Tumor microenvironment (TME) consists of different cell populations, whose interactions contribute to tumor heterogeneity and therapy response. Spatial transcriptomics (ST) offers valuable insights into spatial complexity and heterogeneity of TME. We established a Python package, Geographic Information System (GIS)-augmented In-Silico Reconstruction of Tumor Architecture (GIS-ROTA), based on application of GIS methods to ST data to examine/explore spatial heterogeneity of co-regulated gene sets, such as pathways and cell types within the TME. In our Visium dataset of primary and metastatic estrogen receptor positive breast tumor samples, GIS-ROTA revealed extensive co-localization of estrogen response with metabolic pathway gene sets and mutual exclusivity with metastasis-related and specific immune-related pathway gene sets. Our findings demonstrate the robustness of GIS-ROTA in quantitating tumor heterogeneity and identifying spatially significant regions while minimizing the subjectivity involved in interpreting the clusters from conventional statistical methods. Thus, GIS-ROTA enables the development of therapeutic strategies that target multiple cell populations.
from sddb import Catalog
cohort = Catalog().collection("geo-gse298286")
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-gse298286")
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-gse298286 --dest data/Licenses: 12 NCBI GEO policy
@article{Yoo_2026,
title = {<i>In Silico</i>
Reconstruction of Primary and Metastatic Tumor Architecture Using Geographic Information System–Augmented Spatial Transcriptomics},
volume = {86},
ISSN = {1538-7445},
url = {http://dx.doi.org/10.1158/0008-5472.can-25-3161},
DOI = {10.1158/0008-5472.can-25-3161},
number = {10},
journal = {Cancer Research},
publisher = {American Association for Cancer Research (AACR)},
author = {Yoo, Jin Young and Akter, Sabrina and Zuo, Qianying and Rilloraza, Matthew and Kazemi, Amir and Wu, Wenjun and Goel, Mahima and Lam, Audrey and Dutta, Debapriya and Barnick, Betsy and Pekmezci, Melike and Grosse Perdekamp, Maria and Soliman, Aiman and Madak-Erdogan, Zeynep},
year = {2026},
month = Feb,
pages = {2555–2568}
}