Desmoplasia is a hallmark of pancreatic ductal adenocarcinoma (PDAC), still one of the most deadly cancer types. Typically, epithelial tumor cells represent just a minority of the malignant tissue embedded in a massive amount of stroma. This profound desmoplasia highlights the importance of understanding the interactions between tumor and stromal cells to better understand the disease and to explore novel treatment options. At the same time, it represents a technical challenge when aiming to extract the cell-type-specific signals using molecular analyses derived from bulk tumor samples. To overcome this problem, we have FACS-purified the major cell-type populations present within primary human PDAC tumors (cancer associated fibroblasts (CAFs), immune, endothelial and epithelial cancer cells) and supplemented these with the same cell-type populations isolated from tumor-free adjacent normal pancreas. From these pure populations we have derived their whole genome transcriptome and methylome (epithelial cells only) and generated a comprehensive analysis of the data. Thus, we have revised known pathways involved in PDAC to understand which cell-type population expresses which gene of the pathway; this helps us anticipating which population will be affected by targeted therapies. We have used cell-cell interaction network analyses to identify novelcross-talks taking place between different cell-type populations. The comparison of PDAC and normal pancreas datasets has allowed us to identify potentially targetable genes and pathways deregulated in tumorsat the cell-type population level. As example, our analysis revealed the activation of interferon signalling-related signatures in a subset of patients. This generates a complex interactome between epithelial cancer cells and their environment to which various cell types contribute and which is associated with poor outcome. I will show that the activation of this interferon program associates to a specific DNA methylome profile of the epithelial tumor cells and discuss how this suggests the existence of different cells of origin for distinct PDAC subsets and, importantly, how this information can potentially benefit patient treatment.<\/p>\n
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