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

GSE309034 Homo sapiens (human) RNA-seq (gene expression) 44 samples Submitted 2026/06/22 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 44 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
Prostate cancerThis series is part of our curated briefing on it.
Samples
44 samples: 14 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. Neuroendocrine prostate cancer (NEPC) is the most lethal form of lineage plasticity in prostate cancer. BET bromodomain inhibitors (BETi) previously were shown to block genes associated with lineage plasticity.
Read the full summary ↓
Extensive rewiring of DNA methylation is also associated with NEPC tumors. We sought to determine whether combined BET protein and DNA methyltransferase inhibition (DNMTi) would result in greater anti-tumor activity than single agent treatments alone. To test this, we treated NEPC cell line models with 500nM ZEN (BETi), 100nM dAZA (DNMTi), or the combination daily for 72hrs. We also tested the BETi + DNMTi combination in vivo using an NEPC PDX (LTL331R) with 50mg/kg ZEN and 0.8mg/kg dAZA. Combined BETi and DNMTi was more effective than single agent treatment in suppressing growth in NEPC 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. We also performed RNAseq on a PDX model that reliably transitions from adenocarcinoma (LTL331) to NEPC (LTL331R) in order to uncover the gene expression changes associated with NEPC lineage plasticity and to determine whether BETi and/or DNMTi reverses these gene expression changes.
This dataset
Open GSE309034 in finder → Find more human RNA-seq datasets → Prostate cancer 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 GSE309034_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 PRJNA1333834 and SRA study SRP626291. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
14 conditions, mostly in triplicate
LTL331R_PDX ×5 H660_72hCombo ×3 H660_72hdAZA ×3 H660_72hDMSO ×3 H660_72hZEN ×3 LTL331_PDX ×3 LTL331RPDXcombo_dAZA ×3 LTL331RPDXcombo_Vehicle ×3 +6 more

Supports a between-group comparison across 44 samples.

How we read this: 14 replicated groups read from 44 sample titles; they account for 44 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

+ 4 more — browse all 44 samples with per-sample file links →

More Prostate cancer datasets

Search all human 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.