Mapping the Metabolic Landscape of the Healthy Human Pancreas and the Pancreatic Tumor Microenvironment

Abstract

Pancreatic ductal adenocarcinoma (PDA) is a lethal disease with a current 5-year survival rate of 13% (1). PDA accounts for ~90% of pancreatic cancer cases and arises from the exocrine tissue – responsible for digestive functions in the pancreas. The pancreas also has endocrine functions primarily responsible for blood/sugar homeostasis, composed of acinar, islet, and ductal cells (4). Most patients present with locally advanced or metastatic disease at diagnosis, which is refractory to chemotherapy, radiotherapy, and immunotherapy (1,2). Even when surgical resection is possible, relapse is frequent (3). Thus, there is a critical need to identify new treatment strategies, increase the efficacy of standard of care, and overcome therapeutic resistance. Pancreatic cancer cells are encapsulated by a dense, fibrotic, heterogenous stroma consisting of the extracellular matrix, immune cells, and fibroblasts (4). Metabolic reprogramming due to tumorigenesis engenders metabolic co-adaptations in malignant, non-malignant, and immune cells in the tumor microenvironment (TME) (5–18). The advent of single cell RNA-seq has aided in assessing immune infiltration, identifying novel cellular subtypes, and inferring putative cellular interactions in the pancreatic cancer microenvironment (19–26,26–30). Still, access to healthy pancreas tissue for sequencing is difficult as there isn’t clinical indication to biopsy. Further, adjacent normal tissue has been found to display signs of inflammation, thus not an appropriate control to compare to tumor tissue (30). Through a unique partnership with Gift of Life Michigan (tissue and organ procurement program), we were able to obtain healthy pancreata for single cell RNA-Seq – a true experimental “control”. Our lab previously published findings characterizing the immune landscape of pancreatic cancer via the sequencing of human pancreatic tumor samples (29). Taken together, these data sets presented the opportunity to investigate metabolic alterations across cellular compartments engendered by malignancy. To map metabolic alterations engendered by malignancy I developed a robust computational pipeline employing various computational approaches; pseudo bulk analysis, differential gene expression (DGE) analysis, gene set enrichment analysis (GSEA), and transcription factor inference analysis. In addition, I assessed cellular crosstalk activity between cancer cells and immune cells. To validate computational findings, in vitro experiments were performed as well as co-immunofluorescent staining on healthy human pancreata and patient cancer tumor tissue. In summary, I identified downregulation of mitochondrial programs in several immune populations, relative to their normal counterparts in healthy pancreas. While granulocytes, B cells, and CD8+ T cells all downregulated oxidative phosphorylation, the mechanisms by which this occurred was cell-type specific. In fact, the expression pattern of the electron transport chain complexes was sufficient to identify immune cell types without the use of lineage markers. I also observed changes in tumor associated macrophage (TAM) lipid metabolism, with increased expression of enzymes mediating unsaturated fatty acid synthesis and upregulation in cholesterol export. Concurrently, cancer cells exhibit upregulation of lipid/cholesterol receptor import. Thus, I identified a potential crosstalk whereby TAMs provide cholesterol to cancer cells. I suggest that this may be a new mechanism boosting cancer cell growth and therapeutic target in the future. In this body of work, I present the first metabolic atlas of co-adaptations engendered by malignant and non-malignant cells in human pancreatic cancer. Further, I define novel metabolic alterations that may aid our understanding of the role of metabolic rewiring in immune cell dysfunction and subsequent therapeutic resistance in pancreatic cancer.