An affordable immunohistochemical approach to estimate the prevalence of BRAFV600E in large cohort studies-establishing the baseline rate of BRAF mutation in an institutional series of papillary thyroid carcinoma from Thailand.

Abstract

Papillary thyroid carcinoma (PTC) accounts for the majority of diagnoses of thyroid carcinoma. BRAFV600E mutation is the most common genetic alteration in PTC, which has diagnostic and prognostic significance. The rate of BRAFV600E mutation in PTC from Thailand has not been reported. Our purpose was to estimate the prevalence of BRAF mutation in a large institutional series using an affordable approach, which combined mutation-specific immunohistochemistry (IHC) with VE1 antibody and tissue microarray (TMA). A total of 476 PTC cases plotted on TMA were employed for determining the mutation status in this study. The cancer tissue of initial 100 cases (pilot study) were analyzed for BRAFV600E mutation by using both direct sequencing and VE1 immunostaining. For the subsequent PTC cases, VE1 IHC was used as an alternative to direct sequencing for the detection of mutation. Univariate and multivariate analyses were done to determine the association of clinicopathological variables with BRAFV600E mutation. In the pilot study, VE1 IHC showed excellent analytical performance (κ=0.884) for detecting BRAFV600E mutation in PTC TMA as compared to direct sequencing. The prevalence of BRAFV600E in the whole cohort was 60.9% by using VE1 IHC. The mutation was commonly seen in tall cell (92.9%) and classic (70.2%) variants of PTC. Multivariate analysis (P<0.05) showed association of BRAFV600E with histological type of tumor, extrathyroidal extension, and absence of Hashimoto's thyroiditis. In conclusion, BRAFV600E mutation was detected in 60.9% of Thai PTC and it was associated with several aggressive clinicopathological variables of thyroid cancer. VE1 IHC proved as a reliable method able to replace direct sequencing for detection of the mutation. A combination of mutation-specific IHC and TMA allows conducting large cohort studies more labor-saving and cost-efficiently.