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Abstract
Pancreatic Cancer (PC) is currently the fourth leading cause of cancer death in Western countries and is projected to rise to the second leading cause by 2030. The poor outcomes seen in PC have not improved in four decades. A factor contributing to this poor outcome is underutilisation of available treatment options in clinical practice. The first part of this thesis aims to explore the use of chemotherapy in a large national cohort of patients with PC in Australia and particularly focuses on use of therapy in elderly patients. A key finding is that adjuvant chemotherapy is used less frequently in older patients with PC.
The remainder of the work outlined in this thesis details biomarkers of prognosis in PC and provides in an indepth study of genomic predictive biomarkers of a key therapeutic in advanced PC, erlotinib.
The first biomarker, KRAS, is found at an extremely high rate in PC however, by utilising meta-analytic techniques, combining multiple data-sets provides strong evidence that the KRAS mutation is prognostic of poor survival in PC and identifies a distinct phenotype in PC. Secondly, CDX2 and MUC1 are identified as candidate prognostic biomarkers that predict survival of patients with resected PC in 2 independent cohorts. There are an abundance of putative biomarkers for PC outcome in the literature, however none are used in clinical practice. A major limitation has been lack of reproducibility of key discoveries due to the inherent heterogeneity of PC. The final part of this thesis attempts to validate previously identified predictive biomarkers of erlotinib sensitivity. Erlotinib is an anti-EGFR therapy that has been found to improve survival when used in combination with gemcitabine in advanced PC. However, its benefit is modest and a predictive biomarker is needed to identify the group of patients that are most likely to benefit. An extensively characterised cohort and its associated pre-clinical models are utilised to highlight that literature-based protein expression, gene mutation or gene expression do not predict for benefit of erlotinib in PC. However, a phosphoproteomic signature is validated as predictive for response to erlotinib.