ThyNod Panel efficiently identifies genetic characteristics of thyroid nodules

Abstract

Abstract Purpose We aimed to establish a next-generation sequencing panel for the molecular diagnosis of thyroid nodules. Methods The panel, named ThyNod Panel, was designed to detect SNV, indel, fusion, CNV in 48 thyroid malignancy associated genes as rule in markers, 3 benign associated genes as rule out markers, RNA expression levels in 16 thyroid differentiation/function genes and 23 cell identity marker genes. We retrospectively tested 68 frozen/4 FFPE tissues and prospectively tested 107 thyroid FNA samples. Results Seventeen nodules were C cell origin, one was parathyroid and the others were follicular cell. For follicular cell derived thyroid nodules, 123/161 (76%) were found mutations: malignancy associated mutations were BRAF V600E (n = 80), RAS mutations (n = 12), RET/PTC fusions (n = 7), NTRK3 fusions (n = 6) and, BRAF fusions (n = 4), PIK3CA mutations (n = 3); benign associated mutations were identified in 3 nodules, all with SPOP mutations. The accuracy of the ThyNod Panel in diagnosing malignant and benign follicular cell derived thyroid nodules was 91.30% (95% CI, 85.58–96.17), with sensitivity and specificity as 98.68% and 56.25%; for Bethesda category III/IV nodules, nine (64%, 9/14) were positive with malignancy associated mutations and molecular findings in 67% (4/6) nodules were consistent with histopathologic diagnosis. Eight nodules carried two or more driver alterations, two with ATA high-risk thyroid cancers. Conclusion ThyNod Panel can efficiently identify genetic characteristics in thyroid nodules and be applied in the molecular diagnosis of thyroid nodules.