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Prostate adenocarcinoma transition to neuroendocrine small cell tumors requires ASCL1

GSE264598 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2024/08/28 Platform GPL21626Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 12 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
12 samples: 12 samples, each with a distinct title — no replicated groupsGroups 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.
Paper
Neuroendocrine Differentiation in Prostate Cancer Requires ASCL1Cancer research · 2024
Summary (written by the submitters)
Most patients with prostate adenocarcinoma develop resistance to therapies targeting the androgen receptor (AR). Consequently, a portion of these patients develop AR-indifferent neuroendocrine prostate cancer (NEPC), a rapidly progressing cancer with limited therapies and poor survival outcomes. Current research to understand the transition to NEPC suggests a model of lineage plasticity, where AR-dependent luminal tumors progress towards an AR-independent neuroendocrine (NE) lineage.
Read the full summary ↓
Genetic analysis of human NEPC showed a frequent loss of RB1 and TP53, and in experimental models, loss of both genes facilitates the transition to a NE lineage. Transcriptomics has shown the lineage transcription factors ASCL1 and NEUROD1 are present in NEPC. In this study, we used genetically engineered mouse models harboring Cre induced loss of Rb1 and Trp53 with MycT58A overexpression (RPM), to model prostate adenocarcinoma with NEPC by establishing prostate organoids that are subsequently used to generate subcutaneous allograft tumors. These tumors are heterogeneous and display adenocarcinoma, squamous, and NE features. ASCL1 and NEUROD1 are expressed within the NE defined regions, with ASCL1 being more predominant than NEUROD1. Genetic loss of ASCL1 in this model does not decrease tumor growth or tumor formation, however, there is a notable decrease in NE identity and an increase in cells with basal-like cell identity. This study provides a new in vivo model to study the progression of prostate cancer to NEPC and establishes the requirement for ASCL1 in driving neuroendocrine differentiation.
Published in
Neuroendocrine Differentiation in Prostate Cancer Requires ASCL1
Rodarte KE, Nir Heyman S, Guo L et al. · Cancer research 2024 · PMID 39264686 · doi:10.1158/0008-5472.CAN-24-1388
This dataset
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.

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 GSE264598_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1103220 and SRA study SRP503414. Searching any of these in the dataset finder brings you back here.

Study design
12 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 12 sample titles: 12 distinct titles with little repetition. 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
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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.