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CDK6 kinase inhibition opens up metabolic dependencies in BCR-ABL+ leukemia

GSE267912 Homo sapiens (human) RNA-seq (gene expression) 45 samples Submitted 2025/02/26 Platform GPL21290
At a glance

RNA-seq (gene expression) · human · 45 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
Chronic myeloid leukemiaThis series is part of our curated briefing on it.
Samples
45 samples: 45 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
CDK6 kinase inhibition unmasks metabolic dependencies in BCR::ABL1+ leukemiaCell death & disease · 2025
Summary (written by the submitters)
Metabolic reprogramming and cell cycle deregulation are hallmarks of cancer cells. We here investigate the role of the cell cycle kinase CDK6 in the regulation of cellular energetics in BCR-ABL+ leukemia. Gene expression data and ChIP-Seq analysis from murine BCR-ABL+ cell lines expressing kinase-inactive CDK6 or no CDK6 highlight an activating role for the kinase in regulating the oxidative phosphorylation gene set by interacting with respective promoter regions.
Read the full summary ↓
Our data imply a competition of CDK6 and Nrf-1, a master regulator of genes required for mitochondrial respiration, at the same sites. In line, cells expressing kinase- inactive CDK6 show signs of a defective electron transport chain and morphologically changed mitochondria. An enhanced cytoplasm/mitochondria ATP ratio together with high levels of pyruvate and lactate point towards a metabolic switch to glycolysis in those cells. Combinatorial treatment of BCR-ABL+ cell lines with the clinically used CDK4/6 inhibitor palbociclib and the glycolysis inhibitor 2-deoxyglocose (2-DG) reduced proliferation and led to enhanced apoptosis compared to single-agent treatments. Our data, suggest a new therapeutic avenue for hematologic malignancies with high CDK6 expression.
Published in
CDK6 kinase inhibition unmasks metabolic dependencies in BCR::ABL1+ leukemia
Scheiblecker L, Klampfl T, Doma E et al. · Cell death & disease 2025 · PMID 39966356 · doi:10.1038/s41419-025-07434-1
This dataset
Open GSE267912 in finder → Find more human RNA-seq datasets → Chronic myeloid 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 GSE267912_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 PRJNA1113623 and SRA study SRP508785. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39966356) ↗
Study design
45 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 45 sample titles: 45 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

+ 5 more — browse all 45 samples with per-sample file links →

More Chronic myeloid leukemia datasets

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.