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A multi-analyte serum biomarker panel for early detection of pancreatic adenocarcinoma

Purpose: We determined whether a large, multi-analyte panel of circulating biomarkers can improve detection of early-stage pancreatic ductal adenocarcinoma (PDAC). Experimental Design: We defined a biologically relevant subspace of blood analytes based on previous identification in premalignant lesions or early-stage PDAC and evaluated each in pilot studies. The 31 analytes that met minimum diagnostic accuracy were measured in serum of 837 subjects (461 healthy, 194 benign pancreatic disease, 182 early stage PDAC). We used machine learning to develop classification algorithms using the relationship between subjects based on their changes across the predictors. Model performance was subsequently evaluated in an independent validation data set from 186 additional subjects. Results: A classification model was trained on 669 subjects (358 healthy, 159 benign, 152 early-stage PDAC). Model evaluation on a hold-out test set of 168 subjects (103 healthy, 35 benign, 30 early-stage PDAC) yielded an area under the receiver operating characteristic (ROC) curve (AUC) of 0.920 for classification of PDAC from non-PDAC (benign and healthy controls) and an AUC of 0.944 for PDAC vs. healthy controls. The algorithm was then validated in 146 subsequent cases presenting with pancreatic disease (73 benign pancreatic disease, 73 early and late stage PDAC) as well as 40 healthy control subjects. The validation set yielded an AUC of 0.919 for classification of PDAC from non-PDAC and an AUC of 0.925 for PDAC vs. healthy controls. Conclusions: Individually weak serum biomarkers can be combined into a strong classification algorithm to develop a blood test to identify patients who may benefit from further testing.