TERT Promoter Mutation and Extent of Thyroidectomy in Patients with 1-4 cm Intrathyroidal Papillary Carcinoma.
Aya EbinaYuki TogashiSatoko BabaYukiko SatoSeiji SakataMasashi IshikawaHiroki MitaniKengo TakeuchiIwao SugitaniPublished in: Cancers (2020)
There are concerns regarding overtreatment in papillary thyroid carcinoma (PTC). BRAF V600E and TERT promoter mutations play important roles in the development of PTC. However, initial surgical approaches for PTC based on genetic characteristics remain unclear. The present study aimed to identify genetic mutations as predictors of prognosis and to establish proper indications for lobectomy (LT) in patients with 1-4 cm intrathyroidal PTC. Prospectively accumulated data from 685 consecutive patients with PTC who underwent primary thyroid surgery at the Cancer Institute Hospital, Tokyo, Japan, between 2001 and 2012 were retrospectively reviewed. Of the 685 patients examined, 538 (78.5%) had BRAF V600E mutation and 133 (19.4%) had TERT promoter mutations. Patients with TERT promoter mutations displayed significantly worse outcomes than those without mutations (10-year cause-specific survival (CSS): 73.7% vs. 98.1%, p < 0.001; 10-year disease-free survival (DFS): 53.7% vs. 93.3%, p < 0.001). As for extent of thyroidectomy among TERT mutation-negative patients with 1-4 cm intrathyroidal PTC, patients who underwent LT showed no significant differences in 10-year CSS and 10-year DFS compared to patients who had total thyroidectomy (TT) under propensity score-matching. Avoiding TT for those patients indicates a possible pathway to prevent overtreatment and reduce postoperative complications.
Keyphrases
- end stage renal disease
- newly diagnosed
- dna methylation
- ejection fraction
- gene expression
- transcription factor
- peritoneal dialysis
- papillary thyroid
- healthcare
- prognostic factors
- squamous cell carcinoma
- genome wide
- metabolic syndrome
- minimally invasive
- type diabetes
- patient reported outcomes
- patient reported
- insulin resistance
- lymph node metastasis
- atrial fibrillation
- machine learning
- acute care
- data analysis