GEO series

IRF4 requires ARID1A to establish plasma cell identity in multiple myeloma (RNA-Seq human 16).

GSE253713 Homo sapiens (human) RNA-seq (gene expression) 16 samples Submitted 2024/05/01 Platform GPL11154

At a glance

RNA-seq (gene expression) · human · 16 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: Multiple myelomaThis series is part of our curated briefing on it.
Samples: 16 samples: 8 conditions, mostly in duplicateGroups 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)

Multiple myeloma (MM) is an incurable malignancy of plasma cells that exploits transcriptional networks underpinning normal plasma cell biology to drive malignant growth and survival. The transcription factor IRF4 serves as the principal architect of plasma cell identity, and MM cells are addicted to IRF4 expression for their survival.

Read the full summary ↓

To discover unique molecular vulnerabilities in MM, we employ a multi-omics approach integrating functional genomics screening, spatial-proteomics, and global chromatin mapping. We find that ARID1A, a member of the SWI/SNF chromatin remodeling complex, is both required for IRF4 expression and functionally associated with IRF4 protein on chromatin. Conditional deletion of Arid1a in activated murine B cells thwarts subsequent plasma cell differentiation by disrupting IRF4-dependent transcriptional networks, thus defining ARID1A as a novel plasma cell vulnerability. Targeting ARID1A-dependent SWI/SNF activity via SMARCA2/4 inhibition induces a rapid loss of IRF4-target gene expression and quenches global oncogenic amplification of gene expression driven by MYC, resulting in profound toxicity to MM cells. Notably, SWI/SNF-dependent genes are upregulated in MM patients with aggressive disease, and SMARCA2/4 inhibitors retain their activity in immunomodulatory drug (IMiD)-resistant MM cells. To fully harness the potential of these drugs, we use combinatorial drug screens to uncover profound synergistic toxicity between SMARCA2/4 and MEK inhibitors. Thus, targeting SWI/SNF activity potently represses an IRF4-MYC feed forward loop and provides a feasible path forward to effectively treat this incurable disease.

This dataset

Open GSE253713 in finder → Find more human RNA-seq datasets → Multiple myeloma 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 GSE253713_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 PRJNA1066811 and SRA study SRP484774. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

8 conditions, mostly in duplicate

SKMM1 6 hours DMSO ×2 SKMM1 6 hours AU15330 ×2 NCIH929 6 hours DMSO ×2 NCIH929 6 hours AU15330 ×2 SKMM1 24 hours DMSO ×2 SKMM1 24 hours AU15330 ×2 NCIH929 24 hours DMSO ×2 NCIH929 24 hours AU15330 ×2

Supports a between-group comparison across 16 samples.

How we read this: 8 replicated groups read from 16 sample titles; they account for 16 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

GSM8026877 SKMM1 6 hours DMSO 1 SKMM1 6 hours DMSO
GSM8026878 SKMM1 6 hours DMSO 2 SKMM1 6 hours DMSO
GSM8026879 SKMM1 6 hours AU15330 1 SKMM1 6 hours AU15330
GSM8026880 SKMM1 6 hours AU15330 2 SKMM1 6 hours AU15330
GSM8026881 NCIH929 6 hours DMSO 1 NCIH929 6 hours DMSO
GSM8026882 NCIH929 6 hours DMSO 2 NCIH929 6 hours DMSO
GSM8026883 NCIH929 6 hours AU15330 1 NCIH929 6 hours AU15330
GSM8026884 NCIH929 6 hours AU15330 2 NCIH929 6 hours AU15330
GSM8026885 SKMM1 24 hours DMSO 1 SKMM1 24 hours DMSO
GSM8026886 SKMM1 24 hours DMSO 2 SKMM1 24 hours DMSO
GSM8026887 SKMM1 24 hours AU15330 1 SKMM1 24 hours AU15330
GSM8026888 SKMM1 24 hours AU15330 2 SKMM1 24 hours AU15330
GSM8026889 NCIH929 24 hours DMSO 1 NCIH929 24 hours DMSO
GSM8026890 NCIH929 24 hours DMSO 2 NCIH929 24 hours DMSO
GSM8026891 NCIH929 24 hours AU15330 1 NCIH929 24 hours AU15330
GSM8026892 NCIH929 24 hours AU15330 2 NCIH929 24 hours AU15330

More Multiple myeloma datasets

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GSE167968 RNA-seq of Multiple Myeloma 33 samples same disease briefing · shares multiple, myeloma, rna-seq
GSE167969 ATAC-seq and RNA-Seq of Multiple Myeloma 46 samples same disease briefing · shares multiple, myeloma, rna-seq
GSE148715 “Direct to Drug” screening as a precision medicine tool in multiple myeloma 38 samples same disease briefing · shares multiple, myeloma
GSE179453 Expression data from control-knockdown (CTR-KD) and MAST4-KD human multiple myeloma (MM) cell lines 6 samples same disease briefing · shares multiple, myeloma
GSE178442 Genome-wide maps of transcription in DEAF1 overexpression(OE) and wildtype(WT) cells in Multiple Myeloma 4 samples same disease briefing · shares multiple, myeloma
GSE155631 Oncogenic mechanisms of DIS3 mutations in Multiple Myeloma 62 samples same disease briefing · shares multiple, myeloma
GSE167042 Splicing modulation increases BCL2 dependence and sensitizes Multiple Myeloma cells to Venetoclax 4 samples same disease briefing · shares multiple, myeloma

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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.