A major obstacle for the effective treatment of pancreatic ductal adenocarcinoma (PDAC) is its molecular heterogeneity, reflected by the diverse clinical outcomes and responses to therapies that occur. The tumors of patients with PDAC must therefore be closely examined and classified before treatment initiation in order to predict the natural evolution of the disease and the response to therapy. To stratify patients, it is absolutely necessary to identify biological markers that are highly specific and reproducible, and easily measurable by inexpensive sensitive techniques. Several promising strategies to find biomarkers are already available or under development, such as the use of liquid biopsies to detect circulating tumor cells, circulating free DNA, methylated DNA, circulating RNA, and exosomes and extracellular vesicles, as well as immunological markers and molecular markers. Such biomarkers are capable of classifying patients with PDAC and predicting their therapeutic sensitivity. Interestingly, developing chemograms using primary cell lines or organoids and analyzing the resulting high-throughput data via artificial intelligence would be highly beneficial to patients. How can exploiting these biomarkers benefit patients with resectable, borderline resectable, locally advanced, and metastatic PDAC? In fact, the utility of these biomarkers depends on the patient’s clinical situation. At the early stages of the disease, the clinician’s priority lies in rapid diagnosis, so that the patient receives surgery without delay; at advanced disease stages, where therapeutic possibilities are severely limited, the priority is to determine the PDAC tumor subtype so as to estimate the clinical outcome and select a suitable effective treatment.Copyright © 2020. Published by Elsevier Inc.
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