RNA-Seq profiling of pre- and on-treatment tumor samples from breast cancer patients enrolled in the NeoTRIP trial.
RNA-seq (gene expression) · human · 401 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
- 401 samples: 401 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
- Longitudinal transcriptomic profiling identifies predictors of response to neoadjuvant chemoimmunotherapy in triple-negative breast cancer: results from the NeoTRIPaPDL1 trialAnnals of oncology : official journal of the European Society for Medical Oncology · 2026
Read the full summary ↓
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE319641_RAW.tar (it can run to tens of gigabytes).
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.
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 PRJNA1424908. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from the first 400 of 401 sample titles: 400 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.
- GSM9521946 Tumor sample of patient TNBC00001 at baseline
- GSM9521947 Tumor sample of patient TNBC00001 at day 1 of cycle 2
- GSM9521948 Tumor sample of patient TNBC00988 at baseline
- GSM9521949 Tumor sample of patient TNBC01436 at baseline
- GSM9521950 Tumor sample of patient TNBC01436 at day 1 of cycle 2
- GSM9521951 Tumor sample of patient TNBC01926 at baseline
- GSM9521952 Tumor sample of patient TNBC03683 at baseline
- GSM9521953 Tumor sample of patient TNBC03683 at day 1 of cycle 2
- GSM9521954 Tumor sample of patient TNBC03920 at baseline
- GSM9521955 Tumor sample of patient TNBC03920 at day 1 of cycle 2
- GSM9521956 Tumor sample of patient TNBC04100 at baseline
- GSM9521957 Tumor sample of patient TNBC04687 at baseline
- GSM9521958 Tumor sample of patient TNBC05791 at baseline
- GSM9521959 Tumor sample of patient TNBC05791 at day 1 of cycle 2
- GSM9521960 Tumor sample of patient TNBC06123 at baseline
- GSM9521961 Tumor sample of patient TNBC06135 at baseline
- GSM9521962 Tumor sample of patient TNBC06135 at day 1 of cycle 2
- GSM9521963 Tumor sample of patient TNBC06324 at baseline
- GSM9521964 Tumor sample of patient TNBC06324 at day 1 of cycle 2
- GSM9521965 Tumor sample of patient TNBC06653 at baseline
- GSM9521966 Tumor sample of patient TNBC06653 at day 1 of cycle 2
- GSM9521967 Tumor sample of patient TNBC06775 at baseline
- GSM9521968 Tumor sample of patient TNBC07076 at day 1 of cycle 2
- GSM9521969 Tumor sample of patient TNBC07174 at baseline
- GSM9521970 Tumor sample of patient TNBC07348 at baseline
- GSM9521971 Tumor sample of patient TNBC07550 at baseline
- GSM9521972 Tumor sample of patient TNBC07550 at day 1 of cycle 2
- GSM9521973 Tumor sample of patient TNBC07711 at baseline
- GSM9521974 Tumor sample of patient TNBC07895 at baseline
- GSM9521975 Tumor sample of patient TNBC07895 at day 1 of cycle 2
- GSM9521976 Tumor sample of patient TNBC08166 at baseline
- GSM9521977 Tumor sample of patient TNBC08166 at day 1 of cycle 2
- GSM9521978 Tumor sample of patient TNBC08172 at baseline
- GSM9521979 Tumor sample of patient TNBC08670 at baseline
- GSM9521980 Tumor sample of patient TNBC09613 at baseline
- GSM9521981 Tumor sample of patient TNBC10344 at baseline
- GSM9521982 Tumor sample of patient TNBC10344 at day 1 of cycle 2
- GSM9521983 Tumor sample of patient TNBC10463 at baseline
- GSM9521984 Tumor sample of patient TNBC10463 at day 1 of cycle 2
- GSM9521985 Tumor sample of patient TNBC10855 at baseline
+ 361 more — browse all 401 samples with per-sample file links →
- GSE104193 Hypoxia-mediated translational activation of ITGB3 in breast cancer cells enhances TGF-β signalling and malignant features in vitro and in vivo 32 samples same disease briefing · shares breast
- GSE104675 Role for the Transcriptional Activator ZRF1 in Breast Cancer Progression and Endocrine Resistance 6 samples same disease briefing · shares breast
- GSE10886 A Supervised Risk Predictor of Breast Cancer Based on Biological Subtypes 226 samples same disease briefing · shares breast
- GSE11121 The humoral immune system has a key prognostic impact in node-negative breast cancer 200 samples same disease briefing · shares breast
- GSE10885 An integromic analysis reveals that metaplastic carcinomas are distinct from basal-like tumors. 244 samples same disease briefing
- GSE261713 Linking single cell genomes and transcriptomes at scale to decode breast cancer progression [scRNA-seq] 35 samples shares rna-seq, breast
- GSE303346 Single-Cell RNA-Seq Reveals Immunosuppressive Effects of Triple-Negative Breast Cancer–Derived Exosomes on NK Cell Responses 16 samples shares rna-seq, breast
- GSE245414 RNA-seq profiling of breast metastases from different sites 42 samples shares rna-seq, breast
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.