We performed the first integrative single-cell and spatial transcriptomic analysis on HPV-negative oral squamous cell carcinoma (OSCC) to comprehensively characterize tumor core (TC) and leading edge (LE) transcriptional architectures. We show that the TC and LE are characterized by unique transcriptional profiles, cellular compositions, and ligand-receptor interactions. We demonstrate that LE regions are conserved across multiple cancers while TC states are more tissue specific, highlighting the existence of common mechanisms underlying tumor progression and invasion. Additionally, we found our LE gene signature is associated with worse clinical outcomes while the TC gene signature is associated with improved prognosis across multiple cancer types. Finally, using an in silico modeling approach, we describe spatially-regulated patterns of cell development in OSCC that are predictably associated with drug response. Our work provides pan-cancer insights into TC and LE biology and a platform for data exploration (http://www.pboselab.ca/spatial_OSCC/) that can be foundational for developing novel targeted therapies.
from sddb import Catalog
cohort = Catalog().collection("geo-gse208253")
cohort.members # the 12 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-gse208253")
coll.ordered_artifacts.to_dataframe() # the 12 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-gse208253 --dest data/Licenses: 12 NCBI GEO policy
@article{Arora_2023,
title = {Spatial transcriptomics reveals distinct and conserved tumor core and edge architectures that predict survival and targeted therapy response},
volume = {14},
ISSN = {2041-1723},
url = {http://dx.doi.org/10.1038/s41467-023-40271-4},
DOI = {10.1038/s41467-023-40271-4},
number = {1},
journal = {Nature Communications},
publisher = {Springer Science and Business Media LLC},
author = {Arora, Rohit and Cao, Christian and Kumar, Mehul and Sinha, Sarthak and Chanda, Ayan and McNeil, Reid and Samuel, Divya and Arora, Rahul K. and Matthews, T. Wayne and Chandarana, Shamir and Hart, Robert and Dort, Joseph C. and Biernaskie, Jeff and Neri, Paola and Hyrcza, Martin D. and Bose, Pinaki},
year = {2023},
month = Aug
}