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Transcriptional reprogramming in mantle cell lymphoma promotes BTK inhibitor resistance

GSE305144 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 63 samples Submitted 2025/09/01 Platform GPL20301Platform GPL34284
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 63 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Homo sapiens (human)
Topic
Mantle cell lymphomaThis series is part of our curated briefing on it.
Samples
63 samples: 52 distinct titles, 7 of them repeatedGroups 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
Leveraging BRG1 Driven Ferroptosis Resistance to Overcome Treatment ResistancebioRxiv : the preprint server for biology · 2025
Summary (written by the submitters)
Resistance to Bruton’s tyrosine kinase inhibitors (BTKi) remains a major therapeutic challenge in B-cell malignancies, limiting treatment durability. We demonstrate BRG1-mediated chromatin remodeling as a major driver of BTKi resistance. We show that cancer-associated BRG1 ATPase domain mutations protect cells from BTKi-induced ferroptosis by limiting reactive oxygen species (ROS) production and labile iron.
Read the full summary ↓
Mechanistically, the BRG1T910M mutation activates the unfolded protein response (UPR) via ATF4, leading to upregulation of MEF2B, a key suppressor of ferroptosis. MEF2B further inhibits mitochondrial respiration and thus prevent BTKi-induced mitophagy and ferroptosis. Pharmacological inhibition of BRG1 promotes ferroptosis by suppressing pro-survival B cell receptor (BCR) signaling and the ATF4-MEF2B axis. We show that a clinical stage BRG1 inhibitor restores ferroptosis in BTKi-resistant cells and synergizes with BTKi across various MCL models. Together we propose for co-targeting BRG1 and BTK in treatment of B-cell malignancies.
Published in
Leveraging BRG1 Driven Ferroptosis Resistance to Overcome Treatment Resistance
Hwang SY, Nikolli H, Yoval-Sánchez B et al. · bioRxiv : the preprint server for biology 2025 · PMID 40950195 · doi:10.1101/2025.09.02.673601
This dataset
Open GSE305144 in finder → Find more human RNA-seq datasets → Mantle cell lymphoma 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 GSE305144_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1304501 and SRA study SRP608124. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40950195) ↗
Study design
52 distinct titles, 7 of them repeated
MINO BRG1 WT_GFP ×4 MINO BRG1 T910M_GFP ×4 MINO expressing BRG1-T910M mutant ×2 JEKO parent HA ×2 JEKO MEF2B HA ×2 JEKO WT control ×2 JEKO_sgMEF2B ×2

Supports a case/control comparison: 7 samples read as cases, 13 as controls.

How we read this: Read from 63 sample titles: 52 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

+ 23 more — browse all 63 samples with per-sample file links →

More Mantle cell lymphoma datasets

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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.