Salivary Mucinous Adenocarcinoma Is a Histologically Diverse Single Entity with Recurrent AKT1 E17K Mutations: Clinicopathologic and Molecular Characterization with Proposal for a Unified Classification

Mucin-producing salivary adenocarcinomas were historically divided into separate colloid carcinoma, papillary cystadenocarcinoma, and signet ring cell carcinoma diagnoses based on histologic pattern, but have recently been grouped together in the adenocarcinoma not otherwise specified category. It is currently unclear if these tumors represent 1 or more distinct entities and how they are related to well-circumscribed papillary mucinous lesions with recurrent AKT1 E17K mutations that were recently described as salivary intraductal papillary mucinous neoplasm. Here, we sought to evaluate the clinicopathologic and molecular features of salivary mucinous adenocarcinomas to clarify their classification. We identified 17 invasive mucin-producing salivary adenocarcinomas, 10 with a single histologic pattern, and 7 with mixed patterns. While most tumors demonstrated papillary growth (n=15), it was frequently intermixed with colloid (n=6) and signet ring (n=3) architecture with obvious transitions between patterns. All were cytokeratin 7 positive (100%) and cytokeratin 20 negative (0%). Next-generation sequencing performed on a subset demonstrated recurrent AKT1 E17K mutations in 8 cases (100%) and TP53 alterations in 7 cases (88%). Of 12 cases with clinical follow-up (median: 17 mo), 4 developed cervical lymph node metastases, all of which had colloid or signet ring components. Overall, overlapping histologic and immunohistochemical features coupled with recurrent AKT1 E17K mutations across patterns suggests that mucin-producing salivary adenocarcinomas represent a histologically diverse single entity that is closely related to tumors described as salivary intraductal papillary mucinous neoplasm. We propose a unified mucinous adenocarcinoma category subdivided into papillary, colloid, signet ring, and mixed subtypes to facilitate better recognition and classification of these tumors.