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A systems approach reveals SMARCD3 regulon drives alternate growth signaling and vulnerability to MEK inhibitors in everolimus resistant breast cancer cells

GSE286174 Homo sapiens (human) RNA-seq (gene expression) 36 samples Submitted 2025/12/11 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 36 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
36 samples: 12 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
System analysis links SMARCD3 regulons to growth signaling and MEK inhibitor response in everolimus-resistant ER+ breast cancer cellsCell reports. Medicine · 2025
Summary (written by the submitters)
Estrogen receptor-positive breast cancer (ER+BC) represents nearly 75% of all breast tumors, with nearly 20-40% patients eventually developing metastases. Everolimus is an mTOR inhibitor which has been approved for use in combination with exemestane in patients with metastatic ER+BC. However, many patients eventually develop resistance to everolimus leading to poor survival outcomes.
Read the full summary ↓
Systematic characterization of the molecular phenotypes underlying everolimus resistance could reveal new therapeutic targets for metastatic ER+BC. We analyzed transcriptomic profiles from multiple pairs of everolimus resistant or sensitive ER+BC cell lines, to identify signaling mechanisms that distinguish resistant cells both at baseline and under everolimus treatment. Using a linear mixed-effects model, we identified strong activation of the growth-factor signaling meta-phenotype in resistant cells at baseline, which persisted even under everolimus treatment. This meta phenotype included alternate growth signaling mechanisms including IGF1R/insulin, ESR1, RAS and MAPK signaling. To understand the regulatory mechanism behind alternate growth-factor signaling activation, we adopted a network reconstruction architecture to identify SMARCD3 regulons as key transcriptional regulatory networks in resistant cells. In a neo-adjuvant trial for ER+BC patients, the expression levels of SMARCD3 and its regulon’s activity were significantly elevated in tumors refractory to everolimus. To develop a therapeutic strategy against everolimus resistance, we used transcriptomic and drug sensitivity profiles of 926 cancer cell lines treated with 395 compounds. We found that the activation of SMARCD3 regulons was consistently associated with sensitivity to several known MEK1/2 inhibitors, including trametinib. To validate these findings, we evaluated the efficacy of everolimus in combination with trametinib and demonstrated that the combination treatment significantly reduced the growth of resistant cells compared to everolimus. Our results demonstrate that everolimus resistant ER+BC cells utilize alternate growth-factor signaling to escape therapeutic control. We found that this phenotype is strongly linked with activation of the SMARCD3 regulons and can be therapeutically targeted using MEK1/2 inhibitors.
Published in
System analysis links SMARCD3 regulons to growth signaling and MEK inhibitor response in everolimus-resistant ER+ breast cancer cells
Medina EF, Farmaki E, Griffiths JI et al. · Cell reports. Medicine 2025 · PMID 41260207 · doi:10.1016/j.xcrm.2025.102425
This dataset
Open GSE286174 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 GSE286174_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 PRJNA1208054 and SRA study SRP556076. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41260207) ↗
Study design
12 conditions, mostly in triplicate
CAMA1 Sensitive, DMSO, ×3 CAMA1 Sensitive, Everolimus, ×3 CAMA1 Everolimus-Resistant, DMSO, ×3 CAMA1 Everolimus-Resistant, Everolimus, ×3 MCF7 Sensitive, DMSO, ×3 MCF7 Sensitive, Everolimus, ×3 MCF7 Everolimus-Resistant, DMSO, ×3 MCF7 Everolimus-Resistant, Everolimus, ×3 +4 more

Supports a between-group comparison across 36 samples.

How we read this: 12 replicated groups read from 36 sample titles; they account for 36 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 Breast cancer datasets

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