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Activation of a nongenetic AHR-ELMSAN1 axis optimizes BET-targeting therapy and suppresses leukemia stem cells in preclinical models [RNA-Seq2]

GSE241936 Homo sapiens (human) RNA-seq (gene expression) 36 samples Submitted 2025/09/01 Platform GPL20301
At a glance

RNA-seq (gene expression) · human · 36 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
36 samples: 36 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.
Summary (written by the submitters)
Developing strategies to enhance the response to bromodomain and extraterminal domain (BET) inhibitors and effectively eradicate cancer stem cells would represent a major cancer treatment advance against leukemia. Through a functional CRISPR screen, we identified the aryl hydrocarbon receptor (AHR), a ligand-activated transcription factor, as a critical regulator of MYC expression and BET inhibitor sensitivity in human acute myeloid leukemia (AML).
Read the full summary ↓
Constitutive or pharmacological activation of AHR repressed MYC and synergized with BET inhibitors to inhibit MYC transcription and suppress leukemia growth across diverse AML models. Mechanistically, AHR directly up-regulated a noncanonical target, ELMSAN1, a component of the MiDAC histone deacetylase complex, which promotes histone deacetylation at MYC regulatory elements. ELMSAN1 depletion led to up-regulation of MYC and impaired AHR signaling–induced BET inhibitor sensitization. In vivo, AHR agonists enhanced BET inhibitor efficacy in patient-derived xenografts and murine leukemia models, enabling the use of lower BET inhibitor doses while preserving therapeutic benefit and reducing toxicity. This combination suppressed leukemia stem cell (LSC) gene signatures and reduced LSC frequency, with minimal impact on normal hematopoietic stem and progenitor cells in both human cord blood xenografts and immunocompetent mouse models. Together, these findings uncover a MYC-repressive, nongenetic AHR-ELMSAN1 axis that enhances BET-targeting therapies and selectively impairs LSCs, providing a compelling rationale for clinical translation in AML and potentially other MYC-driven cancers.
This dataset
Open GSE241936 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 GSE241936_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 PRJNA1010781 and SRA study SRP457573. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
36 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 36 sample titles: 36 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

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.