A Panel of four MicroRNAs accurately differentiates Malignant from Benign Indeterminate Thyroid Lesions on Fine Needle Aspiration
Menée sur 72 échantillons thyroïdiens prélevés par aspiration à l'aiguille fine, cette étude suggère qu'un modèle basé sur l'expression de quatre micro-ARNs permet de distinguer une lésion bénigne d'une lésion maligne de la thyroïde
Purpose: Indeterminate thyroid lesions on Fine Needle Aspiration (FNA) harbor malignancy in about 25% of cases. Hemi- or total thyroidectomy has therefore been routinely advocated for definitive diagnosis. In this study we analyzed miRNA expression in indeterminate FNA samples and determined its prognostic effects on final pathological diagnosis. Experimental Design: A predictive model was derived using 29 ex-vivo indeterminate thyroid lesions on FNA to differentiate benign from malignant tumors at a tertiary referral center and validated on an independent set of 72 prospectively collected in-vivo FNA samples. Expression levels of miR-222, miR-328, miR-197, miR-21, mir-181a and mir-146b were determined using RT-PCR. A statistical model was developed using the Support Vector Machine (SVM) approach. Results: A SVM model with 4 miRNAs (miR-222, miR-328, miR-197 and miR-21) was initially estimated to have 86% predictive accuracy using cross-validation. When applied to the 72 independent in-vivo validation samples, performance was actually better than predicted with a sensitivity of 100% and specificity of 86%, for a predictive accuracy of 90% in differentiating benign from malignant indeterminate lesions. When Hurthle Cell lesions were excluded, overall accuracy improved to 97% with 100% sensitivity and 95% specificity. Conclusions: This study shows that that the expression of miR-222, miR-328, miR-197 and miR-21 combined in a predictive model is accurate at differentiating malignant from benign indeterminate thyroid lesions on FNA. These findings suggest that FNA miRNA analysis could be a useful adjunct in the management algorithm of patients with thyroid nodules.
Clinical Cancer Research , résumé, 2012