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Identification of neoadjuvant chemotherapy response transcriptomic meta-signatures in invasive breast cancer in Latino patients

GSE280902 Homo sapiens (human) RNA-seq (gene expression) 58 samples Submitted 2025/12/17 Platform GPL30173
At a glance

RNA-seq (gene expression) · human · 58 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
58 samples: 58 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
Identification of predictive pretreatment biomarkers for neoadjuvant chemotherapy response in Latino invasive breast cancer patientsMolecular medicine (Cambridge, Mass.) · 2025
Summary (written by the submitters)
Breast cancer is a highly heterogeneous disease with varying incidences and mortality rates among women from different populations worldwide. Neoadjuvant chemotherapy (NAC) is the standard treatment for locally advanced breast cancer; however, its efficacy differs significantly among breast cancer subtypes. This study aimed to investigate the genes linked to response to NAC in Colombian women with invasive breast cancer.
Read the full summary ↓
RNA sequencing was performed on formalin-fixed paraffin-embedded tissues from 58 patients, categorized into 29 NAC responders and 29 non-responders. Differentially expressed genes (DEGs) were identified for each molecular subtype and their prognostic performance was evaluated using risk scores. Functional enrichment analysis revealed pathways related to the immune system in the non-responders. Cytokine target activity and immune cell population changes were assessed to understand the role of the TME in the response to treatment. APOD, CCL19, GPR132, FGF10, and HBB were identified as independent predictors of NAC response, with APOD showing a protective effect in LuminalB/Her2- patients. RT-PCR and immunohistochemistry were used to validate these findings. Drug sensitivity analysis showed varying sensitivities to potential therapeutic drugs among the non-responders. This study highlights the importance of identifying specific gene expression profiles and immune cell population changes to predict NAC response in patients with breast cancer, potentially leading to personalized and effective treatment strategies for the Colombian population
Published in
Identification of predictive pretreatment biomarkers for neoadjuvant chemotherapy response in Latino invasive breast cancer patients
Guevara-Nieto HM, Parra-Medina R, Orozco CA et al. · Molecular medicine (Cambridge, Mass.) 2025 · PMID 41366282 · doi:10.1186/s10020-025-01338-8
This dataset
Open GSE280902 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 GSE280902_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 PRJNA1181008 and SRA study SRP542669. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41366282) ↗
Study design
58 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 58 sample titles: 58 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.

Samples in this study

+ 18 more — browse all 58 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.