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Drug-regulated CD33-targeted CAR T cells control AML using clinically optimized rapamycin dosing

GSE255002 Homo sapiens (human) RNA-seq (gene expression) 70 samples Submitted 2024/04/18 Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 70 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
70 samples: 70 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
Drug-regulated CD33-targeted CAR T cells control AML using clinically optimized rapamycin dosingThe Journal of clinical investigation · 2024
Summary (written by the submitters)
Chimeric antigen receptor designs that incorporate pharmacologic control are desirable, however designs suitable for clinical translation are needed. We designed a fully human, rapamycin regulated, drug product for targeting CD33+ tumors called dimerization agent regulated immunoreceptor complex (DARIC33). T cell products demonstrated target specific and rapamycin dependent cytokine release, transcriptional responses, cytotoxicity, and in vivo antileukemic activity in the presence of as little as 1nM rapamycin.
Read the full summary ↓
Rapamycin withdrawal paused DARIC33-stimulated T cell effector functions, which were restored following re-exposure to rapamycin, demonstrating reversible effector function control. While rapamycin-regulated DARIC33 T cells were highly sensitive to target antigen, CD34+ stem cell colony forming capacity was not impacted. We benchmarked DARIC33 potency relative to CD19 CAR T cells to estimate a T cell dose for clinical testing. In addition, we integrated in vitro and preclinical in vivo drug concentration thresholds for OFF-ON state transitions, as well as murine and human rapamycin pharmacokinetics, to estimate a clinically applicable rapamycin dosing schedule. A phase 1 DARIC33 trial has been initiated (PLAT-08, NCT05105152), with initial evidence of rapamycin-regulated T cell activation and anti-tumor impact. Our findings provide evidence that the DARIC platform exhibits sensitive regulation and potency needed for clinical application to other important immunotherapy targets.
Published in
Drug-regulated CD33-targeted CAR T cells control AML using clinically optimized rapamycin dosing
Appelbaum J, Price AE, Oda K et al. · The Journal of clinical investigation 2024 · PMID 38502193 · doi:10.1172/JCI162593
This dataset
Open GSE255002 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 GSE255002_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 PRJNA1073004 and SRA study SRP487957. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38502193) ↗
Study design
70 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 70 sample titles: 70 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

+ 30 more — browse all 70 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.