In Silico Reconstruction of Primary and Metastatic Tumor Architecture Using Geographic Information System-Augmented...

The tumor microenvironment comprises different cell populations that interact, contributing to tumor heterogeneity and therapy response. Spatial transcriptomics offers valuable insights into the transcriptional complexity and heterogeneity of the tumor microenvironment. In this study, we established a geographic information system (GIS)-augmented in silico reconstruction of tumor architecture (GIS-ROTA), a biologically informed analytic framework that integrates pathway or cell type-based enrichment analysis with spatial autocorrelation measurement to uncover functional spatial domains. The approach considers biological functions prior to identifying any spatial domains, providing direct interpretability and minimizing the subjectivity of interpreting clusters observed from conventional analytic methods. The application of GIS-ROTA to a Visium spatial transcriptomics dataset of primary and metastatic estrogen receptor-positive breast tumor samples revealed extensive colocalization of estrogen response with metabolic pathway gene sets and mutual exclusivity with metastasis-related and specific immune-related pathway gene sets. Overall, the GIS-ROTA framework integrates biological knowledge first, yielding spatial patterns with functional relevance and enabling the identification of novel targets for the development of therapeutic strategies.

GIS-ROTA, a spatial transcriptomics analytical framework that maps tissues with biologically curated pathways and signatures followed by spatial localization analysis, yields biologically relevant spatial domains, enabling cancer target and biomarker discovery.