Background: Epithelial-stromal crosstalk plays a critical role in invasive breast cancer pathogenesis; however, little is known on a systems level about how epithelial-stromal interactions evolve during carcinogenesis.
Results: We develop a framework for building genome-wide epithelial-stromal co-expression networks composed of pairwise co-expression relationships between mRNA levels of genes expressed in the epithelium and stroma across a population of patients. We apply this method to laser capture micro-dissection expression profiling datasets in the setting of breast carcinogenesis. Our analysis shows that epithelial-stromal co-expression networks undergo extensive rewiring during carcinogenesis, with the emergence of distinct network hubs in normal breast, and estrogen receptor-positive and estrogen receptor-negative invasive breast cancer, and the emergence of distinct patterns of functional network enrichment. In contrast to normal breast, the strongest epithelial-stromal co-expression relationships in invasive breast cancer mostly represent self-loops, in which the same gene is co-expressed in epithelial and stromal regions. We validate this observation using an independent laser capture micro-dissection dataset and confirm that self-loop interactions are significantly increased in cancer by performing computational image analysis of epithelial and stromal protein expression using images from the Human Protein Atlas.
Conclusions: Epithelial-stromal co-expression network analysis represents a new approach for systems-level analyses of spatially localized transcriptomic data. The analysis provides new biological insights into the rewiring of epithelial-stromal co-expression networks and the emergence of epithelial-stromal co-expression self-loops in breast cancer. The approach may facilitate the development of new diagnostics and therapeutics targeting epithelial-stromal interactions in cancer.