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The spatially informed mFISHseq assay resolves biomarker discordance and predicts treatment response in breast cancer

GSE283522 Homo sapiens (human) RNA-seq (gene expression) 1254 samples Submitted 2024/12/23 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 1254 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
1254 samples: 333 × 3038_20 vs 15 × 08T vs 10 × 07TGroups 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
The spatially informed mFISHseq assay resolves biomarker discordance and predicts treatment response in breast cancerNature communications · 2025
Summary (written by the submitters)
Background: Breast cancer (BCa) is a heterogeneous disease requiring precise diagnostics to guide effective treatment. Current assays fail to adequately address the complex biology of BCa subtypes/risk groups and accurately predict responses to treatments like antibody-drug conjugates (ADCs). To address these limitations, we developed and validated a novel diagnostic, prognostic, and predictive tool, mFISHseq.
Read the full summary ↓
Methods: Our approach, mFISHseq, integrates multiplexed RNA fluorescent in situ hybridization with RNA-sequencing, guided by laser capture microdissection. This technique ensures tumor purity, allows unbiased profiling of whole transcriptome data, and explicitly quantifies intratumoral heterogeneity. Results: In a retrospective cohort study involving 1,082 FFPE breast tumors, mFISHseq demonstrated high analytical validity with 93% accuracy compared to immunohistochemistry across training and test sets. Our consensus subtyping approach provided near-perfect concordance with other molecular classifiers (κ > 0.85) and reclassified 30% of samples into subtypes with distinct prognostic implications. Consensus risk groups mitigated misclassification of single samples and provided prognostic information about both early and late relapse. High risk patients had enriched innate and adaptive immune signatures, which predicted response to neoadjuvant immunotherapy. Furthermore, we identified patients responsive to ADCs, as evidenced by a 19-feature classifier for T-DM1 sensitivity, validated on the multicenter, phase II, prospective I-SPY2 trial. To demonstrate the clinical potential, we deployed mFISHseq as a research use only test on 48 patients, revealing insights into the efficacy of novel targeted therapies, such as CDK4/6 inhibitors, immune checkpoint inhibitors, and ADCs. Conclusion: The mFISHseq method solves a long-standing challenge in the precise diagnosis and classification of BCa subtypes/prognostic risk groups, and allows accurate response prediction for patients, including those treated with immunotherapies and ADCs.
Published in
The spatially informed mFISHseq assay resolves biomarker discordance and predicts treatment response in breast cancer
Paul ED, Huraiová B, Valková N et al. · Nature communications 2025 · PMID 39747865 · doi:10.1038/s41467-024-55583-2
This dataset
Open GSE283522 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 GSE283522_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 PRJNA1190437 and SRA study SRP547489. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39747865) ↗
Study design
333 × 3038_20 vs 15 × 08T vs 10 × 07T

Supports a between-group comparison across 358 samples.

How we read this: 3 replicated groups read from the first 400 of 1254 sample titles; they account for 358 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

+ 1214 more — browse all 1254 samples with per-sample file links →

More Breast cancer datasets

Search all human RNA-seq datasets in GEO →

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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.