In Silico Analysis of RET Variants in Medullary Thyroid Cancer: From the Computer to the Bedside. (2nd March 2015)

• Record Type: Journal Article
• Authors: Heineman, Thomas E.; Joshi, Rohan; Cohen, Marc A.; Kuhel, William I.; Kutler, David I.
• Is Part Of: Otolaryngology--head and neck surgery. Volume 152:Number 4(2015:Apr.)
• Page Start: 650
• Page End: 654
• Publication Date: 2015-03-02
• Subjects: medullary thyroid cancer -- multiple endocrine neoplasia -- thyroidectomy -- in silico analysis -- RET gene; Head -- Surgery -- Periodicals; Neck -- Surgery -- Periodicals; Otolaryngology -- Periodicals; 617.51
• DOI: 10.1177/0194599815569709
• Languages: English
• ISSNs: 0194-5998

Abstract

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.