Triple negative breast cancer (TNBC) is an aggressive and clinically challenging subtype of breast cancer. TNBC disporportionately affects African-American (AA) women, but the biological basis of this disparity is not understood. To gain a better understanding of TNBC, particularly in AA patients, we applied spatial transcriptomics to thoroughly characterize gene expression and tissue architecture in TNBC. Our cohort consisted of 22 patients, 15 of whom were AA, and 7 who were Caucasian. We obtained 2 sections from each patient's tumor (except for patient 19) for a total of 43 samples.
from sddb import Catalog
cohort = Catalog().collection("geo-gse210616")
cohort.members # the 43 datasets as a table
print(cohort.licenses()) # check the terms before you download
sdatas = cohort.to_dict() # {uid: SpatialData}, cached locally
# or keep the .zarr files in a folder of your choice:
# sdatas = cohort.to_dict(dest="data/")Needs pip install lamindb and network access to the Lamin database, which is limited during the alpha. If it fails, use the Client tab.
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
coll = db.Collection.get(key="geo-gse210616")
coll.ordered_artifacts.to_dataframe() # the 43 members, ordered
# download every member .zarr to the local cache:
paths = [a.cache() for a in coll.ordered_artifacts.all()]# sddb client: download every member .zarr into data/
sddb-cli download --collection geo-gse210616 --dest data/Licenses: 43 NCBI GEO policy
@article{Bassiouni_2022,
title = {Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer},
volume = {83},
ISSN = {1538-7445},
url = {http://dx.doi.org/10.1158/0008-5472.can-22-2682},
DOI = {10.1158/0008-5472.can-22-2682},
number = {1},
journal = {Cancer Research},
publisher = {American Association for Cancer Research (AACR)},
author = {Bassiouni, Rania and Idowu, Michael O. and Gibbs, Lee D. and Robila, Valentina and Grizzard, Pamela J. and Webb, Michelle G. and Song, Jiarong and Noriega, Ashley and Craig, David W. and Carpten, John D.},
year = {2022},
month = Oct,
pages = {34–48}
}