from sddb import Catalog
cohort = Catalog().collection("geo-gse301973")
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/")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-gse301973")
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()]# sddb client: download every member .zarr into data/
sddb-cli download --collection geo-gse301973 --dest data/Licenses: 2 NCBI GEO policy
<h4>Background</h4>Despite an initial favorable response of EGFR-mutant non-small cell lung cancer (NSCLC) to osimertinib, an EGFR tyrosine kinase inhibitor (TKI), resistance to this drug inevitably develops. Whereas genetic mechanisms for such acquired resistance have been identified, the molecular mediators of resistance induction have remained unclear.<h4>Methods</h4>To identify factors that mediate induction of osimertinib resistance, we studied clinical samples from individuals with EGFR-mutant NSCLC as well as cell lines including PC-9 and H1975. Methods adopted included transcriptomics analysis and immunohistochemistry of pretreatment NSCLC specimens, spatial transcriptomics analysis, a cell viability assay, immunofluorescence and quantitative PCR analysis, RNA sequencing, immunoblot analysis, comprehensive proteomics analysis by mass spectrometry, co-immunoprecipitation and proximity ligation assays, and a mouse xenograft tumor model.<h4>Results</h4>Transcriptomics analysis of pretreatment clinical specimens identified IFITM3 (interferon-induced transmembrane protein 3) as a gene specifically upregulated in patients with a poor response to osimertinib treatment. Immunohistochemistry confirmed that patients with IFITM3-positive tumors experienced a shorter progression-free survival on osimertinib treatment. Spatial transcriptomics and other analyses further revealed that IFITM3 expression in tumor cells was increased in response to cytokines derived from the tumor microenvironment (TME) during osimertinib treatment. IFITM3 was found to promote the development of osimertinib resistance in NSCLC cell lines through interaction with MET and activation of the AKT signaling pathway. Furthermore, combined treatment with a MET inhibitor suppressed the development of osimertinib resistance in a mouse xenograft tumor model.<h4>Conclusions</h4>Our findings reveal that upregulation of IFITM3 driven by TME cytokines represents a previously unrecognized mechanism of osimertinib resistance, and they suggest that targeting of the IFITM3-MET axis may improve EGFR-TKI treatment outcome for EGFR-mutant NSCLC.
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