Immune checkpoint inhibitors (ICIs) have transformed Merkel cell carcinoma (MCC) outcomes, but most patients with MCC develop resistance. We identified T cell receptor (TCR)MCC1, a highly avid, HLA-A*02:01–restricted TCR targeting the Merkel cell polyomavirus (MCPyV) oncoprotein large-T antigen15–23. Seven patients with ICI-refractory metastatic MCPyV+ MCC received TCRMCC1-transduced cells (TTCR-MCC1 cells) after lymphodepleting chemotherapy or HLA-enhancing interventions [radiation or interferon gamma-1b (Actimmune)], with concurrent ICIs (NCT03747484). TTCR-MCC1 cells trafficked to tumor sites and expressed a gene expression profile compatible with T cell activation, with tumor regression observed in two patients. However, therapeutic activity was limited by HLA class I silencing, a common mechanism of immune escape in MCC. In one patient, delayed tumor regression coincided with endogenous effector immune activation and restoration of MCC HLA expression, implying that robust local responses could reverse HLA silencing. To overcome this barrier, we engineered CD4 and CD8 TTCR-MCC1 cells to coexpress CD8αβ and a CD200R-CD28 switch receptor, enabling CD4 T cell engagement and T cell costimulation. These modifications enhanced tumor infiltration, increased HLA expression, and improved control of HLAlow MCC in vivo in mice. These findings support the feasibility of TCR-engineered cell therapy for MCPyV+ MCC and provide a blueprint for overcoming immune evasion via targeted localized enhancement of antigen presentation.
from sddb import Catalog
cohort = Catalog().collection("geo-gse326660")
cohort.members # the 6 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-gse326660")
coll.ordered_artifacts.to_dataframe() # the 6 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-gse326660 --dest data/Licenses: 6 NCBI GEO policy
No linked publication found — please cite the data source.