Pancreatic cancer early detection: expanding higher-risk group with clinical and metabolomics parameters

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is the fourth and fifth leading cause of cancer death for each gender in developed countries. With lack of effective treatment and screening scheme available for the general population, the mortality rate is expected to increase over the next several decades in contrast to the other major malignancies such as lung, breast, prostate and colorectal cancers. Endoscopic ultrasound, with its highest level of detection capacity of smaller pancreatic lesions, is the commonly employed and preferred clinical imaging-based PDAC detection method. Various molecular biomarkers have been investigated for characterization of the disease, but none are shown to be useful or validated for clinical utilization for early detection. As seen from studies of a small subset of familial or genetically high-risk PDAC groups, the higher yield and utility of imaging-based screening methods are demonstrated for these groups. Multiple recent studies on the unique cancer metabolism including PDAC, demonstrate the potential for utility of the metabolites as the discriminant markers for this disease. In order to generate an early PDAC detection screening strategy available for a wider population, we propose to expand the population of higher risk PDAC group with combination clinical and metabolomics parameters.

Keywords: Biomarkers; Early detection; Endoscopic ultrasound; Metabolomics; Pancreatic cancer.

Figure 1

Example of metabolomics based analysis, allowing segregation of pancreatic ductal adenocarcinoma from benign cases. Heat map illustration of discriminant capability of a metabolite set derived from gas chromatography and liquid chromatography/mass spectrometry plasma metabolomics dataset comparing pancreatic ductal adenocarcinoma patients (Red or group 1; n = 110) and benign pancreas (includes benign cysts, chronic pancreatitis, and normal pancreas) (Green or group 2; n = 90). Metabolites are plotted on x-axis, and the cases on the y-axis. Blue color indicates data points with a value smaller than the median of the respective metabolite and the red indicates higher values. This candidate set of metabolites enabled the segregation of the two groups.