Trastuzumab-associated cardiotoxicity in Patient Specific iPSC-derived cardiomyocytes from HER2+ breast cancer patients
RNA-seq (gene expression) · human · 74 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
- 74 samples: 74 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.
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 GSE264120_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 PRJNA1101013 and SRA study SRP502120. 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 74 sample titles: 74 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.
- GSM8212295 102-4R P24D39 T0 [01_S70]
- GSM8212296 102-4R P24D39 T200 [02_S05]
- GSM8212297 102-4R P24D39 T400 [03_S64]
- GSM8212298 102-4R P28D48-1 T0 [04_S68]
- GSM8212299 102-4R P28D48-1 T200 [05_S67]
- GSM8212300 102-4R P28D48-1 T400 [06_S22]
- GSM8212301 102-4R P28D48-2 T0 [07_S73]
- GSM8212302 102-4R P28D48-2 T200 [08_S57]
- GSM8212303 102-4R P28D48-2 T400 [09_S58]
- GSM8212304 102-4R P29D32 T0 [10_S25]
- GSM8212305 102-4R P29D32 T200 [11_S18]
- GSM8212306 NT1-15 P25D29 T0 [13_S43]
- GSM8212307 NT1-15 P25D29 T200 [14_S35]
- GSM8212308 NT1-21 P43D42 T0 [15_S03]
- GSM8212309 NT1-21 P43D42 T200 [16_S24]
- GSM8212310 NT1-21 P43D42 T400 [17_S34]
- GSM8212311 NT1-21 P43D37 T0 [18_S31]
- GSM8212312 NT1-21 P43D37 T200 [19_S55]
- GSM8212313 NT1-21 P43D37 T400 [20_S48]
- GSM8212314 NT1-21 P45D35 T0 [21_S50]
- GSM8212315 NT1-21 P45D35 T200 [22_S47]
- GSM8212316 NT1-21 P45D35 T400 [23_S38]
- GSM8212317 NT1-32 P35D37 T0 [24_S56]
- GSM8212318 NT1-32 P35D37 T200 [25_S23]
- GSM8212319 NT1-32 P35D37 T400 [26_S41]
- GSM8212320 NT1-32 P36D35 T0 [27_S13]
- GSM8212321 NT1-32 P36D35 T200 [28_S12]
- GSM8212322 NT1-32 P36D35 T400 [29_S19]
- GSM8212323 NT2-22 P25D35 T0 [30_S30]
- GSM8212324 NT2-22 P25D35 T200 [31_S08]
- GSM8212325 NT2-22 P40D37 T0 [32_S02]
- GSM8212326 NT2-22 P40D37 T200 [33_S15]
- GSM8212327 NT2-22 P43D36 T0 [34_S07]
- GSM8212328 NT2-22 P43D36 T200 [35_S49]
- GSM8212329 NT2-23 P23D32 T0 [36_S52]
- GSM8212330 NT2-23 P23D32 T200 [37_S42]
- GSM8212331 NT2-23 P43D41 T0 [38_S40]
- GSM8212332 NT2-23 P43D41 T200 [39_S45]
- GSM8212333 NT3-4 P35D37 T0 [40_S66]
- GSM8212334 NT3-4 P35D37 T200 [41_S61]
+ 34 more — browse all 74 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
- GSE243375 HER2 heterogeneity and treatment response-associated profiles in HER2-positive breast cancer in the NCT02326974 clinical trial 287 samples shares her2, breast
- GSE234519 NSABP B-41: A Study of AC Followed by a Combination of Paclitaxel Plus Trastuzumab or Lapatinib or Both Given Before Surgery to Patients With Operable HER2 Positive Invasive Breast Cancer 245 samples shares her2, breast
- GSE255359 High mechanical conditioning by tumor extracellular matrix stiffness is a predictive biomarker for anti-fibrotic therapy in HER2-negative breast cancer 108 samples shares her2, 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.