BIOmarkers in Patients With Pancreatic Cancer ("BIOPAC")

The overall survival of patients with pancreatic cancer (PC) is dismal and has only improved slightly during the last decades primarily due to combination chemotherapy. Early detection of PC is difficult and less than 25% of all PC patients are operated. No validated biomarkers to identify PC at an early stage and to predict treatment outcomes in the individual patient exist. The objective of the present study is to find diagnostic, prognostic and predictive biomarkers, which can be used to 1) diagnose PC early in the disease course with high specificity and sensitivity, 2) improve prognostication, or 3) predict and monitor treatment effectiveness and tolerability for the individual patient. BIOPAC is an observational and translational open cohort study with prospective collection of biological materials and clinical data in patients with PC treated in routine care and also patients who were suspicious for malignancy in the pancreas but then operated without evidence of malignancy. Patients contribute with blood samples (i.e. serum, EDTA plasma and buffy coat, and blood in PAXgeneRNA tubes) before operation or start of adjuvant or palliative chemotherapy and during treatment with blood sampling before 2. cycle of chemotherapy and longitudinally every time of CT scan until disease progression. This schedule is repeated if patients are treated with subsequent lines of chemotherapy. The patients are followed until death. Demographics, disease characteristics, comorbidities and lifestyle factors are registered at inclusion; weight and performance status at each treatment cycle; routine blood tests (i.e. haematology, creatinine, liver enzymes, bilirubin, carbohydrate antigen 19-9, C-reactive protein); type of operation; types of chemotherapy and number of cycles given; date of disease recurrence in operated patients; date of disease progression for each line of chemotherapy; and date of death. Biomarker analyses will include a range of molecules with different characteristics such as DNA, Single Nucleotide Polymorphism (SNPs), RNA, microRNA, proteins and metabolites. Data will be analysed using appropriate methods and statistical analyses.