Digital gene expression profiling of a series of cytologically indeterminate thyroid nodules

Fine-needle aspiration cytology (FNAC) has been widely accepted as the most crucial step in the preoperative assessment of thyroid nodules. Testing for the expression of specific genes should improve the accuracy of FNAC diagnosis, especially when it is performed in samples with indeterminate cytology.

In total, 69 consecutive FNACs that had both cytologic and histologic diagnoses were collected, and expression levels of 34 genes were determined in RNA extracted from FNAC cells by using a custom digital mRNA counting assay. A supervised k-nearest neighbor (K-nn) learning approach was used to build a 2-class prediction model based on a subset of 27 benign and 26 malignant FNAC samples. Then, the K-nn models were used to classify the 16 indeterminate FNAC samples.

Malignant and benign thyroid nodules had different gene expression profiles. The K-nn approach was able to correctly classify 10 FNAC samples as benign, whereas only 1 sample was grouped in the malignant class. Two malignant FNAC samples were incorrectly classified as benign, and 3 of 16 samples were unclassified.

Although the current data will require further confirmation in a larger number of cases, the preliminary results indicate that testing for specific gene expression appears to be useful for distinguishing between benign and malignant lesions. The results from this study indicate that, in indeterminate FNAC samples, testing for cancer-specific gene expression signatures, together with mutational analyses, could improve diagnostic accuracy for patients with thyroid nodules.