GEO series

An orally active dual CBP/p300 degrader targets core dependencies of multiple myeloma [ATAC-seq]

GSE318521 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 24 samples Submitted 2026/05/14 Platform GPL24676

At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 24 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: Multiple myelomaThis series is part of our curated briefing on it.
Samples: 24 samples: 6 conditions, mostly with about 4 replicates eachGroups 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)

The enhancer lysine acetyltransferases CBP and p300 are compelling targets for multiple myeloma therapy. Chemical inhibition of these multidomain factors, either through the bromodomain or the catalytic acetyltransferase domain, show promising activity in pre-clinical models of myeloma and other cancers. Some compounds, including the CBP/p300 bromodomain inhibitor inobrodib, have progressed to early-stage clinical trials.

Read the full summary ↓

Chemical degradation of CBP/p300 is the only modality that can completely disrupt all functional domains of CBP and p300. Our previous attempts to induce CBP/p300 targeted degradation led to dCBP-1, a potent tool compound that induces degradation of both CBP and p300 by hijacking the activity of the E3 ligase CRBN. Here we comprehensively demonstrate across a large panel of cell lines how CBP/p300 degradation compares to inhibition, with pronounced selective antiproliferative activity towards multiple myeloma cells. We use chemical linker optimization strategies to create a compound with better pharmacokinetic properties to enable use in animal models. Through these efforts we define an advanced analog of dCBP-1, dCBP-30, that has improved potency in vitro, along with improved in vivo properties including oral bioavailability. dCBP-30 led to potent and sustained loss of CBP and p300 in myeloma cell lines, potent inhibition of several myeloma-specific dependency programs, and elicits tumor reduction in xenograft models as a single agent and in combination with dexamethasone when delivered orally.

This dataset

Open GSE318521 in finder → Find more human ChIP / ATAC / CUT&Tag 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 GSE318521_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 PRJNA1419157 and SRA study SRP674837. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

6 conditions, mostly with about 4 replicates each

MM1S, DMSO, 24h, ×4 MM1S, DMSO, 6h, ×4 MM1S, DMSO, 2h, ×4 MM1S, dCBP30, 24h, ×4 MM1S, dCBP30, 6h, ×4 MM1S, dCBP30, 2h, ×4

Supports a between-group comparison across 24 samples.

How we read this: 6 replicated groups read from 24 sample titles; they account for 24 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

GSM9497034 MM1S, DMSO, 24h, rep1 MM1S, DMSO, 24h,
GSM9497035 MM1S, DMSO, 24h, rep2 MM1S, DMSO, 24h,
GSM9497036 MM1S, DMSO, 24h, rep3 MM1S, DMSO, 24h,
GSM9497037 MM1S, DMSO, 24h, rep4 MM1S, DMSO, 24h,
GSM9497038 MM1S, DMSO, 6h, rep1 MM1S, DMSO, 6h,
GSM9497039 MM1S, DMSO, 6h, rep2 MM1S, DMSO, 6h,
GSM9497040 MM1S, DMSO, 6h, rep3 MM1S, DMSO, 6h,
GSM9497041 MM1S, DMSO, 6h, rep4 MM1S, DMSO, 6h,
GSM9497042 MM1S, DMSO, 2h, rep1 MM1S, DMSO, 2h,
GSM9497043 MM1S, DMSO, 2h, rep2 MM1S, DMSO, 2h,
GSM9497044 MM1S, DMSO, 2h, rep3 MM1S, DMSO, 2h,
GSM9497045 MM1S, DMSO, 2h, rep4 MM1S, DMSO, 2h,
GSM9497046 MM1S, dCBP30, 24h, rep1 MM1S, dCBP30, 24h,
GSM9497047 MM1S, dCBP30, 24h, rep2 MM1S, dCBP30, 24h,
GSM9497048 MM1S, dCBP30, 24h, rep3 MM1S, dCBP30, 24h,
GSM9497049 MM1S, dCBP30, 24h, rep4 MM1S, dCBP30, 24h,
GSM9497050 MM1S, dCBP30, 6h, rep1 MM1S, dCBP30, 6h,
GSM9497051 MM1S, dCBP30, 6h, rep2 MM1S, dCBP30, 6h,
GSM9497052 MM1S, dCBP30, 6h, rep3 MM1S, dCBP30, 6h,
GSM9497053 MM1S, dCBP30, 6h, rep4 MM1S, dCBP30, 6h,
GSM9497054 MM1S, dCBP30, 2h, rep1 MM1S, dCBP30, 2h,
GSM9497055 MM1S, dCBP30, 2h, rep2 MM1S, dCBP30, 2h,
GSM9497056 MM1S, dCBP30, 2h, rep3 MM1S, dCBP30, 2h,
GSM9497057 MM1S, dCBP30, 2h, rep4 MM1S, dCBP30, 2h,

More Multiple myeloma datasets

GSE167969 ATAC-seq and RNA-Seq of Multiple Myeloma 46 samples same disease briefing · shares multiple, atac-seq, myeloma
GSE178328 Genome-wide maps of chromatin state in DEAF1 overexpression (OE in Multiple Myeloma) 2 samples same disease briefing · shares multiple, myeloma
GSE161195 Single cell RNA-seq of relapsed and refractory multiple myeloma patients defines new resistance pathways and targeted treatments 213 samples same disease briefing · shares multiple, myeloma
GSE155631 Oncogenic mechanisms of DIS3 mutations in Multiple Myeloma 62 samples same disease briefing · shares multiple, myeloma
GSE148715 “Direct to Drug” screening as a precision medicine tool in multiple myeloma 38 samples same disease briefing · shares multiple, myeloma
GSE167968 RNA-seq of Multiple Myeloma 33 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
GSE167042 Splicing modulation increases BCL2 dependence and sensitizes Multiple Myeloma cells to Venetoclax 4 samples same disease briefing · shares multiple, myeloma

Search all human ChIP / ATAC / CUT&Tag datasets in GEO →

Share this dataset

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.