In silico analysis of RET variants in medullary thyroid cancer: from the computer to the bedside.
Academic Article
Overview
abstract
OBJECTIVE: The American Thyroid Association (ATA) medullary thyroid cancer (MTC) guidelines group RET variants, in the setting of familial medullary thyroid cancer and multiple endocrine neoplasia type 2, into 4 classes of severity based on epidemiological data. The aim of this study was to determine if genotype correlates with phenotype in RET missense mutations. STUDY DESIGN: In silico mutational tolerance prediction. SETTING: Academic research hospital. SUBJECTS AND METHODS: We analyzed all RET variants currently listed in the ATA guidelines for the management of MTC using 2 computer-based (in silico) mutation tolerance prediction approaches: PolyPhen-2 HumVar and PolyPhen-2 HumDiv. Our analysis also included 27 different RET single-nucleotide polymorphisms resulting in missense variants. RESULTS: There was a statistically significant difference in the overall HumDiv score between ATA groups A and B (P = .025) and a statistically significant different HumVar score between benign polymorphisms and ATA group A (P = .023). Overall, RET variants associated with a less aggressive clinical phenotype generally had a lower Hum Div/Var score. CONCLUSIONS: Polyphen-2 Hum Div/Var may provide additional clinical data to help distinguish benign from MEN2/familial medullary thyroid carcinoma-causing RET variants as well as less aggressive phenotypes (ATA A) from more aggressive ones (ATA B-C). In silico genetic analyses, with proper validation, may predict the phenotypic severity of RET variants, providing clinicians with a tool to aid clinical decision making in cases in which the RET variant is currently unknown or little epidemiological data are available.
