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Exploiting Androgen Receptor Cell Cycle Effects to Enhance CDK4/6 Inhibitor Efficacy in Estrogen Receptor Positive Metastatic Breast Cancer

GSE300130 Homo sapiens (human) RNA-seq (gene expression) 61 samples Submitted 2026/06/23 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 61 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
Breast cancerThis series is part of our curated briefing on it.
Samples
61 samples: 61 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
Exploiting androgen receptor agonism as a treatment strategy in estrogen receptor-positive metastatic breast cancerNPJ breast cancer · 2026
Summary (written by the submitters)
The androgen receptor (AR) is expressed in 75% of estrogen receptor-positive (ER+) breast cancers (BC). Selective AR modulators (SARMs), like EP0062, present a promising therapeutic strategy for ER+ BC, particularly in patients who cannot tolerate endocrine therapy (ET) or whose tumors have developed resistance. We aimed to study the antitumor activity of EP0062 in ER+ patient-derived xenograft (PDX) models.
Read the full summary ↓
EP0062 displayed comparable antitumor efficacy as selective ER degraders (SERDs), including in PDXs with ESR1, PIK3CA, or PTEN mutations. Tumors sensitive to both SARM and SERDs were enriched in GATA3 mutations. EP0062 treatment induced AR-target genes across all models tested. A 122-gene signature of SARM sensitivity associated with good prognosis in patients with ER+ BC was developed. Mechanistically, EP0062 downmodulated S-phase cell cycle proteins in sensitive models. In EP0062-resistant, ER-driven tumors, the addition of palbociclib restored drug sensitivity thus providing a promising strategy for metastatic patients with acquired ET resistance.
Published in
Exploiting androgen receptor agonism as a treatment strategy in estrogen receptor-positive metastatic breast cancer
Monserrat L, García-García J, Molina C et al. · NPJ breast cancer 2026 · PMID 42310300 · doi:10.1038/s41523-026-00993-2
This dataset
Open GSE300130 in finder → Find more human RNA-seq datasets → Breast 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 GSE300130_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 PRJNA1279088 and SRA study SRP592913. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42310300) ↗
Study design
61 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 61 sample titles: 61 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

+ 21 more — browse all 61 samples with per-sample file links →

More Breast 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.