GSE168410
Deconvolution of breast tumors for optimal chemotherapy selection
Extensive work in pre-clinical models has shown that microenvironmental cells influence many aspects of cancer cell behavior including metastatic potential and their sensitivity to therapeutics. In the human setting, this behavior is mainly correlated with the presence of immune cells, whilst the relevance of other cell types have been largely ignored. Here, in addition to T cells, B cells, macrophages and mast cells, we identified the relevance of non-immune cell types for breast cancer survival and therapy benefit, including fibroblast, myoepithelial cells, muscle cells, endothelial cells, and 7 distinct epithelial cell types. Using single-cell sequencing data, we generated reference profiles for all these cell types. We used these reference profiles in deconvolution algorithms to optimally detangle the cellular composition of over 3500 primary breast tumors of patients that were enrolled in the SCAN-B and MATADOR clinical trials, and for which bulk mRNA sequencing data was available. This large data set enables us to identify and subsequently validate the cellular composition of microenvironments that distinguish differential survival and treatment benefit for different treatment regiments in primary breast cancer patients. Overall design: Single-cell mRNA sequencing of 12 breast tumors for generation of reference profiles to use in bulk mRNA sequencing deconvolution