Objectives: Current diagnostic tools for pancreatic cysts fail to reliably differentiate mucinous from nonmucinous cysts. Reliable biomarkers are needed. MicroRNAs (miRNA) may offer insights into pancreatic cysts. Our aims were to (1) identify miRNAs that distinguish benign from both premalignant cysts and malignant pancreatic lesions using formalin-fixed, paraffin-embedded (FFPE) pathology specimens; (2) identify miRNAs that distinguish mucinous cystic neoplasm (MCN) from branch duct-intraductal papillary mucinous neoplasm (BD-IPMN).
Methods: A total of 69 FFPE pancreatic specimens were identified: (1) benign (20 serous cystadenoma (SCA)), (2) premalignant (10 MCN, 10 BD-IPMN, 10 main duct IPMN (MD-IPMN)), and (3) malignant (19 pancreatic ductal adenocarcinoma (PDAC)). Total nucleic acid extraction was performed followed by miRNA expression profiling of 378 miRNAs interrogated using TaqMan MicroRNA Arrays Pool A and verification of candidate miRNAs. Bioinformatics was used to generate classifiers.
Results: MiRNA profiling of 69 FFPE specimens yielded 35 differentially expressed miRNA candidates. Four different 4-miRNA panels differentiated among the lesions: one panel separated SCA from MCN, BD-IPMN, MD-IPMN, and PDAC with sensitivity 85% (62, 97), specificity 100% (93, 100), a second panel distinguished MCN from SCA, BD-IPMN, MD-IPMN, and PDAC with sensitivity and specificity 100% (100, 100), a third panel differentiated PDAC from IPMN with sensitivity 95% (76, 100) and specificity 85% (72, 96), and the final panel diagnosed MCN from BD-IPMN with sensitivity and specificity approaching 100%.
Conclusions: MiRNA profiling of surgical pathology specimens differentiates serous cystadenoma from both premalignant pancreatic cystic neoplasms and PDAC and MCN from BD-IPMN.