DNA Methylation-Based Method to Differentiate Malignant from Benign Thyroid Lesions

• Autor(a) principal: Barros-Filho, Mateus Camargo
• Data de Publicação: 2019
• Outros Autores: Reis, Mariana Bisarro dos [UNESP], Beltrami, Caroline Moraes, Homem de Mello, Julia Bette, Marchi, Fabio Albuquerque, Kuasne, Hellen, Drigo, Sandra Aparecida [UNESP], Andrade, Victor Piana de, Saieg, Mauro Ajaj, Lopes Pinto, Clovis Antonio, Kowalski, Luiz Paulo, Rogatto, Silvia Regina
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1089/thy.2018.0458; http://hdl.handle.net/11449/184635
• topic: papillary thyroid carcinoma; follicular thyroid carcinoma; DNA methylation; diagnostic markers; bisulfite-pyrosequencing

Background: The differential diagnosis of thyroid nodules using fine-needle aspiration biopsy (FNAB) is challenging due to the inherent limitation of the cytology tests. The use of molecular markers has potential to complement the FNAB-based diagnosis and avoid unnecessary surgeries. In this study, we aimed to identify DNA methylation biomarkers and to develop a diagnostic tool useful for thyroid lesions. Methods: Genome-wide DNA methylation profiles (Illumina 450K) of papillary thyroid carcinoma (PTC = 60) and follicular thyroid carcinoma (FTC = 10) were compared with non-neoplastic thyroid tissue samples (NT = 50) and benign thyroid lesions (BTL = 17). The results were confirmed in publicly available databases from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) using the same DNA methylation platform. Two classifiers were trained to discriminate FTC and PTC from BTL. To increase the applicability of the method, six differentially methylated CpGs were selected and evaluated in 161 thyroid tumors and 69 BTL postsurgical specimens and 55 prospectively collected FNAB using bisulfite-pyrosequencing. Results: DNA methylation analysis revealed 2130 and 19 differentially methylated CpGs in PTC and FTC, respectively. The CpGs confirmed by GEO and TCGA databases showing high areas under the receiver operating characteristic curve in all sample sets were used to train our diagnostic classifier. The model based on six CpGs was able to differentiate benign from malignant thyroid lesions with 94.3% sensitivity and 82.4% specificity. A similar performance was found applying the algorithm to TCGA and GEO external data sets (91.3-97.4% sensitivity and 87.5% specificity). We successfully evaluated the classifiers using a bisulfite-pyrosequencing technique, achieving 90.7% sensitivity and 75.4% specificity in surgical specimens (five of six CpGs). The study comprising FNAB cytology materials corroborated the applicability and performance of the methodology, demonstrating 86.7% sensitivity and 89.5% specificity in confirmed malignant tumors, and 100% sensitivity and 89% specificity in cases with indeterminate cytology. Conclusions: A novel diagnostic tool with potential application in preoperative screening of thyroid nodules is reported here. The proposed protocol has the potential to avoid unnecessary thyroidectomies.