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Combined BET Bromodomain and DNMT Inhibition Targets Lineage Plasticity in Prostate Cancer [BETi_DNMTi_mouse_RNAseq]

GSE309033 Mus musculus (mouse) RNA-seq (gene expression) 51 samples Submitted 2026/06/22 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 51 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
Mus musculus (mouse)
Samples
51 samples: 17 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.
Summary (written by the submitters)
Lineage plasticity is increasingly recognized as a resistance mechanism to androgen receptor (AR) inhibition in prostate cancer. Loss of the tumor suppressors TP53 and RB1 is common in tumors exhibiting lineage plasticity; however, mechanisms by which TP53/RB1 loss promotes this phenotype remain poorly understood, and effective treatments are limited.
Read the full summary ↓
Thus we used multi-omic profiling of TP53/RB1 loss prostate cancer models to identify alterations in chromatin accessibility, DNA methylation, and gene expression associated with lineage plasticity. BET bromodomain inhibitors previously were shown to block genes associated with lineage plasticity. We sought to determine whether they could block pathways activated upon TP53/RB1 loss as well. TP53/RB1-deficient cells also harbored widespread DNA methylation changes that silenced pathways linked with lineage plasticity, which we found could be reversed by DNA methyltransferase (DNMT) inhibitors. Combined BET bromodomain and DNMT inhibition was more effective than single agent treatment in suppressing growth in TP53/RB1 loss models compared to single agent treatments. To identify the gene expression changes underlying the superior viability effect of the combo treatment, we peformed RNAseq on treated cells.
This dataset
Open GSE309033 in finder → Find more mouse 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 GSE309033_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 PRJNA1333833 and SRA study SRP626346. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
17 conditions, mostly in triplicate
DKO_24hDMSO ×3 DKOCr105_24hDMSO ×3 DKOCr298_24hDMSO ×3 DKOCr298_24hJQ1 ×3 DKOCr298_24hZEN ×3 DKOcr298_72hCombo ×3 DKOcr298_72hdAZA ×3 DKOcr298_72hDMSO ×3 +9 more

Supports a between-group comparison across 51 samples.

How we read this: 17 replicated groups read from 51 sample titles; they account for 51 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

+ 11 more — browse all 51 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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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.