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Discovery of a paralog-selective p300 protein degrader with potent anti-cancer activity in hematological malignancies

GSE301413 Homo sapiens (human) RNA-seq (gene expression) 16 samples Submitted 2026/02/26 Platform GPL20301
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)
Samples
16 samples: 4 × DMSO-treated, vs 4 × p300 degrader 27-treated, vs 4 × NTC, vs 4 × p300 KO,Groups 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 E1A-associated protein p300 (EP300) is a key regulator of oncogenic transcription factors, making it a promising target for cancer therapy. However, high sequence similarity to its paralog, CREB-binding protein (CBP), has hindered the development of selective inhibitors, leading to toxic side effects. Here, we describe a highly potent and selective p300 degrader.
Read the full summary ↓
Unlike dual p300/CBP degraders, this compound forms a stronger, more stable ternary complex with p300, induces higher level of p300 ubiquitination and recruitment to the proteosome, and ubiquitinates a unique lysine site on p300. Hematological cancers, including multiple myeloma, non-Hodgkin’s lymphoma and acute myeloid leukemia, showed the greatest sensitivity to the p300-selective degrader, which induced a lethal phenotype in cells and demonstrated antitumor efficacy in xenograft models. These findings highlight the therapeutic potential of selective p300 degradation as a novel strategy for the treatment of hematological malignancies and the inhibition of cancer progression.
This dataset
Open GSE301413 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 GSE301413_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 PRJNA1284952 and SRA study SRP596503. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
4 × DMSO-treated, vs 4 × p300 degrader 27-treated, vs 4 × NTC, vs 4 × p300 KO,

Supports a between-group comparison across 16 samples.

How we read this: 4 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
Datasets on a similar topic

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