Collections / Single cell profiling of primary and paired metastatic lymph node tumors in breast cancer patients (10x Visium)

Single cell profiling of primary and paired metastatic lymph node tumors in breast cancer patients (10x Visium)

The microenvironment of lymph node metastasized tumors (LNMT) determines tumor progression and response to therapy, but a systematic study of LNMT is lacking. Here, we generate single-cell maps of primary tumors (PTs) and paired LNMTs in 8 breast cancer patients. We demonstrate that the activation, cytotoxicity, and proliferation of T cells are suppressed in LNMT compared with PT. CD4+CXCL13+ T cells in LNMT are more likely to differentiate into an exhausted state. Interestingly, LAMP3+ dendritic cells in LNMT display lower T cell priming and activating ability than in PT. Additionally, we identify a subtype of PLA2G2A+ cancer-associated fibroblasts enriched in HER2+ breast cancer patients that promotes immune infiltration. We also show that the antigen-presentation pathway is downregulated in malignant cells of the metastatic lymph node. Altogether, we characterize the microenvironment of LNMT and PT, which may shed light on the individualized therapeutic strategies for breast cancer patients with lymph node metastasis.

Datasets4
Size~403 MB
AssayVisium
Samplehuman · lymph node
KindReplicates
Flag a data issue
PMID:36357424 ↗
Python · sddb client
from sddb import Catalog

cohort = Catalog().collection("geo-gse190811")
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/")
Python · lamindb

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-gse190811")
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()]
Shell
# sddb client: download every member .zarr into data/
sddb-cli download --collection geo-gse190811 --dest data/

Licenses: 4 NCBI GEO policy

Open in Vitessce
Visium
Human lymph node
GSM5732357
human lymph node breast cancer 86.0 MB 4,992 spots silver H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissuelymph node
Conditionbreast cancer
Size86.0 MB
Spots4,992
silver H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Human lymph node
GSM5732360
human lymph node breast cancer 107 MB 4,992 spots silver H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissuelymph node
Conditionbreast cancer
Size107 MB
Spots4,992
silver H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Human lymph node
GSM5732358
human lymph node breast cancer 97.4 MB 4,992 spots silver H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissuelymph node
Conditionbreast cancer
Size97.4 MB
Spots4,992
silver H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Human lymph node
GSM5732359
human lymph node breast cancer 113 MB 4,988 spots silver H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissuelymph node
Conditionbreast cancer
Size113 MB
Spots4,988
silver H&E NCBI GEO policy Open in Vitessce

Cite this collection

No linked publication found — please cite the data source.