Multiplatform profiling of pancreatic neuroendocrine tumors: Correlative analyses of clinicopathologic factors and identification of co-occurring pathogenic alterations

Background: Multi-omic profiling of pancreatic neuroendocrine tumors (PanNETs) was performed to correlate genomic, proteomic, and molecular pathway alterations with clinicopathologic factors and identify novel co-occurring pathogenic alterations of potential clinical relevance to PanNET management. Methods: PanNETs referred to Perthera, Inc. having undergone molecular profiling for precision matched therapeutic purposes were screened. Correlative analyses were performed using Fisher’s exact test across individual pathogenic alterations or altered molecular pathways and clinicopathologic variables. Associations were visualized by hierarchical clustering. Prognostic associations with overall survival (OS) were identified using Cox regression for pathogenic alterations and pathway-level alterations. Hazard ratios (HR) and odds ratios (OR) were reported with 95% confidence intervals (CI). Results: From 12/2014–1/2019, 46 patients with predominantly locally advanced and metastatic PanNETs were included. MEN1 alterations by next-generation sequencing (NGS) were less associated with having high-grade PanNETs and metastatic disease at diagnosis (p ≤ 0.05). Genomic alterations associated with increased replicative stress (primarily driven by RB1 and TP53) correlated with higher grade (OR 6.87 [95% CI: 1.57-35.18], p = 0.0043) and worse OS (HR 13.62 [95% CI: 1.51-122.5], p = 0.0198). Other significant associations included: ERCC1 protein expression with DAXX or MEN1 alterations (NGS), PTEN (NGS) with ARID1A or TP53 alterations (NGS), and history of diabetes coincided with cell cycle pathway alterations but was mutually exclusive with replicative stress pathway alterations. Conclusions: We identified several molecular signatures of potential clinical significance for therapeutic targeting and prognostication in PanNETs warranting prospective validation. Our findings are hypothesis generating and can inform larger molecular profiling efforts in PanNETs.