Genetic and Circulating Biomarker Data Improve Risk Prediction for Pancreatic Cancer in the General Population.

Authors

Kim J, Yuan C, Babic A, Bao Y, Clish CB, Pollak MN, Amundadottir LT, Klein AP, Stolzenberg-Solomon RZ, Pandharipande PV, Brais LK, Welch MW, Ng K, Giovannucci EL, Sesso HD, Manson JE, Stampfer MJ, Fuchs CS, Wolpin BM, Kraft P

Affiliations

• Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
• Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts.
• Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
• Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts.
• Cancer Prevention Research Unit, Department of Oncology, Faculty of Medicine, McGill University, Montreal, Quebec, Canada.
• Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland.
• Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, Maryland.
• Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland.
• Department of Radiology and Institute for Technology Assessment, Massachusetts General Hospital, Boston, Massachusetts.
• Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.

Abstract

BACKGROUND: Pancreatic cancer is the third leading cause of cancer death in the United States, and 80% of patients present with advanced, incurable disease. Risk markers for pancreatic cancer have been characterized, but combined models are not used clinically to identify individuals at high risk for the disease.

METHODS: Within a nested case-control study of 500 pancreatic cancer cases diagnosed after blood collection and 1,091 matched controls enrolled in four U.S. prospective cohorts, we characterized absolute risk models that included clinical factors (e.g., body mass index, history of diabetes), germline genetic polymorphisms, and circulating biomarkers.

RESULTS: Model discrimination showed an area under ROC curve of 0.62 via cross-validation. Our final integrated model identified 3.7% of men and 2.6% of women who had at least 3 times greater than average risk in the ensuing 10 years. Individuals within the top risk percentile had a 4% risk of developing pancreatic cancer by age 80 years and 2% 10-year risk at age 70 years.

CONCLUSIONS: Risk models that include established clinical, genetic, and circulating factors improved disease discrimination over models using clinical factors alone.

IMPACT: Absolute risk models for pancreatic cancer may help identify individuals in the general population appropriate for disease interception.