chimeric antigen receptor (CAR) T cells are effective cancer therapies, particularly in indications with high, stable, and tumor-specific antigen expression. Other settings may require improved targeting sensitivity, controllable targeting selectivity, and/or additional potency enhancements to achieve robust efficacy.
Read the full summary ↓
Here, we describe a novel receptor architecture called RESET (rapamycin-enabled, switchable endogenous T-cell receptor) that combines: (i) cell surface antigen targeting; (ii) small-molecule regulation; and (iii) the signaling proficiency and inherent sensitivity of native T-cell receptors. RESET-T cells outperformed both constitutive and drug-regulated CAR-T cells and show hallmarks of TCR activation that suggest improved fidelity to native T-cell responses. Pharmacological control then increases safety through toggling T-cell activation between active and resting states and may mitigate T- cell exhaustion caused by continuous antigen exposure. This convergence of drug-regulated targeting and natural immune receptor signal transduction may better replicate the kinetics and physiology of a classical T-cell responses and potentiate more successful and safer immunotherapies.
Published in
RESET: A TCR-coupled antigen receptor with superior targeting sensitivity and reversible drug-regulated anti-tumor activity
Leleux J, Rosenberg J, Sonzogni O et al. · Molecular therapy : the journal of the American Society of Gene Therapy 2025 · PMID 39980194 · doi:10.1016/j.ymthe.2025.02.026
This dataset
Open GSE284664 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 GSE284664_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 PRJNA1200012 and SRA study SRP552515. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39980194) ↗
Study design
62 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 62 sample titles: 62 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

+ 22 more — browse all 62 samples with per-sample file links →

Datasets on a similar topic

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.