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Otolaryngol Head Neck Surg. 2015 Apr;152(4):650-4. doi: 10.1177/0194599815569709. Epub 2015 Mar 2.

In silico analysis of RET variants in medullary thyroid cancer: from the computer to the bedside.

Author information

1
Weill Cornell Medical College, New York, New York, USA.
2
Department of Otolaryngology-Head and Neck Surgery, Weill Cornell Medical College/New York Presbyterian, New York, New York, USA.
3
Department of Otolaryngology-Head and Neck Surgery, Weill Cornell Medical College/New York Presbyterian, New York, New York, USA dik2002@med.cornell.edu.

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.

KEYWORDS:

RET gene; in silico analysis; medullary thyroid cancer; multiple endocrine neoplasia; thyroidectomy

PMID:
25733075
DOI:
10.1177/0194599815569709
[Indexed for MEDLINE]

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