Improved Risk Models Identify People at Higher than Normal Risk of Pancreatic Cancer

Researchers demonstrated that risk models that include established clinical, genetic, and circulating factors are better able to identify people at significantly higher than normal risk of pancreatic cancer over those using clinical factors alone.

A study, published in Cancer Epidemiology, Biomarkers & Prevention, found that risk models that include established clinical, genetic, and circulating factors were able to better identify people at significantly higher than normal risk of pancreatic cancer over those using clinical factors alone.1

However, further refinement and validation of these models in independent samples is still necessary in order to make the models clinically actionable and impact survival of patients with pancreatic cancer according to the researchers.

“Pancreatic cancer is a particularly deadly cancer, with about 80 percent of patients diagnosed with advanced, incurable disease,” lead author Peter Kraft, PhD, professor of epidemiology at the Harvard T.H. Chan School of Public Health in Boston, said in a press release.2 “Catching it at an earlier stage makes it more likely that surgery will be an option, increasing the chances of survival.” 

Using data from 4 large prospective cohort studies, the researchers analyzed data from 500 patients diagnosed with primary pancreatic adenocarcinoma between 1984 and 2010, as well as 1,091 matched controls. Data was collected on lifestyle and clinical characteristics from patient questionnaires, blood samples, and genomic DNA from peripheral blood leukocytes of the participants. Kraft explained that the study enrolled only US non-Hispanic white participants, as genomic risk variants have been confirmed in the white population but not in other groups. 

Moreover, a genetic risk score was calculated based on data from 2 large genome-wide association studies. Three relative risk models were developed for women and men separately; one included only clinical factors, 1 added the weighted genetic risk score to the clinical factors, and the last added biomarkers proinsulin, adiponectin, IL-6, and total branched-chain amino acids. According to Kraft, each new level of data improved the model and allowed for more accurate identification of pancreatic cancer risk. 

Overall, the models identified subsets of participants who were at three-fold or higher increased risk of developing pancreatic cancer compared to the general population. The model that only included clinical characteristics identified 0.2% of men and 1.5% of women who were at three-fold or higher increased risk during the full follow-up period, and the model that included clinical, genetic, and biomarker data additionally identified 1.8% of men and 0.7% of women. During the full follow-up period, this number increased to 2.0% of men and 2.3% of women at three-fold risk of pancreatic cancer.

Ultimately, when restricting the follow-up time to 0 to 10 years, the final integrated model identified 3.7% of men and 2.6% of women who had at least 3 times greater than average risk over the ensuing 10 years. Further, individuals within the top risk percentile were found to have a 4% risk of developing pancreatic cancer by age 80 and a 2% 10-year risk at age 70 years.

“Like most cancers, pancreatic cancer is multifactorial,” Kraft said. “The more we are able to combine information from multiple domains, the better we will become at identifying those who could benefit from screening.” 

Notably, the family history of pancreatic cancer was not collected from most study participants, therefore the relative risk for family history could not be estimated from the data provided. Additionally, because smoking status was a matching factor in the study design, the researches could not estimate the risk of current smoking in this population. 

“The final integrated model has improved risk discrimination over those that include clinical factors alone and successfully identify a small segment of the general population at elevated risk of pancreatic cancer,” the authors wrote. “Given the late stage at presentation for most patients with pancreatic cancer, earlier detection approaches are worthy of significant investment as a critical means to reduce mortality from pancreatic cancer, soon to be the second leading cause of cancer death in the United States.”


1. Kim J, Yuan C, Babic A, et al. Genetic and Circulating Biomarker Data Improve Risk Prediction for Pancreatic Cancer in the General Population. Cancer Research. doi:10.1158/1055-9965.EPI-19-1389.

2. Risk Prediction Model That Combines Clinical and Genetic Factors with Circulating Biomarkers Could Identify Those at Higher Risk of Pancreatic Cancer [news release]. Philadelphia. Published April 22, 2020. Accessed April 22, 2020. 

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