In silico reconstruction of primary and metastatic tumor architecture using GIS-augmented spatial transcriptomics
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. Overall design: Twelve primary and metastatic bone tumor samples from estrogen receptor positive metastatic breast cancer patients were analyzed using spatial transcriptomics. Two additional primary and metastatic bone tumor samples were analyzed using spatial transcriptomics and proteomics.