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Discovery and Evaluation of Biomarkers for Triple-Negative Breast Cancer Subtypes Uncovers Patient Stratification and Targeted Therapeutic Strategies

GSE300385 Homo sapiens (human) Single-cell RNA-seq 12 samples Submitted 2026/02/04 Platform GPL28038
At a glance

Single-cell RNA-seq · human · 12 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
12 samples: 3 × BT549_CT vs 3 × BT549_KO vs 3 × Hs578T_CT vs 3 × Hs578T_KOGroups 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
Discovery and Evaluation of Biomarkers for Triple-Negative Breast Cancer Subtypes Uncovers Patient Stratification and Targeted Therapeutic StrategiesCancer research · 2026
Summary (written by the submitters)
Triple-negative breast cancer (TNBC) is the most heterogeneous and aggressive subtype of breast carcinoma, defined by the absence of clinical biomarkers and the lack of targeted therapies. Despite numerous clinical trials, patient stratification remains suboptimal, limiting the identification of effective treatment strategies. In this study, we aimed to identify biomarkers exclusively expressed in the basal mammary epithelial compartment to refine TNBC subclassification.
Read the full summary ↓
Through computational analysis of single-cell RNA sequencing data, we defined a set of basal identity genes, which were subsequently validated by immunohistochemistry in two independent TNBC cohorts. This approach enabled the identification of a novel TNBC subgroup, termed true basal TNBC (tB-TNBC), associated with poorer prognosis and distinct molecular features. To uncover therapeutic vulnerabilities in this subgroup, we conducted a high-throughput screen of 3,200 FDA-approved compounds in breast cancer cell lines classified by basal marker expression. This analysis identified dasatinib as a promising candidate with selective activity against tB-TNBC models. Furthermore, TAGLN emerged as a strong predictive biomarker of dasatinib response, with functional studies confirming its role in modulating drug sensitivity. Altogether, these findings support the clinical utility of basal markers for TNBC stratification and highlight a targeted treatment opportunity for tB-TNBC patients.
Published in
Discovery and Evaluation of Biomarkers for Triple-Negative Breast Cancer Subtypes Uncovers Patient Stratification and Targeted Therapeutic Strategies
Ortega-Álvarez D, Tébar-García D, Casado-Peláez M et al. · Cancer research 2026 · PMID 41671401 · doi:10.1158/0008-5472.CAN-24-2758
This dataset
Open GSE300385 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 GSE300385_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 PRJNA1280172 and SRA study SRP593500. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41671401) ↗
Study design
3 × BT549_CT vs 3 × BT549_KO vs 3 × Hs578T_CT vs 3 × Hs578T_KO

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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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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.