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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.
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Documentation files

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

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

Name:

Stockholms medicinska biobank

Type(s) of sample:

Tumor sample

Geographic coverage

Geographic location:

Administrative information

Responsible department/​unit:

Department of Oncology-Pathology [K7]

Ethical Review

Reviewer:

Registration number:

2016/957-31

Reviewer:

Registration number:

2017/742-32

Reviewer:

Registration number:

2021-00795

Reviewer:

Registration number:

2022-05245-02

Topic and keywords

Swedish Standard Classification of Research Subjects 2025:

Keywords:

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/s41698-025-01104-3

SwePub:

oai:DiVA.org:kth-370404

Contact

Xinsong Chenxinsong.chen@ki.se
Johan Hartmanjohan.hartman@ki.se

Metadata

Version 1
doris
ki
Version 1 published:Metadata updated: