Context. Detection of thyroid cancer among benign nodules on fine-needle aspiration biopsies (FNAB), which presently relies on cytological examination, is expected to be improved by new diagnostic tests set up from genomic data.

Objective. The aim of the study was to use a set of genes discriminating benign from malignant tumors, on the basis of their expression levels, to built tumor classifiers and evaluate their capacity to predict malignancy on FNAB.

Design. We analyzed the level of expression of 200 potentially informative genes in 56 thyroid tissue samples (benign or malignant tumors and paired normal tissue) using nylon macroarrays. Gene expression data were subjected to a weighted voting algorithm to generate tumor classifiers. The performances of the classifiers were evaluated on a series of 26 "sham" FNAB i.e. FNAB carried out on thyroid nodules after surgical resection.

Results. A series of 19 genes with a similar expression in follicular adenomas (FA) and normal tissue (NT) and discriminating FA+NT from i) follicular thyroid carcinomas (FTC), ii) papillary thyroid carcinomas (PTC) or iii) both FTC and PTC, were used to generate four classifiers, the FTC, PTC, common (FTC+PTC) and global classifiers. In 23 out of the 26 "sham" FNAB, the four classifiers yielded a diagnosis in agreement with the diagnosis of the pathologist used as reference; in the three other cases, the correct diagnosis was given by three of four classifiers.

Conclusions. We developed a procedure of molecular diagnosis of benign versus malignant tumors applicable to the material collected by FNAB. The molecular test complied with a pre-clinical validation stage; it must be now evaluated on ultrasound-guided FNAB in a large-scale prospective study.