Jinfeng Xiang, Liang Liu, Wenquan Wang, Huaxiang Xu, Chuntao Wu,

Jin Xu, Chen Liu, Jiang Long, Quanxing Ni, Xianjun Yu

Department of Pancreatic and Hepatobiliary Surgery, Pancreatic Cancer Institute, Shanghai Cancer Center, Fudan University

Objective:Pancreatic cancer is currently one of the deadliest solid malignancies and pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer. In the past decade, diagnostics and surgical techniques for PDAC have been evolving steadily; however, clinical outcomes of patients with PDAC have shown little, if any, improvement. Subgroup classification based on accurate prediction of prognosis in patients with pancreatic cancer is important for treatment selection and clinical decision-making. The traditional method to evaluate prognosis relies on the TNM staging system, but it may not reflect the true status of every patient due to individual biological differences. Metabolomics is a field of study that involves the identification and quantification of metabolites present in a biological system. Analysis of metabolic differences between cancerous and noncancerous tissues can provide novel insights into tumor biology that are closely associated with disease prognosis and diagnosis. Therefore, evaluation of metabolic tumor burden may improve the accuracy of the clinical decision-making process, thereby facilitating optimization of the treatment strategies for pancreatic cancer. Method: system review involving recent studies and a series of recent studies we have done: (Our team previously showed that cancer antigen (CA) 125 is superior to CA19-9 in predicting the resectability of pancreatic cancer. Also, we identified a potential serum signature focused on biomarker levels, (carcinoembryonic antigen [CEA] +/ CA125+/CA19-9) ≥1,000 U/mL, which is associated with poor surgical outcome and can be used to select appropriate therapies for patients with pancreatic cancer before treatment. Some ongoing tumor immunology studies also found that a specific pretreatment neutrophil-lymphocyte ratio is related with the overall survival of patients with PDAC. Result: MTB may be of utility in accurate pretreatment evaluation of patients with PDAC and ultimately inform the development of precise individualized treatment options. Conclusion: Recent studies have focused on genomics or proteomics as the tool for prediction of cancer prognosis and for guiding comprehensive treatment in pancreatic cancer. In this review, we make a case for the evaluation of whole-body MTB as a significant prognostic factor in pancreatic and other cancers. The MTB-related parameters we propose for further development includes MTV, total lesion glycolysis (TLG), and blood-based biomarkers such as CA199, CEA, and /or CA125, all of which may be assessed independent of the TNM staging system. We have also summarized empirical data supporting the hypothesis that combined metabolic imaging and biological sampling may be a more accurate and comprehensive way for determining cancer prognosis. Indices reflecting MTB may be of utility in accurate pretreatment evaluation of patients with PDAC and ultimately inform the development of precise individualized treatment options.

Key Words: PDAC  pancreatic ductal adenocarcinoma  MTB