AI-predicted spatial transcriptomics unlocks breast cancer biomarkers from pathology
Summary
Spatial transcriptomics (ST) assays are transforming our understanding of tumor heterogeneity, but their high cost limits their application in large-scale biomarker discovery. Here, we present "Path2Space," a deep-learning model that predicts spatial gene expression directly from histopathology slides. Trained on extensive breast cancer ST data, Path2Space robustly predicts the spatial expression of thousands of genes, outperforming 21 established methods. Charting the tumor microenvironme
Content
# AI-predicted spatial transcriptomics unlocks breast cancer biomarkers from pathology
*Published: 2026 May 8*
Spatial transcriptomics (ST) assays are transforming our understanding of tumor
heterogeneity, but their high cost limits their application in large-scale
biomarker discovery. Here, we present "Path2Space," a deep-learning model that
predicts spatial gene expression directly from histopathology slides. Trained on
extensive breast cancer ST data, Path2Space robustly predicts the spatial
expression of thousands of genes, outperforming 21 established methods. Charting
the tumor microenvironment (TME) of 976 breast cancer TCGA (The Cancer Genome
Atlas) tumors, it accurately infers cell-type abundances and identifies three
spatially defined breast cancer subgroups with distinct survival outcomes.
Notably, the derived low-cost spatial TME landscapes enable more accurate
predictions of patient response to chemotherapy and trastuzumab compared with
costly conventional bulk-sequencing-based biomarkers. Path2Space thus offers a
scalable, fast, and cost-effective alternative to molecular assays. It opens
avenues for large cohort treatment biomarker discovery and translationally
relevant insights into tumor biology, with potential applicability across many
cancer indications.
Published by Elsevier Inc.
DOI: 10.1016/j.cell.2026.04.023