Metaplastic breast cancer (MpBC) is a rare and aggressive form of breast cancer. Characteristically heterogeneous, MpBC are defined by the presence of various morphological elements, typically biphasic, with epithelial (e.g. non-special type (NST), squamous) and mesenchymal (e.g. spindle, chondroid, osteoid) components. The established clonality of the different components, favours an evolution model encompassing either a multipotent progenitor, or a linear metaplastic conversion. We performed methylation profiling to understand the nature of the different components, and to investigate their origins. The different morphologies demonstrated specific methylation profiles, with inter-patient clustering of common elements, rather than intra-patient clustering of distinct elements. While the malignant squamous components had a methylation profile consistent with normal squamous cells, chondroid regions were more like malignant chondroid tumours e.g. chondrosarcoma; indeed, this corresponds to the good and bad prognosis of squamous and chondroid MpBC phenotypes respectively. Our spatial transcriptomic approach, using 10X Genomics Visium and trajectory analysis evidenced that spindle cells form a transition between the originating carcinoma of non-special type (NST) and pleomorphic regions, with osteoid differentiation likely to be an end-stage fate of the chondroid growth pattern. This data lends itself to the conversion model of lineage differentiation. Considering the gene expression changes in more detail, we have also identified a series of master transcription factors likely to regulate these processes, and which are significantly associated with metaplastic-like features (basal/claudin-low; high grade; triple negative) in clinical data (METABRIC). Together, our study defines the micro-methylome and a spatial transcriptomic profile in MpBC, and identifies potential drivers associated with tumour heterogeneity that supports the conversion model of metaplasia and warrants further functional analysis.
from sddb import Catalog
cohort = Catalog().collection("geo-gse283412")
cohort.members # the 8 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-gse283412")
coll.ordered_artifacts.to_dataframe() # the 8 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-gse283412 --dest data/Licenses: 8 NCBI GEO policy
@article{Feng_2025,
title = {Integrated profiling of metaplastic breast cancer identifies putative master regulators of intratumoral heterogeneity},
volume = {11},
ISSN = {2374-4677},
url = {http://dx.doi.org/10.1038/s41523-025-00807-x},
DOI = {10.1038/s41523-025-00807-x},
number = {1},
journal = {npj Breast Cancer},
publisher = {Springer Science and Business Media LLC},
author = {Feng, Yufan and Xiong, Albert and Mulay, Onkar and Sokolova, Anna and Lim, Malcolm and Van Haeringen, Benjamin and McGuire, Natasha and de Luca, Xavier and Simpson, Peter T. and Nguyen, Quan and Lakhani, Sunil R. and McCart Reed, Amy E.},
year = {2025},
month = Aug
}