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p300 catalytic inhibition selectively targets IRF4 oncogenic activity in multiple myeloma (MM1S_SOC)

GSE274846 Homo sapiens (human) RNA-seq (gene expression) 45 samples Submitted 2025/10/20 Platform GPL34284
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)
Samples
45 samples: 15 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
Catalytic Inhibition of p300 Preferentially Targets IRF4 Oncogenic Activity and Tumor Growth in Multiple MyelomaCancer research · 2026
Summary (written by the submitters)
The oncogenic transcription factor (TF) IRF4 is a universal multiple myeloma (MM) dependency that remains undrugged owing to its disordered structure. Using transcriptional regulatory network (TRN) mapping, an unbiased multi-omic approach to nominate druggable TF cofactors, we identified the chromatin coactivator lysine acetyltransferase (KAT) p300 as a key IRF4 partner.
Read the full summary ↓
We developed KB528, a highly selective p300 KAT inhibitor to explore the regulatory relationship between IRF4 and p300. Instead of broadly inhibiting transcription, partial p300 KAT inhibition selectively downregulates IRF4 and its downstream gene expression program leading to apoptosis selectively in MM cells. IRF4 dependency is a hallmark of MM that exists downstream of existing MM therapies. Consequently, p300 KAT inhibition exhibits strong antiproliferative activity ex vivo and in vivo as a single agent as well as in combination regimens in treatment refractory models. p300 KAT inhibition is well-tolerated in vivo motivating further clinical development in MM.
Published in
Catalytic Inhibition of p300 Preferentially Targets IRF4 Oncogenic Activity and Tumor Growth in Multiple Myeloma
Lenoir WF, McKeown MR, Giorgetti G et al. · Cancer research 2026 · PMID 41248492 · doi:10.1158/0008-5472.CAN-25-3440
This dataset
Open GSE274846 in finder → Find more human RNA-seq datasets →
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 GSE274846_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 PRJNA1148173 and SRA study SRP526464. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41248492) ↗
Study design
15 conditions, mostly in triplicate
KB528_DMSO_0H ×3 KB528_6H ×3 KB528_24H ×3 GNE781_DMSO_0H ×3 GNE781_6H ×3 GNE781_24H ×3 CCS1477_DMSO_0H ×3 CCS1477_6H ×3 +7 more

Supports a between-group comparison across 45 samples.

How we read this: 15 replicated groups read from 45 sample titles; they account for 45 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

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

Datasets on a similar topic

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