Distinct Cellular Mechanisms Underlie Chemotherapies and Their Combinations with PD-L1 Checkpoint Inhibitor in Triple-Negative Breast Cancer [TNBC]
Single-cell RNA-seq · human · 117 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
- 117 samples: 117 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
- Distinct cellular mechanisms underlie chemotherapies and PD-L1 blockade combinations in triple-negative breast cancerCancer cell · 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 GSE266919_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 PRJNA1108722. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 117 sample titles: 117 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.
- GSM8256541 P037, Pre-treatment, scRNA-seq
- GSM8256542 P037, Post-treatment, scRNA-seq
- GSM8256543 P038, Pre-treatment, scRNA-seq
- GSM8256544 P038, Post-treatment, scRNA-seq
- GSM8256545 P041, Pre-treatment, scRNA-seq
- GSM8256546 P041, Post-treatment, scRNA-seq
- GSM8256547 P032, Pre-treatment, scRNA-seq
- GSM8256548 P039, Pre-treatment, scRNA-seq
- GSM8256549 P039, Post-treatment, scRNA-seq
- GSM8256550 P031, Pre-treatment, scRNA-seq
- GSM8256551 P044, Pre-treatment, scRNA-seq
- GSM8256552 P034, Pre-treatment, scRNA-seq
- GSM8256553 P034, Post-treatment, scRNA-seq
- GSM8256554 P040, Pre-treatment, scRNA-seq
- GSM8256555 P040, Post-treatment, scRNA-seq
- GSM8256556 P040-L, Pre-treatment, scRNA-seq
- GSM8256557 P040-L, Post-treatment, scRNA-seq
- GSM8256558 P059, Pre-treatment, scRNA-seq
- GSM8256559 P059, Post-treatment, scRNA-seq
- GSM8256560 P061, Pre-treatment, scRNA-seq
- GSM8256565 P060, Pre-treatment, scRNA-seq
- GSM8256566 P060, Post-treatment, scRNA-seq
- GSM8256568 P054, Pre-treatment, scRNA-seq
- GSM8256569 P054, Post-treatment, scRNA-seq
- GSM8256570 P045, Pre-treatment, scRNA-seq
- GSM8256571 P045, Post-treatment, scRNA-seq
- GSM8256572 P051, Pre-treatment, scRNA-seq
- GSM8256573 P051, Post-treatment, scRNA-seq
- GSM8256574 P049, Pre-treatment, scRNA-seq
- GSM8256575 P049, Post-treatment, scRNA-seq
- GSM8256576 P058, Pre-treatment, scRNA-seq
- GSM8256577 P058, Post-treatment, scRNA-seq
- GSM8256578 P056, Pre-treatment, scRNA-seq
- GSM8256579 P056, Post-treatment, scRNA-seq
- GSM8256580 P052, Pre-treatment, scRNA-seq
- GSM8256581 P052, Post-treatment, scRNA-seq
- GSM8256582 P062, Pre-treatment, scRNA-seq
- GSM8256583 P047, Pre-treatment, scRNA-seq
- GSM8256584 P047, Post-treatment, scRNA-seq
- GSM8256585 P053, Pre-treatment, scRNA-seq
+ 77 more — browse all 117 samples with per-sample file links →
- GSE10885 An integromic analysis reveals that metaplastic carcinomas are distinct from basal-like tumors. 244 samples same disease briefing · shares distinct
- GSE104193 Hypoxia-mediated translational activation of ITGB3 in breast cancer cells enhances TGF-β signalling and malignant features in vitro and in vivo 32 samples same disease briefing
- GSE104675 Role for the Transcriptional Activator ZRF1 in Breast Cancer Progression and Endocrine Resistance 6 samples same disease briefing
- GSE10886 A Supervised Risk Predictor of Breast Cancer Based on Biological Subtypes 226 samples same disease briefing
- GSE11121 The humoral immune system has a key prognostic impact in node-negative breast cancer 200 samples same disease briefing
- GSE310025 Intrinsic RB activation induces tumoral and stromal anti-tumor responses that limit triple-negative breast cancer [RNA-Seq] 28 samples shares triple-negative
- GSE322659 Nitric Oxide Synthase 2-Dependent Metabolic Dysregulation, Immunosuppression, and Genomic Instability in Metastatic Triple-Negative Breast Cancer 24 samples shares triple-negative
- GSE290037 Loss of IL13RA2 promotes metastatic tumour growth in triple-negative breast cancer via increased AKT signalling 21 samples shares triple-negative
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.