||5R01CA109767-06 Interpret this number
||University Of California, San Francisco
||Molecular Epidemiology of Pancreatic Cancer
DESCRIPTION (provided by applicant): This application is 100% related to PC. Recent studies show that factors associated with abnormal glucose metabolism are important in PC development. We hypothesize that insulin resistance (IR) is a growth-promoting factor in PC etiology. Also, genetic variation in insulin, glucose, and lipid metabolism and in candidate genes for IR are related to PC risk. A clinic-based case-control study will include 600 patients and 600 controls obtained from UCSF clinics. Detailed history of diet, body mass index, obesity, physical activity, diabetes and other factors including medication use and smoking will be collected during interviews. Blood will be collected from eligible participants. UCSF's Molecular Epidemiology Core will process all bloods and extract DMA for use in molecular/ genetic studies. SNPs will be examined in candidate genes related to IR and metabolism, and lipid metabolism. Genetic testing will be done using UCSF's Genome Core facilities and personnel.
Main effects for exposures and SNPs will be evaluated as will gene-environment and gene-gene interactions when sample sizes permit. We will work with NCI on the PC consortium development and combine new data with data from our earlier population-based PC study (N=2,233) to conduct analyses. We will collaborate with Seattle and Mayo clinic PC studies to pool data and increase power to study rare exposures and factors within small groups. Major strengths are: 1. Large number of PC cases will allow analyses by sex, ethnicity and race; 2. Innovative hypotheses to advance PC research (e.g. SNPs); 3. Expertise of lab faculty and facilities; 4. No proxy interviews; 5. Clinic-based cases will diminish case loss due to poor survival; 6. Studies of diabetes, IR, diet, obesity, physical activity and SNPs are innovative and new to large studies of PC; 7. Blood samples will be banked for future genetic studies and data will be pooled for greater statistical power.