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Single Cell Smart-Seq 3 RNA-Seq and Bulk Exome Seq from Breast Cancer Patients

posted on 2021-08-17, 22:03 authored by Seong-Hwan Jun, Hosein ToosiHosein Toosi, Jeff Mold, Camilla Engblom, Xinsong Chen, Ciara O’Flanagan, Michael Hagemann-Jensen, Rickard Sandberg, Johan Hartman, Samuel Aparicio, Andrew Roth, Jens Lagergren
Data Set Description

Single cell RNA sequencing (Samrt-Seq3) and Whole exome sequencing from multiple regions of individual tumors from Breast Cancer patients and also single cell RNA seq for two ovarian cancer cell lines.

The dataset contains raw sequencing data for various high-throughput molecular tests performed on two sample types: tumor samples from two breast cancer patients and cell lines derived from High-grade serous carcinoma Patients.

The breast cancer data comes from two patients: patient 1 (BCSA1) has two tumor regions A-B and patient 2 (BCSA2) has five regions(A-E). For a normal sample and each region from each patient Whole Exome Sequencing was performed using Twist Biosciences Human Exome Kit by the SNP&SEQ Technology platform, SciLifeLab, National Genomics Infrastructure Uppsala, Sweden. Also for each patient, EPCAM+ CD45- sorted cells from all the regions where sorted to a 384 well plate, and Smart-Seq3 libraries were prepared at Karolinska Institutet and sequenced at National Genomics Infrastructure Uppsala, Sweden.

The HGSOC cell-line data comes from OV2295R2 and TOV2295R cell lines described in Laks et al
Cell 2019 Nov 14; 179(5): 1207–1221.e22 doi: 10.1016/j.cell.2019.10.026 . The cell line Smart-Seq3 libraries were prepared from two 384 well plates at Karolinska Institutet and
sequenced at National Genomics Infrastructure Uppsala, Sweden.

Terms for access
This dataset is to be used for research on intratumor heterogeneity and subclonal evolution of tumors.
To apply for conditional access to the dataset in this publication, please contact


The computations were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at Uppsala University and Linköping University partially funded by the Swedish Research Council through360grant agreement no. 2018-05973. This project was made possible through funding by the Michael Smith Foundation361for Health Research Scholar Award [18245 to AR] and by generous support from the Swedish Foundation for362Strategic Research grant BD15-0043. SHJ was supported by Postdoctoral Fellowship from the Natural363Sciences and Engineering Research Council of Canada.



KTH Royal Institute of Technology

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