Development of a risk classification system combining TN-categories and circulating EBV DNA for non-metastatic NPC in 10,149 endemic cases.

BACKGROUND

The objective of this study was to construct a risk classification system integrating cell-free Epstein-Barr virus (cfEBV) DNA with T- and N- categories for better prognostication in nasopharyngeal carcinoma (NPC).

METHODS

Clinical records of 10,149 biopsy-proven, non-metastatic NPC were identified from two cancer centers; this comprised a training ( = 9,259) and two validation cohorts ( = 890; including one randomized controlled phase 3 trial cohort). Adjusted hazard ratio (AHR) method using a two-tiered stratification by cfEBV DNA and TN-categories was applied to generate the risk model. Primary clinical endpoint was overall survival (OS). Performances of the models were compared against American Joint Committee on Cancer/Union for International Cancer Control (AJCC/UICC) 8th edition TNM-stage classification and two published recursive partitioning analysis (RPA) models, and were validated in the validation cohorts.

RESULTS

We chose a cfEBV DNA cutoff of ⩾2,000 copies for optimal risk discretization of OS, disease-free survival (DFS) and distant metastasis-free survival (DMFS) in the training cohort. AHR modeling method divided NPC into six risk groups with significantly disparate survival (  < 0.001 for all): AHR1, T1N0; AHR2A, T1N1/T2-3N0 cfEBV DNA  < 2,000 (EBV); AHR2B, T1N1/T2-3N0 cfEBV DNA ⩾ 2,000 (EBV) and T1-2N2/T2-3N1 EBV; AHR3, T1-2N2/T2-3N1 EBV and T3N2/T4N0 EBV; AHR4, T3N2/T4 N0-1 EBV and T1-3N3/T4N1-3 EBV; AHR5, T1-3N3/T4 N2-3 EBV. Our AHR model outperformed the published RPA models and TNM stage with better hazard consistency (1.35 3.98-12.67), hazard discrimination (5.29 6.69-13.35), explained variation (0.248 0.164-0.225), balance (0.385 0.438-0.749) and C-index (0.707 0.662-0.700). In addition, our AHR model was superior to the TNM stage for risk stratification of OS in two validation cohorts (  < 0.001 for both).

CONCLUSION

Herein, we developed and validated a risk classification system that combines the AJCC/UICC 8th edition TN-stage classification and cfEBV DNA for non-metastatic NPC. Our new clinicomolecular model provides improved OS prediction over the current staging system.