Computational pathology annotation enhances the resolution and interpretation of breast cancer spatial transcriptomics data
https://doi.org/10.48723/f4v5-m008
The samples in the dataset are connected to a study focusing on studying breast cancer intratumoral heterogeneity using spatial transcriptomic data and computational pathology. The dataset contains 14 samples from 3 patients (one triple negative breast cancer and two HER2-positive breast cancer). Multiple regions of the tumor were collected for analysis. Each sample is one tumor region from one of the patients.
Libraries for spatial transcriptomics were prepared using Visium spatial gene expression kits (10x genomics). Sequencing was performed using the Illumina NovaSeq 6000 platform at the National Genomics Infrastructure, SciLifeLab in Solna, Sweden.
The dataset contains 28 fastq files, compressed with GNUzip (gzip), from paired-end RNA sequencing (10X Visium spatial transcriptomics). The meta data is described in SND_metadata.xlsx file. The md5sum.txt file is provided for validation of data integrity. The total size of the dataset is approximately 300 GB.
Citation and access
Citation and access
Data access level:
Creator/Principal investigator(s):
Research principal:
Data contains personal data:
Yes
Type of personal data:
Transcriptomic data
Code key exists:
Yes
Sensitive personal data:
Yes
Citation:
Language:
Method and outcome
Method and outcome
Population:
Breast cancer samples were collected at Karolinska University Hospital, Stockholm, Sweden from patients with untreated invasive ductal carcinomas.
Study design:
- Experimental study
Sampling procedure:
Description of sampling:
Samples contained in this repository are random patient samples that fit our selection criteria (i.e., big tumor size and treatment naive). Patients' samples were collected from multiple regions of the same tumor to investigate the intra-tumoral heterogeneity.
Number of individuals/objects:
3
Data format/data structure:
Samples/material - Existing from scientific collection/biobank
Samples/material - Existing from scientific collection/biobank
Name:
Stockholms medicinska biobank
Type(s) of sample:
Tumor sample
Geographic coverage
Geographic coverage
Geographic location:
Administrative information
Administrative information
Responsible department/unit:
Department of Oncology-Pathology [K7]
Ethics Review 1:
Swedish Ethical Review Authority - 2016/957-31
Ethics Review 2:
Swedish Ethical Review Authority - 2017/742-32
Ethics Review 3:
Swedish Ethical Review Authority - 2021-00795
Ethics Review 4:
Swedish Ethical Review Authority - 2022-05245-02
Topic and keywords
Topic and keywords
Standard för svensk indelning av forskningsämnen 2025:
Keywords:
Publications
Publications
Citation:
Li, T., Yang, Q., Acs, B., Sifakis, E. G., Toosi, H., Engblom, C., Thrane, K., Lin, Q., Mold, J. E., Sun, W., Boyaci, C., Steen, S., Frisén, J., Lagergren, J., Lundeberg, J., Chen, X., & Hartman, J. (2025). Computational pathology annotation enhances the resolution and interpretation of breast cancer spatial transcriptomics data. In npj Precision Oncology (No. 310; Vol. 9, Issue 1). https://doi.org/10.1038/s41698-025-01104-3Opens in a new tab
