Multi-Omic Biomarkers as Potential Tools for the Characterisation of Pancreatic Cystic Lesions and Cancer: Innovative Patient Data Integration

SIMPLE SUMMARY: Pancreatic cancer (PC) is among the most aggressive types of cancer, having caused over 495,000 deaths worldwide in 2020, with increasing annual incidence. Pancreatic cystic lesions (PCLs) are protrusions found within or on the surface of the pancreas, and in many cases have the potential to become malignant. Current methods of characterising PCLs are imperfect and there is a profound need for improved diagnostic algorithms. This review highlights the importance of biological markers in the context of PCLs and PC, with a focus on ‘omics’-related work. Successful integration of different ‘omics’ data could aid in the identification of a novel integrated biomarker profile for the risk stratification of patients with PCLs and PC. ABSTRACT: Pancreatic cancer (PC) is regarded as one of the most lethal malignant diseases in the world, with GLOBOCAN 2020 estimates indicating that PC was responsible for almost half a million deaths worldwide in 2020. Pancreatic cystic lesions (PCLs) are fluid-filled structures found within or on the surface of the pancreas, which can either be pre-malignant or have no malignant potential. While some PCLs are found in symptomatic patients, nowadays many PCLs are found incidentally in patients undergoing cross-sectional imaging for other reasons—so called ‘incidentalomas’. Current methods of characterising PCLs are imperfect and vary hugely between institutions and countries. As such, there is a profound need for improved diagnostic algorithms. This could facilitate more accurate risk stratification of those PCLs that have malignant potential and reduce unnecessary surveillance. As PC continues to have such a poor prognosis, earlier recognition and risk stratification of PCLs may lead to better treatment protocols. This review will focus on the importance of biomarkers in the context of PCLs and PCand outline how current ‘omics’-related work could contribute to the identification of a novel integrated biomarker profile for the risk stratification of patients with PCLs and PC.