A 63-year-old male who received ciltacabtagene autoleucel (cilta-cel) CAR-T cells and the GPRC5DxCD3 bispecific talquetamab for early relapse of his multiple myeloma (MM) developed a leukemic peripheral T-cell lymphoma (PTCL) with cutaneous and intestinal involvement. Longitudinal single-cell RNA and T-cell receptor sequencing of peripheral blood and bone marrow revealed two hyperexpanded CAR-carrying, exhausted effector-memory T-cell clones with marked immunophenotypic as well as transcriptional alterations and different susceptibilities towards treatment with dexamethasone. Spatial transcriptomes of skin lesions confirmed the aberrant CAR-expressing T cells. Whole genome sequencing revealed three distinct integration sites, within the introns of ZGPAT, KPNA4, and polycomb-associated non-coding RNAs. Pre/post-CAR-T whole-genome analyses implicated clonal outgrowth of a TET2-mutated precursor propelled by additional sub-clone specific LOH and other secondary mechanisms
from sddb import Catalog
cohort = Catalog().collection("geo-gse283145")
cohort.members # the 3 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-gse283145")
coll.ordered_artifacts.to_dataframe() # the 3 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-gse283145 --dest data/Licenses: 3 NCBI GEO policy
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