Abstract

Anaplastic thyroid carcinoma (ATC) is rare but highly aggressive. We investigated the association of selected driver mutations, including BRAF, RAS, PIK3CA, TERT promoter, TP53, POLE, and mismatch repair deficiency (MMR-D) with the clinicopathological features of ATC to identify prognostic and predictive biomarkers. Thirty-nine retrospective cases from pathology archives were enrolled for clinicopathology analysis and immunohistochemistry, and 27 cases had sufficient specimens for further molecular testing using targeted next-generation sequencing and mass spectrometry. BRAFV600E and RAS mutations were identified in 25.9% and 40.7% of ATC, respectively. BRAFV600E mutation was significantly associated with coexisting papillary thyroid carcinoma (p = 0.009) and RAS mutations with female gender (p = 0.012). In univariant analysis, the non-BRAF/RAS tumors were significantly associated with the presence of a sarcomatoid pattern (p = 0.045). PIK3CA, TERT promoter, and TP53 mutations were identified in 14.8%, 81.5%, and 70.4% of cases, respectively. No MMR-D or POLE mutations were detected. In survival analyses, RAS and PIK3CA mutations were significantly associated with inferior outcomes (p = 0.03 and p = 0.006, respectively). In conclusion, driver mutations in ATC are associated with distinct clinicopathological features. RAS and PIK3CA mutations were negative predictors for patient survival. Emerging therapeutic agents targeting BRAF, RAS, and PI3 kinase may benefit a substantial proportion of ATC patients.

Highlights

  • Thyroid carcinoma is the most common malignancy of the endocrine system [1]

  • We identified distinct clinicopathological features in Anaplastic thyroid carcinoma (ATC) stratified by BRAFV600E

  • This study illustrates clinicopathological features in ATC stratified by BRAFV600E and RAS

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Summary

Introduction

Thyroid carcinoma is the most common malignancy of the endocrine system [1]. Anaplastic thyroid carcinoma (ATC) is an aggressive tumor comprising 1%~2% of thyroid carcinoma [2]. Identification of actionable treatment targets, as well as biological predictors for clinical behavior in ATCs, is of value to improve clinical outcomes

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