Epithelial-to-Mesenchymal transition (EMT) regulates tumour initiation, progression, metastasis and resistance to anti-cancer therapy. Whereas great progress had recently been made in understanding the role and mechanisms that regulate EMT in cancer, no therapeutic strategy to pharmacologically target EMT had been identified so far. Here, we found that Netrin-1 is upregulated in a primary mouse model of skin squamous cell carcinoma (SCCs) presenting spontaneous EMT. Pharmacological inhibition of Netrin-1 by administrating NP137, an anti-Netrin-1 blocking monoclonal antibody currently used in clinical trials in human cancer, decreased the proportion EMT tumour cells in skin SCCs, as well as decreased the number of metastasis and increased the sensitivity of tumour cells to chemotherapy. Single-cell RNA-seq revealed the presence of different EMT states including epithelial, early and late hybrid EMT as well as fully EMT states in control SCCs. In contrast, administration of NP137 prevents the progression of cancer cells towards a late EMT state and sustains tumour epithelial states. ShRNA knockdown (KD) of Netrin-1 and its receptor Unc5b in EPCAM+ tumour cells inhibited EMT in vitro in the absence of stromal cells and regulated a common gene signature promoting tumour epithelial state and restricting EMT. To assess the relevance of these findings to human cancers, we treated mice transplanted with A549 human cancer cell line that undergoes EMT following TGF-b1 administration with NP137. Netrin-1 inhibition decreased EMT in A549 cells in vivo. Altogether, our results identify a new pharmacological strategy to target EMT in cancer opening novel therapeutic interventions for anti-cancer therapy.
from sddb import Catalog
cohort = Catalog().collection("geo-gse234266")
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-gse234266")
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-gse234266 --dest data/Licenses: 2 NCBI GEO policy
@article{Lengrand_2023,
title = {Pharmacological targeting of netrin-1 inhibits EMT in cancer},
volume = {620},
ISSN = {1476-4687},
url = {http://dx.doi.org/10.1038/s41586-023-06372-2},
DOI = {10.1038/s41586-023-06372-2},
number = {7973},
journal = {Nature},
publisher = {Springer Science and Business Media LLC},
author = {Lengrand, Justine and Pastushenko, Ievgenia and Vanuytven, Sebastiaan and Song, Yura and Venet, David and Sarate, Rahul M. and Bellina, Melanie and Moers, Virginie and Boinet, Alice and Sifrim, Alejandro and Rama, Nicolas and Ducarouge, Benjamin and Van Herck, Jens and Dubois, Christine and Scozzaro, Samuel and Lemaire, Sophie and Gieskes, Sarah and Bonni, Sophie and Collin, Amandine and Braissand, Nicolas and Allard, Justine and Zindy, Egor and Decaestecker, Christine and Sotiriou, Christos and Salmon, Isabelle and Mehlen, Patrick and Voet, Thierry and Bernet, Agnès and Blanpain, Cédric},
year = {2023},
month = Aug,
pages = {402–408}
}