Towards Precision Prevention: Testing a Novel Risk Prediction Algorithm in Pancreatic Cancer
Abstract
Pancreatic cancer is a growing health problem in the United States. It is currently the fourth leading cause of cancer death, but it is expected to transition to the second leading cause of cancer-related death by 2030. The survival rate is less than 8%, and most patients are diagnosed with advanced disease. While pancreatic cancer is slow-growing, and survival can be improved if the cancer is detected early, few advances have been made in improving pancreatic cancer patient outcomes. It is difficult to address pancreatic cancer because although it is deadly (most people diagnosed with the disease die soon after), it is a rare cancer, making the development of screening or prevention programs difficult. More precise identification and targeting of individuals at high risk for pancreatic cancer are likely needed to address the growing burden of pancreatic cancer. Previous studies have used existing knowledge about the potential causes of pancreatic cancer, including genes, lifestyle behaviors, like cigarette smoking, and medical conditions, such as diabetes, to identify high-risk populations. However, these studies have limited clinical utility because they are missing key risk factor information, and they do not consider how or which risk factors work together to cause disease. It is clear that we must take a more comprehensive approach to identifying new risk factor combinations that can lead to improved definitions of high-risk populations in order to ensure more rapid clinical management of this increasingly deadly cancer. Here, our objective is to apply new computer algorithms that can empirically test many different combinations of risk factors to identify which factors (or groups of risk factors) are most predictive of pancreatic cancer and are most associated with poor clinical outcomes, such as early age at diagnosis, tumor stage, and survival time. The rationale for the proposed research is that, by identifying risk factor combinations that are related to both pancreatic cancer development and poor clinical outcomes, we will improve our understanding of how seemingly different risk factors, such as behavior and genetics, work together to cause disease. Further, we will be able to more precisely pinpoint individuals at high risk for poor pancreatic cancer outcomes, where prevention, early detection, and alternative treatment approaches may be targeted. The overarching goal of my research program is to adapt and develop complex computer algorithms that can discover and validate different risk factor combinations that best predict pancreatic cancer and that could be used to target high-risk individuals for pancreatic cancer prevention, screening, and treatment interventions. This Career Development Award will provide me with support to address important biologically and clinically relevant questions in the field of pancreatic cancer during the first years of my tenure-track faculty position. To achieve my goals, I will work closely with my primary mentor, Dr. Stolzenberg-Solomon, a Senior Investigator at the National Cancer Institute, and my secondary mentor, Dr. Jason Moore, who is the Director for the Institute for Biomedical Informatics at the University of Pennsylvania. We have designed a career development plan that will help me develop the proper skills in bioinformatics and pancreatic cancer biologic pathways to launch a successful career in pancreatic cancer research and reach goals of this proposal. By discovering novel combinations of sets of risk factors that work together to cause disease (for example, genetic factors that are current treatment targets and certain dietary factors), improvements in multimodal therapies (i.e., treatments with drugs AND dietary changes), early detection, and even surveillance of high-risk populations could be utilized to address the growing pancreatic cancer disease burden. I predict that it will take 3 years to complete our proposed project and another 1-3 ye
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 07, 2017
- Source ID
- W81XWH1710276
Entities
People
- Shannon M Lynch
Organizations
- United States Army