Collections / Targeting the lipid metabolism proteins FASN and GPAM in alveolar type II cells decreases lung metastasis

Targeting the lipid metabolism proteins FASN and GPAM in alveolar type II cells decreases lung metastasis

Cancer cells that seed in the lung require lipids, which are mostly produced by alveolar type II (AT2) cells. However, whether overt metastases depend on AT2 cell-derived lipids and whether AT2 cells can be targeted to reduce metastasis growth remains unknown. We discovered that metastases stimulate the proliferation of AT2 cells in their vicinity and reprogram them into lipid feeder cells in mice and patients with breast cancer using spatial mass spectrometry imaging combined with immunohistochemistry and spatial transcriptomics. Mechanistically, we find that the secretome of the metastases contains IL-6 and formate, which increase the activity of the transcription factor sterol regulatory element-binding transcription factor 1 (SREBP-1). This in turn enhances the expression of key de novo lipid synthesis genes including fatty acid synthase (FASN) and glycerol-3-phosphate acyltransferase 1 (GPAM). Deleting Fasn selectively in AT2 cells or targeting FASN and GPAM systemically is sufficient to impair breast cancer-derived metastasis growth in mice. In summary, we discovered that overt metastases reprogram AT2 cells and that targeting the lipid metabolism of AT2 cells impairs metastasis growth.

Datasets2
Size~50.1 MB
AssayVisium
Samplemouse · lung
KindComparison
Flag a data issue
PMID:41778850 ↗
Python · sddb client
from sddb import Catalog

cohort = Catalog().collection("geo-gse318532")
cohort.members        # the 2 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-gse318532")
coll.ordered_artifacts.to_dataframe()   # the 2 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-gse318532 --dest data/

Licenses: 2 NCBI GEO policy

Open in Vitessce
Visium
Mouse lung
GSM9497186
mouse lung breast carcinoma 29.5 MB 1,888 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissuelung
Conditionbreast carcinoma
Size29.5 MB
Spots1,888
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Mouse lung
GSM9497187
mouse lung breast carcinoma 20.5 MB 1,110 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissuelung
Conditionbreast carcinoma
Size20.5 MB
Spots1,110
bronze H&E NCBI GEO policy Open in Vitessce

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No linked publication found — please cite the data source.