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Anti-leukemic effect of TGN-1062, a novel CDK7 inhibitor, on AML-leukemic stem cells by targeting RNA modification and Mitochondrial metabolism

GSE282364 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2024/11/24 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 myeloid 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
Targeting RNA modification and mitochondrial metabolism cross talk in leukemic stem cells with CDK7 inhibitor TGN-1062Blood advances · 2025
Summary (written by the submitters)
High expression of CDK7, a cell cycle regulator and transcriptional modulator, correlates with poor outcomes in acute myeloid leukemia (AML). We report that TGN-1062, a novel CDK7 inhibitor, exhibits significant antileukemic effects by inhibiting cell growth and inducing apoptosis in AML cell lines and primary CD34+CD38- blasts (enriched for leukemia stem cells; LSCs), while sparing normal CD34+CD38- cells (enriched for hematopoietic stem cells; HSCs).
Read the full summary ↓
Our study revealed that TGN-1062 treatment in primary CD34+CD38- AML blasts suppress RNA m6A modification by downregulating METTL3/14 proteins, key components of the m6A methyltransferase complex that regulate RNA stability and translation. Decrease in METTL proteins reduced BCL-2 m6A RNA modification and led to increased BCL-2 mRNA decay and protein levels. BCL-2 downregulation resulted in disruption of mitochondrial metabolism and mitofusion in LSCs through decrease of HMGB1/HSPB1 and NRF2/DRP1 signaling, respectively. In vivo, TGN-1062 significantly reduced leukemia burden and prolonged survival in a murine MllPTD/WT/Flt3ITD/ITD AML model and in FLT3-WT and inv(16) AML patient-derived xenografts (PDXs). Increased survival in secondary transplant experiments supported TGN-1062 activity on LSCs. Of note, we proved that TGN-1062 synergized with the BCL-2 inhibitor, venetoclax in eliminating LSCs in both murine and PDX AML models, underscoring its potential as a promising therapeutic approach for AML patients.
Published in
Targeting RNA modification and mitochondrial metabolism cross talk in leukemic stem cells with CDK7 inhibitor TGN-1062
Kang H, Zhang L, Kaadige MR et al. · Blood advances 2025 · PMID 39908477 · doi:10.1182/bloodadvances.2024014225
This dataset
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.

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 GSE282364_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1188203 and SRA study SRP546274. Searching any of these in the dataset finder brings you back here.

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
More Acute myeloid leukemia datasets

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