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A Molecular Switch from Tumor Suppressor to Oncogene in ER-Positive Breast Cancer: Role of Androgen Receptor, JAK-STAT, and Lineage Plasticity [AR low/high]

GSE274715 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2024/10/16 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 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
Homo sapiens (human)
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
A molecular switch from tumor suppressor to oncogene in ER+ve breast cancer: Role of androgen receptor, JAK-STAT, and lineage plasticityProceedings of the National Academy of Sciences of the United States of America · 2024
Summary (written by the submitters)
Cancers develop resistance to inhibitors of oncogenes mainly due to target-centric mechanisms such as mutations and splicing. While inhibitors or antagonists force targets to unnatural conformation contributing to protein instability and resistance, activating tumor suppressors may maintain the protein in an agonistic conformation to elicit sustainable tumor growth inhibition.
Read the full summary ↓
Due to lack of tumor suppressor-agonists, this hypothesis and mechanisms underlying resistance are not understood. In estrogen receptor (ER)-positive breast cancer (BC), androgen receptor (AR) is a druggable tumor suppressor and offers a promising avenue for this investigation. Spatial genomics suggest that the molecular portrait of AR-expressing BC cells in tumor microenvironment corresponds to better overall patient survival, clinically confirming AR’s role as a tumor suppressor. Ligand-activation of AR-positive BC xenografts reprograms cistromes, inhibits oncogenic pathways, and promotes cellular elasticity towards a more differentiated state, while sustained treatment results in cistromes rearrangement towards transcription factor PROP1, transformation of AR into an oncogene, and activation of the JAK/STAT pathway, all culminating in lineage plasticity to an aggressive resistant subtype. While the molecular profile of AR agonist-sensitive tumors corresponds to better patient survival, the profile represented in the resistant phenotype corresponds to shorter survival. Inhibition of activated oncogenes in resistant tumors reduces growth and re-sensitizes them to AR agonists. Together, these findings indicate that persistent activation of a tumor suppressor is not fail-safe and leads to resistance through lineage plasticity-driven tumor metamorphosis. Our work provides a framework to explore the above phenomenon across multiple cancer types and underscores the importance of factoring the sensitization of tumor suppressor targets while developing agonist-like drugs
Published in
A molecular switch from tumor suppressor to oncogene in ER+ve breast cancer: Role of androgen receptor, JAK-STAT, and lineage plasticity
Asemota S, Effah W, Holt J et al. · Proceedings of the National Academy of Sciences of the United States of America 2024 · PMID 39312663 · doi:10.1073/pnas.2406837121
This dataset
Open GSE274715 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 GSE274715_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 PRJNA1147702 and SRA study SRP526085. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39312663) ↗
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
Datasets on a similar topic

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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.