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High mechanical conditioning by tumor extracellular matrix stiffness is a predictive biomarker for anti-fibrotic therapy in HER2-negative breast cancer

GSE255359 Homo sapiens (human) RNA-seq (gene expression) 108 samples Submitted 2024/08/19 Platform GPL21697
At a glance

RNA-seq (gene expression) · human · 108 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
108 samples: 10 conditions, mostly in duplicateGroups 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
High Mechanical Conditioning by Tumor Extracellular Matrix Stiffness Is a Predictive Biomarker for Antifibrotic Therapy in HER2-Negative Breast CancerClinical cancer research : an official journal of the American Association for Cancer Research · 2024
Summary (written by the submitters)
Background Tumor progression has been linked to stiffening of the extracellular matrix (ECM) caused by fibrosis. Cancer cells can be mechanically conditioned by stiff ECM, exhibiting a 1004-gene signature (MeCo score). Nintedanib has demonstrated anti-fibrotic activity in idiopathic pulmonary fibrosis. This study explores nintedanib’s anti-fibrotic effect on breast cancer outcomes.
Read the full summary ↓
Methods We present long-term follow-up and analysis of a neoadjuvant randomized Phase 2 trial in early HER2-negative breast cancer. Patients (N = 130) underwent a baseline biopsy and received 12 paclitaxel courses alone (control arm) or in combination with nintedanib (experimental arm). Tumor MeCo score was determined by RNAseq. The primary aim was to assess nintedanib's impact on event-free survival (EFS) based on MeCo scores. Results Follow-up data were retrieved from 111 patients; 75 baseline and 24 post-run-in phase samples were sequenced. After median follow-up of 9.67 years, median EFS was not statistically different between arms (P = 0.37). However, in the control arm, High versus Low MeCo patients had a statistically higher relapse risk: hazard ratio (HR) = 0.21; P = 0.0075. This risk was corrected by nintedanib in the experimental arm: HR = 0.37; P = 0.16. Nintedanib demonstrated pharmacodynamic engagement, reducing the MeCo score by 25% during the run-in phase (P<0.01). Patients with Low MeCo after run-in had the best long-term prognosis (HR = 0.087; P = 0.03). Conclusions High MeCo is predictive of poor outcomes in HER2-negative early breast cancer, although this risk can be mitigated by nintedanib, which is able to specifically reduce mechanical conditioning.
Published in
High Mechanical Conditioning by Tumor Extracellular Matrix Stiffness Is a Predictive Biomarker for Antifibrotic Therapy in HER2-Negative Breast Cancer
Quintela-Fandino M, Bermejo B, Zamora E et al. · Clinical cancer research : an official journal of the American Association for Cancer Research 2024 · PMID 39283720 · doi:10.1158/1078-0432.CCR-24-1518
This dataset
Open GSE255359 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 GSE255359_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 PRJNA1074736 and SRA study SRP488942. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39283720) ↗
Study design
10 conditions, mostly in duplicate
mjb-5001 ×16 mjb-137 ×13 mjb-148 ×10 mjb-93 ×9 mjb-136 ×7 mjb-44 ×6 mjb-55 ×4 mjb-19 ×3 +38 more

Supports a between-group comparison across 72 samples.

How we read this: 10 replicated groups read from 108 sample titles; they account for 72 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

+ 68 more — browse all 108 samples with per-sample file links →

More Breast cancer datasets

Search all human RNA-seq datasets in GEO →

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