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HER2 heterogeneity and treatment response-associated profiles in HER2-positive breast cancer in the NCT02326974 clinical trial

GSE243375 Homo sapiens (human) RNA-seq (gene expression) 287 samples Submitted 2024/02/29 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 287 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
287 samples: 282 distinct titles, 5 of them repeatedGroups 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
HER2 heterogeneity and treatment response-associated profiles in HER2-positive breast cancer in the NCT02326974 clinical trialThe Journal of clinical investigation · 2024
Summary (written by the submitters)
HER2-targeted therapies including antibody drug conjugates have shown great efficacy in HER2-positive breast cancer. However, resistance to treatment in part due to pre-existing HER2 heterogeneity (HET) is a significant clinical challenge and requires the use of rationally-designed combination therapies. Here we describe transcriptomic profiling of 287 biopsies from 129 patients in a phase II neoadjuvant clinical trial using a combination of T-DM1 and pertuzumab for early-stage HER2-positive breast cancer, we investigated the mechanisms driving T-DM1 resistance.
Read the full summary ↓
In pre-treatment tumors from patients without a complete response (pCR), distinct molecular features were identified: HER2 HET tumors retained PI3K pathway activation and a basal-like phenotype, while HER2 Non-HET tumors exhibited lower PI3K pathway enrichment. T-DM1 treatment universally reduced HER2 protein expression and copy number heterogeneity while increasing PI3K pathway enrichment and luminal identity in residual tumors. Importantly, HER2 HET tumors showed minimal transcriptomic response compared to HER2 Non-HET tumors, in line with the lesser clinical response. The response to T-DM1 was not associated with hotspot PIK3CA/ERBB2 mutations but rather correlated with immune infiltration. HER2 Non-HET tumors exhibited less effective immune surveillance at baseline but became immune activated with T-DM1 treatment, while HER2 HET tumors exhibited a more immune suppressed microenvironment upon treatment. Our study highlights the multifaceted nature of T-DM1 resistance driven by HER2 heterogeneity and provides essential evidence for optimizing therapeutic strategies for these patients.
Published in
HER2 heterogeneity and treatment response-associated profiles in HER2-positive breast cancer in the NCT02326974 clinical trial
Li Z, Metzger Filho O, Viale G et al. · The Journal of clinical investigation 2024 · PMID 38300710 · doi:10.1172/JCI176454
This dataset
Open GSE243375 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 GSE243375_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 PRJNA1018108 and SRA study SRP461212. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38300710) ↗
Study design
282 distinct titles, 5 of them repeated
21_site1_core ×2 46_site2_core ×2 48_site1_core ×2 54_site1_core ×2 60_site2_core ×2

How we read this: Read from 287 sample titles: 282 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

+ 247 more — browse all 287 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.