GEO series

TP53-Agnostic Lethality through Combined Pan-HDAC and CDK Inhibition in Acute Myeloid Leukemia

GSE305560 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2025/12/02 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 18 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: 18 samples: 6 conditions, mostly in triplicateGroups 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: TP53-agnostic lethality through combined pan-HDAC and CDK inhibition in acute myeloid leukemiaCancer letters · 2025

Summary (written by the submitters)

Tumor protein 53 (TP53)-mutated acute myeloid leukemia (AML) is characterized by poor outcomes and the quick development of treatment resistance. Here, we report that simultaneous inhibition of cyclin-dependent kinases (CDKs) and histone deacetylases (HDACs) with dinaciclib and CAY10603, respectively, eliminates the therapeutic response gap between TP53-mutant and TP53 wild-type AML.

Read the full summary ↓

Biochemical profiling showed that CAY10603 is not only HDAC6-selective but also exhibits pan-HDAC activity similar to suberoylanilide hydroxamic acid, enabling dual targeting of transcriptional and cell cycle pathways. Across parental wild-type lines and isogenic TP53 mutants, the combination consistently suppressed clonogenic growth, induced caspase-dependent apoptosis, and downregulated key regulators such as CDK2, CDK4/6, and their cyclins, while restoring the CDK inhibitor CDKN1A/p21. In an orthotopic NSG mouse model, dinaciclib + CAY10603 significantly reduced leukemia burden and extended survival without adverse toxicity. By “normalizing” TP53-mutant AML to respond like its wild-type counterpart, this pan-HDAC/multi-CDK blockade offers a TP53-agnostic therapeutic option and warrants clinical evaluation as a strategy that remains effective regardless of baseline allelic status.

Published in

TP53-agnostic lethality through combined pan-HDAC and CDK inhibition in acute myeloid leukemia

Pottier A, Park S, Lee Y et al. · Cancer letters 2025 · PMID 40902843 · doi:10.1016/j.canlet.2025.218011

This dataset

Open GSE305560 in finder → Find more human RNA-seq datasets → Acute 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 GSE305560_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 PRJNA1306511 and SRA study SRP609139. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40902843) ↗

Study design

6 conditions, mostly in triplicate

HL60, Control ×3 HL60, CAY10603 ×3 HL60, SAHA ×3 U937, Control ×3 U937, CAY10603 ×3 U937, SAHA ×3

Supports a between-group comparison across 18 samples.

How we read this: 6 replicated groups read from 18 sample titles; they account for 18 of them. 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

GSM9179400 HL60, Control_1 HL60, Control
GSM9179401 HL60, Control_2 HL60, Control
GSM9179402 HL60, Control_3 HL60, Control
GSM9179403 HL60, CAY10603_1 HL60, CAY10603
GSM9179404 HL60, CAY10603_2 HL60, CAY10603
GSM9179405 HL60, CAY10603_3 HL60, CAY10603
GSM9179406 HL60, SAHA_1 HL60, SAHA
GSM9179407 HL60, SAHA_2 HL60, SAHA
GSM9179408 HL60, SAHA_3 HL60, SAHA
GSM9179409 U937, Control_1 U937, Control
GSM9179410 U937, Control_2 U937, Control
GSM9179411 U937, Control_3 U937, Control
GSM9179412 U937, CAY10603_1 U937, CAY10603
GSM9179413 U937, CAY10603_2 U937, CAY10603
GSM9179414 U937, CAY10603_3 U937, CAY10603
GSM9179415 U937, SAHA_1 U937, SAHA
GSM9179416 U937, SAHA_2 U937, SAHA
GSM9179417 U937, SAHA_3 U937, SAHA

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