• fine-needle aspiration (FNA)
• computational model
• thyroid cancer

ABSTRACT

Background. The initial presentation of thyroid carcinoma is through a nodule and the best way nowadays to evaluate it is by fine-needle aspiration (FNA). However many thyroid FNAs are not definitively benign or malignant, yielding an indeterminate or suspicious diagnosis which ranges from 10 to 25% of FNAs. The development of molecular initial diagnostic tests for evaluating a thyroid nodule is needed in order to define optimal surgical approach for patients with uncertain diagnosis pre- and intra-operatively.
A large amount of information has been collected on the molecular tumorigenesis of thyroid cancer. A low expression of KIT gene has been reported during the transformation of normal thyroid epithelium to papillary carcinoma suggesting a possible role of the gene in the differentiation of thyroid tissue rather than in the proliferation. Moreover, several gene expression studies have shown differential gene expression signatures between malignant and benign thyroid tumors.
The aim of the current study was to determine the diagnostic utility of a molecular assay based on the gene expression of a panel of molecular markers (KIT, SYNGR2, C21orf4, Hs.296031, DDI2, CDH1, LSM7, TC1, NATH) plus BRAF mutational status to distinguish benign from malignant thyroid neoplasm.
Methods. The mRNA expression level of 9 genes (KIT, SYNGR2, C21orf4, Hs.296031, DDI2, CDH1, LSM7, TC1, NATH) was analyzed by quantitative Real-Time PCR (qPCR) in 93 FNA cytological samples. To evaluate the diagnostic utility of all the genes analyzed, we assessed the area under the curve (AUC) for each gene individually and in combination. BRAF exon 15 status was determined by capillary sequencing. A gene expression computational model (Neural Network Bayesian Classifier) was built and a multiple-variable analysis was then performed to analyze the correlation between the markers.
Results. While looking at KIT expression, we have found a highly preferential decrease rather than increase in transcript of KIT in malignant thyroid lesions compared to the benign ones. To explore the diagnostic utility of KIT expression in thyroid nodules, its expression values were divided in four arbitrarily defined classes, with class I characterized by the complete silencing of the gene. Class I and IV represented the two most informative groups, with 100% of the samples found malignant or benign respectively. The molecular analysis was proven by ROC (receiver operating characteristic) analysis to be highly specific and sensitive improving the cytological diagnostic accuracy of 15%.
The AUC for each significant marker was further assessed and ranged between 0.625 and 0.900, thus all the significant markers, alone and in combination, can be used to distinguish between malignant and benign FNA samples. The classifier made up of KIT, CDH1, LSM7, C21orf4, DDI2, TC1, Hs.296031 and BRAF had a predictive power of 88.8%. It proved to be useful for risk stratification of the most critical cytological group of the indeterminate lesions for which there is the greatest need for accurate diagnostic markers.
Conclusion. The genetic classification obtained with such a model is highly accurate and may provide a tool to overcome the difficulties in today’s pre-operative diagnosis of thyroid malignancies.