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Biomarker Data Improves Risk Prediction for Pancreatic Cancer

By LabMedica International staff writers
Posted on 07 May 2020
Pancreatic cancer risk prediction models based on clinical factors may be refined and improved by bringing in other types of data, including germline genetic risk variant and blood-based biomarker data.

Pancreatic cancer is the third leading cause of cancer death in the USA, 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.

Image: Histopathological micrograph of pancreatic adenocarcinoma arising in the pancreas head region (Photo courtesy of KGH).
Image: Histopathological micrograph of pancreatic adenocarcinoma arising in the pancreas head region (Photo courtesy of KGH).

A team of scientists led by those at the Harvard T.H. Chan School of Public Health (Boston, MA, USA) used a nested case-control approach, and brought together genetic, clinical, and other data for 500 individuals with primary pancreatic adenocarcinoma and 1,091 matched control individuals, enrolled through four prospective studies, using these data to develop several pancreatic cancer risk models based on clinical data alone, genetic and clinical factors, or genetic, clinical, and biomarker data.

Genomic DNA was extracted from peripheral blood leucocytes of cohort participants. Details on genotyping, variant imputation, and quality control procedures were previously reported. Circulating levels of proinsulin (pmol/L), adiponectin (mg/mL), interleukin-6 (IL-6; pg/mL), and branched-chain amino acids (BCAAs; mmol/L) were measured and represent four major categories of circulating markers related to pancreatic cancer risk (insulin resistance, adipokines, inflammation, and peripheral tissue catabolism, respectively). The team dichotomized circulating adiponectin with a cut off of 4.4 mg/mL).

When the team applied those models to almost 1,000 more cases or controls followed for up to 10 years, it found that the integrated model provided the most accurate pancreatic risk prediction, surpassing risk estimates done using clinical data without genetic or blood biomarker insights. Model discrimination showed an area under ROC curve of 0.62 via cross-validation. The final integrated model identified 3.7% of men and 2.6% of women who had at least three 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.

The authors wrote that because all of their subjects were enrolled in prospective cohorts, all risk factor data and circulating markers were measured before the cases' diagnosis of pancreatic cancer, faithfully recapitulates the situation faced by primary care physicians, where decisions related to disease screening are made in the prediagnostic setting using data collected in the several years prior to cancer diagnosis.

Peter Kraft, PhD, a Professor of Epidemiology and senior author of the study, said, “Like most cancers, pancreatic cancer is multifactorial. The more we are able to combine information from multiple domains, the better we will become at identifying those who could benefit from screening. These factors have been investigated individually and in this study, we wanted to examine the combined effect of clinical factors, common genetic predisposition variants, and circulating biomarkers.” The study was published on April 22, 2020 in the journal Cancer Epidemiology, Biomarkers, and Prevention.

Related Links:
Harvard T.H. Chan School of Public Health


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