In Silico Reconstruction of Primary and Metastatic Tumor Architecture using Geographic Information System-Augmented Spatial Transcriptomics.

Abstract

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