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N-Myc induces an EZH2-mediated transcriptional program driving Neuroendocrine Prostate Cancer

GSE86532 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 38 samples Submitted 2016/09/08 Platform GPL9185Platform GPL9052
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 38 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
Prostate cancerThis series is part of our curated briefing on it.
Samples
38 samples: 12 conditions, mostly in duplicateGroups 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 transition from castration resistant prostate adenocarcinoma (CRPC) to neuroendocrine prostate cancer (NEPC) is emerging as an important mechanism of treatment resistance. NEPC are associated with over-expression and gene amplification of MYCN (encoding N-Myc). N-Myc is a bona fide driver oncogene in several rare tumor types, but its role in prostate cancer progression is not well established.
Read the full summary ↓
Integrating a novel genetically engineered mouse model and human prostate cancer transcriptome data, we show that N-Myc over-expression leads to the development of poorly differentiated, invasive prostate cancer that is molecularly similar to human NEPC tumors which includes an abrogation of AR signaling and induction of Polycomb Repressive Complex 2 signaling and that N-Myc interacts with AR and this interaction depends on Enhancer of Zeste Homolog 2. Altogether, our data shows that N-Myc drives the neuroendocrine phenotype in prostate cancer.
This dataset
Open GSE86532 in finder → Find more 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 GSE86532_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 PRJNA342161 and SRA study SRP087491. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 27728805) ↗
Study design
12 conditions, mostly in duplicate
LnCaP N-Myc ×4 LNCaP Ctrl ×3 pbCre - Sample_PTEN+/+_LSLMYCN-/ ×3 organoids_pbCre+/+_Ptenf/f_LSL-MYCN+/+ ×3 organoids_pbCre+/+_Ptenf/f_LSL-MYCN-/ ×3 22RV1_Ctrl ×3 T2-Cre - Sample_PTENf/f_LSLMYCN-/ ×2 T2-Cre - Sample_PTENf/f_LSLMYCN+/+ ×2 +11 more

Supports a between-group comparison across 31 samples.

How we read this: 12 replicated groups read from 38 sample titles; they account for 31 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
More Prostate cancer datasets

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