Endocrine therapy resistance in ER+ breast cancer remains a major therapeutic challenge in clinic. This study offers a robust workflow to identify ET-insensitive cell populations that persist under selective pressure of ET in clinical samples and experimental models. We demonstrated that these populations are transcriptionally diverse and not defined by ER status/activity or molecular subtype. Importantly, we developed a predictive therapeutic pipeline that enables identification of new drugs tailored to target ET-insensitive cell populations. These findings provide a clinically relevant strategy for overcoming ET resistance and support the integration of single-cell profiling and organoid modeling into precision oncology workflows.
from sddb import Catalog
cohort = Catalog().collection("geo-gse310362")
cohort.members # the 4 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-gse310362")
coll.ordered_artifacts.to_dataframe() # the 4 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-gse310362 --dest data/Licenses: 4 NCBI GEO policy
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