The multidimensional world of pancreatic cancer: Omic strategies to improve outcome

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer type, which is projected to become the second leading cause of cancer-related deaths in the coming decades. Recently, cancer research has investigated large-scale molecular data from different technologies, so called “big data”. It encompasses different methods referred to as omics. This includes genomics, transcriptomics, proteomics and phosphoproteomics. This thesis the exploits multiple dimensions of omics: from microRNAs to the functionally relevant (phospho)proteome. In Chapter 1, we explored the current knowledge on biology and treatment of PDAC and summarized the three main contributing factors of this devasting disease; (I.) the aggressive growth of PDAC and its complex microenvironment, (II.) the difficulty of adequate diagnosis with current biomarkers, (III.) the need for novel treatments to halt the disease. To decipher the aggressive tumor biology of PDAC, we reviewed current literature of omic studies performed of PDAC in Chapter 2. One of PDAC’s most defining feature is the presence of an abundant desmoplastic microenvironment. This microenvironment, also named stroma, can influence the tumor and its behavior. In Chapter 3, a study of recently identified subtypes of PDAC and stroma is reviewed in depth. In Chapter 4, both compartments were studied by proteomics to identify their individual protein landscapes. In this study, over 5.000 unique proteins were identified. Meta-analysis was performed to validate prognostic markers in gene expression data sets, after which stromal COL11A1 and tumor cell-derived CALB2 were validated by immunohistochemistry in an independent cohort for their association with a poorer prognosis. Expression of proteins is associated with a pathway, often regulated by a common denominator. Thus, analysis of co-expression can identify important biology in a disease. Hence, we studied proteins that are co-expressed in similar patterns across PDAC samples in Chapter 5. In Chapter 6, we reviewed the diagnostic potential of microRNAs in PDAC. These short RNAs have a role in epigenetic regulation. In Chapter 7, we analyzed blood from a cohort of patients suffering from distal cholangiocarcinoma to identify differential microRNAs compared to healthy controls. Chapter 8 described the analysis of RNA from tumor-educated platelets (TEPs) from PDAC patients. Recently, platelets were found to be influenced by tumor cells, so called TEPs. Machine-learning algorithms were implemented to optimize for the most discriminative RNA panel. In Chapter 9, we evaluated secreted proteins of tumor tissue of PDAC and distal cholangiocarcinoma and identified candidate biomarker thrombospondin-2 (THBS2), which was validated in a large second cohort. The final part of this thesis focused on optimalization of treatment of patients with PDAC. Using preclinical models, we explored novel targets and mechanisms of chemoresistance. In Chapter 10, we evaluated differential protein expression of a parental PDAC cell line and its gemcitabine resistant counterpart. In Chapter 11, we explored this gemcitabine-resistant model for its features in vitro and in vivo. To identify novel drug targets, we evaluated kinase expression in the tumor areas of PDAC in Chapter 4. Eph recepter A2 (EPHA2) was found to be abundantly expressed in PDAC cells. Inhibition resulted in enhanced cell death and reduced migration of PDAC cells. Additionally, Chapter 12 explored phosphoproteomics and explored focal adhesion kinase as target in PDAC. In conclusion, the studies described in this thesis exploited the potential of omic strategies to understand the aggressive biology of PDAC, to uncover innovative biomarkers, and identify novel therapeutic options. These findings can contribute to the future diagnosis and treatment of PDAC patients, ultimately improving clinical outcome for these patients.