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Progesterone receptors drive advanced breast cancer phenotypes including circulating tumor- and stem-like cell expansion in the context of ESR1 mutation [RNA-seq]

GSE310672 Homo sapiens (human) RNA-seq (gene expression) 48 samples Submitted 2026/06/10 Platform GPL34281
At a glance

RNA-seq (gene expression) · human · 48 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
48 samples: 16 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.
Paper
Progesterone receptors drive advanced breast cancer phenotypes including circulating tumor- and stem-like cell expansion in the context of ESR1 mutationNPJ breast cancer · 2026
Summary (written by the submitters)
Endocrine therapy resistance remains a major challenge in the treatment of advanced estrogen receptor positive (ER+) breast cancer. This can be driven by acquired mutations in the estrogen receptor gene (ESR1), such as Y537S or D538G, that primarily emerge in patients with prior aromatase inhibitor therapy and results in constitutive estrogen-independent ER activity.
Read the full summary ↓
Progesterone receptors (PR) are important modifiers of ER activity, in part via direct binding. We previously showed that PR mediates expansion of cancer stem-like cell (CSC) populations and promotes tamoxifen resistance in nuclear ER/PR transcriptional complexes. In this study, we sought to define whether PR function changes in the context of ESR1 mutations. PR readily interacted with wild type (WT), but not Y537S or D538G ERs. RNA-seq and ChIP-seq studies demonstrated that ER+ breast cancer models expressing Y537S ER exhibited a distinct response to progesterone. CSC populations were enhanced in Y537S ER+ cells compared to WT ER+ cells. PR knockdown demonstrated that this property required PR expression but was unresponsive to antiprogestins. Moreover, we identified PR-dependent transcriptional programs such as the unfolded protein response (UPR) that can be leveraged to target CSC populations in Y537S ESR1-mutant breast cancer. The UPR activator ErSO, but not UPR inhibitors, blocked expansion of CSCs in WT as well as Y537S ER+ models. Together, our findings demonstrate a critical interplay between PR and mutant ER function and provide insight into PR-driven pathways including hyperactivation of the stress-sensing UPR that can be exploited as potential therapeutic avenues in advanced ER+ breast cancer.
Published in
Progesterone receptors drive advanced breast cancer phenotypes including circulating tumor- and stem-like cell expansion in the context of ESR1 mutation
Truong TH, Gillis NE, Dwyer AR et al. · NPJ breast cancer 2026 · PMID 41946751 · doi:10.1038/s41523-026-00939-8
This dataset
Open GSE310672 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 GSE310672_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 PRJNA1366924 and SRA study SRP647173. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41946751) ↗
Study design
16 conditions, mostly in triplicate
2D_WT_shGFP_E2_R50 ×3 2D_WT_shGFP_E2_veh ×3 2D_WT_shGFP_veh_R50 ×3 2D_WT_shGFP_veh_veh ×3 2D_WT_shPR_E2_veh ×3 2D_WT_shPR_veh_veh ×3 2D_YS_shGFP_E2_R50 ×3 2D_YS_shGFP_E2_veh ×3 +8 more

Supports a between-group comparison across 48 samples.

How we read this: 16 replicated groups read from 48 sample titles; they account for 48 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

+ 8 more — browse all 48 samples with per-sample file links →

More Breast cancer datasets

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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.