In Silico Analysis of RET Variants in Medullary Thyroid Cancer: From the Computer to the Bedside. (April 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: 2015-04
• 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

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.