Diagnostic value and lymph node metastasis prediction of a custom‑made panel (thyroline) in thyroid cancer.

Abstract

Differentiation of benign and malignant thyroid nodules is crucial for clinical management. Here, we explored the efficacy of next-generation sequencing (NGS) in predicting the classification of benign and malignant thyroid nodules and lymph node metastasis status, and simultaneously compared the results with ultrasound (US). Thyroline was designed to detect 15 target gene mutations and 2 fusions in 98 formalin-fixed, paraffin-embedded (FFPE) tissues, including those from 82 thyroid cancer (TC) patients and 16 patients with benign nodules. BRAF mutations were found in 57.69% of the papillary thyroid cancer (PTC) cases, while RET mutations were detected among all the medullary thyroid cancer (MTC) cases. Multiple mutations were positive but none showed dominance in anaplastic thyroid cancer (ATC) and follicular thyroid cancer (FTC). The sensitivity and specificity of NGS prediction in differentiation of benign and malignant thyroid nodules were 79.27 and 93.75%, respectively, and the positive predictive value (PPV) and negative predictive value (NPV) were 98.48 and 46.88%, respectively. The sensitivity and specificity of US were 76.83 and 6.25%, respectively, and the PPV and NPV were 80.77 and 5.00%, respectively. The area under curve (AUC) of NGS and US were 0.865 and 0.415, respectively. A total of 27 patients had ≥1 metastases to lymph nodes, 19 of which carried mutations, including BRAF, RET, NRAS, PIK3CA, TP53, CTNNB1 and PTEN. However, there was no correlation between the variant allele frequency of specific gene mutations and the number of metastatic lymph nodes. In conclusion, the prediction value of NGS was higher than the US-based Thyroid Imaging Reporting and Data System (TI-RADS). NGS is valuable for the accurate differentiation of benign and malignant thyroid nodules, and pathological subtypes in FFPE samples. The findings of the present study may pave the way for the application of NGS in analyzing fine-needle aspiration (FNA) biopsy samples.