An Integrated Approach to Reclassify <i>MEN1</i> Variants of Uncertain Significance Using Clinical and Computational Evidence

CONTEXT: Multiple endocrine neoplasia type 1 (MEN1) is a highly penetrant hereditary tumour syndrome caused by pathogenic variants in the MEN1 gene. Many detected variants are classified as variants of uncertain significance (VUS), limiting clinical decision-making, surveillance, and cascade testing. Structured re-evaluation incorporating clinical, structural, and computational evidence has not been widely applied in MEN1. OBJECTIVE: To determine whether integrated reanalysis of MEN1 VUS supports reclassification using ACMG/AMP criteria. DESIGN: Retrospective cohort study. SETTING: Three Australian tertiary referral centres. PATIENTS: Ten individuals with germline MEN1 VUS. INTERVENTIONS: Variants were re-curated using ACMG/AMP guidelines incorporating phenotype, family history, segregation, population data, and computational tools (REVEL, AlphaMissense, SpliceAI). Protein structural modelling using AlphaFold and PyMOL assessed variant localisation within functional domains. Computational score distributions were compared with ClinVar likely pathogenic/pathogenic (LP/P) and likely benign/benign (LB/B) variants using the Kruskal-Wallis test. MAIN OUTCOME MEASURES: Variant reclassification and supporting clinical, computational, and structural evidence. RESULTS: Ten VUS were analysed (six missense, one in-frame deletion, one frameshift, two splice-affecting). On re-curation, 7/10 (70%) were reclassified as likely pathogenic based on phenotype specificity, segregation, in silico prediction, and predicted loss-of-function effects. Cohort variants demonstrated computational scores comparable to ClinVar LP/P variants (median REVEL 0.913; AlphaMissense 0.999) and significantly higher than LB/B variants (p<0.001). Structural modelling showed clustering within constrained JunD- and MLL-binding domains of menin. Three variants remained VUS due to insufficient evidence. CONCLUSIONS: Integrated re-evaluation combining phenotype, segregation, structural modelling, and computational prediction reduces diagnostic uncertainty and supports broader implementation of structured VUS reanalysis in MEN1.