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Overcoming intrinsic mechanisms of cell cycle inhibitor resistance in estrogen receptor-positive (ER+) breast cancer

GSE284956 Homo sapiens (human) Single-cell RNA-seq 180 samples Submitted 2026/07/01 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 180 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Topic
Breast cancerThis series is part of our curated briefing on it.
Samples
180 samples: 64 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.
Summary (written by the submitters)
Estrogen receptor-positive breast cancer (ER+BC) represent about 70% of all breast cancer cases. Approximately 30-50% of ER+BCs eventually develop resistance to primary endocrine therapy and progress to metastatic disease. The addition of cyclin-dependent kinases 4 and 6 inhibitors (CDK4/6i) to endocrine therapy has improved disease control in advanced and metastatic ER+BC.
Read the full summary ↓
However, progression on CDK4/6i is common and further research is required to fully understand and overcome the mechanisms of CDK4/6i resistance in ER+BC. We performed single-cell RNA sequencing (scRNA-seq) of temporal tumor samples from the FELINE clinical trial (NCT02712723) to uncover emergent resistance phenotypes in ER+BC patients receiving a combination of ribociclib with letrozole or letrozole alone in a neoadjuvant setting. In the majority of patient tumors, resistant cancer cells activated ErbB growth factor signaling during treatment, with an upregulation in ERBB2 and ERBB4 in post-treatment cells, as well as increased activity of transcription factors downstream of ErbB signaling. This indicates that endocrine therapy plus CDK4/6i activated compensatory ErbB growth factor signaling in resistant cells, supporting an established estrogen-independent state of proliferation. To further understand this shift in cell state, we used afatinib, an ErbB family blocker, in combination with ribociclib to target this resistance mechanism. We developed multiple in vitro three-dimensional ER+BC cell culture models that are sensitive or resistant to ribociclib. Samples were collected regularly during monotherapy or combination of ribociclib and afatinib treatment and bulk RNA-sequencing measured the dynamical changes in the cell state over short and long timescales. Generalized additive models quantitatively evaluated cancer cell plasticity and synergy of combination therapy using the RNA-seq data. Our models revealed how activity of signaling pathways drive transcriptional changes in cell cycle regulators that promote or inhibit cell cycle progression. We found that ribociclib plus afatinib synergize to promote early and sustained cell cycle arrest, and later apoptosis, in both sensitive and resistant cells. Combination therapy simultaneously restored G1/S phase regulation by inhibiting the cyclin E/CDK2 complex and enforced G2/M phase control by inhibiting the cyclin B/CDK1 complex, thereby blocking entry into mitosis. This dual inhibition strategy not only controlled cancer proliferation, but also induced caspase-dependent apoptosis. Overall, our study revealed a key CDK4/6i resistance mechanism, and demonstrated that a combination of ribociclib plus afatinib overcomes intrinsic mechanisms of resistance in ER+BC.
This dataset
Open GSE284956 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 GSE284956_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1200729 and SRA study SRP552772. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
64 conditions, mostly in triplicate
MCF-7 Sensitive, DMSO, Day0 H ×3 MCF-7 Ribociclib-Resistant, DMSO, Day0 H ×3 MCF-7 Sensitive, DMSO, Day6 H ×3 MCF-7 Sensitive, Ribociclib, Day6 H ×3 MCF-7 Sensitive, Afatinib, Day6 H ×3 MCF-7 Sensitive, Ribociclib + Afatinib, Day6 H ×3 MCF-7 Ribociclib-Resistant, DMSO, Day6 H ×3 MCF-7 Ribociclib-Resistant, Ribociclib, Day6 H ×3 +56 more

Supports a between-group comparison across 180 samples.

How we read this: 64 replicated groups read from 180 sample titles; they account for 180 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

+ 140 more — browse all 180 samples with per-sample file links →

More Breast cancer datasets

Search all human RNA-seq datasets in GEO →

Share this dataset

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.