Refining dynamic risk stratification and prognostic groups for differentiated thyroid cancer with TERT promoter mutations

Abstract

Context: Currently, no recurrence or mortality risk systems consider molecular testing when predicting thyroid cancer outcomes. Objective: We developed an integrative prognostic system that incorporates telomerase reverse transcription (TERT) promoter mutations into the recently proposed risk reclassification system after initial therapy [dynamic risk stratification (DRS)] to better categorize and predict outcomes. Design: A total of 357 differentiated thyroid cancer (DTC) patients without initial distant metastasis were enrolled. Among patients with mutated TERT and wild-type, recurrence-free survival (RFS) was compared according to DRS grouping. Cox regression was used to calculate adjusted hazard ratios (AHRs) to derive AHR groups. Performance of the AHR grouping system with respect to prediction of structural recurrence and cancer-specific survival (CSS) was assessed against the current DRS system and the tumor/node/metastasis (TNM) classification. Results: Among 357 patients, there were 90 recurrences and 15 cancer-related deaths during a median of 14 years of follow-up. Patients in higher AHR groups were at higher risk of recurrence (10-year RFS for AHR 1, 2, 3, and 4: 94.9%, 82.7%, 50.2%, and 23.1%; P < 0.001) and cancer-related death (10-year CSS: 100.0%. 98.7%, 94.2%, and 76.9%; P < 0.001). The proportions of variance explained (PVEs) for the ability of AHR and DRS grouping to predict recurrence were 22.4% and 18.5%. PVEs of AHR and TNM system to predict cancer-related deaths were 11.5% and 7.4%. Conclusions: The AHR grouping system, a simple two-dimensional prognostic system, is as effective as DRS at predicting structural recurrence and provides clinical implication for long-term CSS in patients with nonmetastatic DTC.