A High Performing Biomarker Signature for Detecting Early-Stage Pancreatic Ductal Adenocarcinoma in High-Risk Individuals

<b>Background/Objectives:</b> Early detection of pancreatic cancer can improve patient survival, and blood-based biomarkers to aid in this are a significant need. The goal of this study was to develop and evaluate the performance of a 4- to 6-plex biomarker signature for detection of early-stage pancreatic ductal adenocarcinoma (PDAC) that performs well in high-risk controls. <b>Methods</b>: Enzyme-linked immunosorbent assays were used to measure 10 previously identified serum protein biomarker candidates in Stage I and II PDAC cases (<i>n</i> = 128), high-risk controls (<i>n</i> = 465), and normal-risk controls (<i>n</i> = 30). Various combinations of biomarker candidates (models) were trained using machine learning and tested for robustness in differentiating cases from controls on the full cohort and in clinically relevant sub-types including those with diabetes, those ≥65 years of age, and low producers of carbohydrate antigen 19-9 (CA 19-9). <b>Results</b>: At 98% specificity, the top performing model, which was comprised of tissue inhibitor of metalloproteinase 1 (TIMP1), intracellular adhesion molecule 1 (ICAM1), thrombospondin 1 (THBS1), cathepsin D (CTSD), and CA 19-9, achieved 85% sensitivity in the full cohort and sensitivities of 91% in diabetics, 90% in ≥65 years of age, and 60% in low CA 19-9 producers. This model demonstrated significantly higher sensitivity in detecting PDAC in the full cohort and all sub-populations compared to CA 19-9 alone (<i>p</i> < 0.001). <b>Conclusions</b>: Our findings demonstrate the feasibility of a blood-based assay for detecting early-stage PDAC in high-risk individuals and key sub-populations, representing an important step towards improving diagnostic success for early-stage disease.