Spatial Transcriptomic Analysis Reveals Associations between Genes and Cellular Topology in Breast and Prostate Cancers.

Abstract

Simple SummaryTissues consist of various cell types in complex spatial arrangements. These tissues require signaling between the cells and structural proteins to maintain their organization. Spatial arrangements or the organization of cells in tissue can be readily studied via imaging analysis of dissected tissue. In cancer tissues, there is, additionally, signaling between immune cells and between immune and cancer cells. The signaling molecules as well as the structural molecules can be readily measured using molecular profiling technology. Until now, there have not been ways to have both high-resolution imaging and high-resolution molecular profiling for the same locations in tissue. Now that the technology exists to measure both molecular profile and corresponding image, we can study the relationship between molecules in the tissue and the resultant tissue architecture in order to determine functional relationships between the two in cancer. We find that image features correspond well with extracellular matrix and other mechanisms that are important in tumor structure and aggressiveness.Background: Cancer is the leading cause of death worldwide with breast and prostate cancer the most common among women and men, respectively. Gene expression and image features are independently prognostic of patient survival; but until the advent of spatial transcriptomics (ST), it was not possible to determine how gene expression of cells was tied to their spatial relationships (i.e., topology). Methods: We identify topology-associated genes (TAGs) that correlate with 700 image topological features (ITFs) in breast and prostate cancer ST samples. Genes and image topological features are independently clustered and correlated with each other. Themes among genes correlated with ITFs are investigated by functional enrichment analysis. Results: Overall, topology-associated genes (TAG) corresponding to extracellular matrix (ECM) and Collagen Type I Trimer gene ontology terms are common to both prostate and breast cancer. In breast cancer specifically, we identify the ZAG-PIP Complex as a TAG. In prostate cancer, we identify distinct TAGs that are enriched for GI dysmotility and the IgA immunoglobulin complex. We identified TAGs in every ST slide regardless of cancer type. Conclusions: These TAGs are enriched for ontology terms, illustrating the biological relevance to our image topology features and their potential utility in diagnostic and prognostic models.