Single-Cell RNA Sequencing Identifies Molecular Biomarkers Predicting Response to CDK4/6 Inhibition in Metastatic HR+/HER2- Breast Cancer
Single-cell RNA-seq · human · 19 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
- Organism
- Homo sapiens (human)
- Samples
- 19 samples: 18 distinct titles, 1 of them repeatedGroups 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
- Single-cell RNA sequencing identifies molecular biomarkers predicting late progression to CDK4/6 inhibition in patients with HR+/HER2- metastatic breast cancerMolecular cancer · 2025
Read the full summary ↓
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE262288_RAW.tar (it can run to tens of gigabytes).
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.
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 PRJNA1090971 and SRA study SRP497314. Searching any of these in the dataset finder brings you back here.
How we read this: Read from 19 sample titles: 18 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.
- GSM8162619 Patient 1 pleural effusion scRNA-seq sample
- GSM8162620 Patient 3 ascites scRNA-seq sample 1
- GSM8162621 Patient 3 ascites scRNA-seq sample 2
- GSM8162622 Patient 126 liver scRNA-seq sample
- GSM8162623 Patient 130 liver scRNA-seq sample
- GSM8162624 Patient 139 liver scRNA-seq sample
- GSM8162625 Patient 99 pleural effusion scRNA-seq sample
- GSM8162626 Patient 165 pelvic bone scRNA-seq sample
- GSM8162627 Patient 46 pleural effusion scRNA-seq sample
- GSM8162628 Patient 11 pleural effusion scRNA-seq sample
- GSM8162629 Patient 144 liver scRNA-seq sample
- GSM8162630 Patient 3 liver scRNA-seq sample
- GSM8162631 Patient 5 pleural effusion scRNA-seq sample
- GSM8162632 Patient 45 liver scRNA-seq sample
- GSM8162633 Patient 95 liver scRNA-seq sample
- GSM8162634 Patient 110 pleural effusion scRNA-seq sample
- GSM8162635 Patient 120 liver scRNA-seq sample
- GSM8162636 Patient 131 liver scRNA-seq sample
- GSM8162637 Patient 153 liver scRNA-seq sample
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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.