Chen Fo-Ping, Lin Li, Liang Jin-Hui, Tan Sze Huey, Ong Enya H W, Luo Ying-Shan, Huang Luo, Sim Adelene Y L, Wang Hai-Tao, Gao Tian-Sheng, Deng Bin, Zhou Guan-Qun, Kou Jia, Chua Melvin L K, Sun Ying
Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, Guangzhou, China.
Department of Radiation Oncology, Wuzhou Red Cross Hospital, Wuzhou, China.
Ther Adv Med Oncol. 2021 Oct 26;13:17588359211052417. doi: 10.1177/17588359211052417. eCollection 2021.
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).
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.
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).
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.
本研究的目的是构建一个将游离 Epstein-Barr 病毒（cfEBV）DNA 与 T 分期和 N 分期相结合的风险分类系统，以更好地预测鼻咽癌（NPC）的预后。
从两个癌症中心识别出 10149 例经活检证实的非转移性 NPC 的临床记录；这包括一个训练队列（n = 9259）和两个验证队列（n = 890；包括一个随机对照 3 期试验队列）。采用基于 cfEBV DNA 和 TN 分期的两层分层的调整风险比（AHR）方法来生成风险模型。主要临床终点是总生存期（OS）。将模型的性能与美国癌症联合委员会/国际癌症控制联盟（AJCC/UICC）第 8 版 TNM 分期以及两个已发表的递归分区分析（RPA）模型进行比较，并在验证队列中进行验证。
我们选择 cfEBV DNA 截断值≥2000 拷贝，以在训练队列中实现 OS、无病生存期（DFS）和无远处转移生存期（DMFS）的最佳风险离散化。AHR 建模方法将 NPC 分为六个风险组，其生存期有显著差异（所有 P < 0.001）：AHR1，T1N0；AHR2A，T1N1/T2 - 3N0 且 cfEBV DNA < 2000（EBV）；AHR2B，T1N1/T2 - 3N0 且 cfEBV DNA≥2000（EBV）以及 T1 - 2N2/T2 - 3N1 EBV；AHR3，T1 - 2N2/T2 - 3N1 EBV 以及 T3N2/T4N0 EBV；AHR4，T3N2/T4 N0 - 1 EBV 以及 T1 - 3N3/T4N1 - 3 EBV；AHR5，T1 - 3N3/T4 N2 - 3 EBV。我们的 AHR 模型在风险一致性（1.35 3.98 - 12.67）、风险区分度（5.29 6.69 - 13.35）、解释变异度（0.248 0.164 - 0.225）、平衡性（0.385 0.438 - 0.749）和 C 指数（0.707 0.662 - 0.700）方面优于已发表的 RPA 模型和 TNM 分期。此外，在两个验证队列中，我们的 AHR 模型在 OS 风险分层方面优于 TNM 分期（两者 P < 0.001）。
在此，我们开发并验证了一个将 AJCC/UICC 第 8 版 TN 分期分类和 cfEBV DNA 相结合的非转移性 NPC 风险分类系统。我们的新临床分子模型比当前分期系统提供了更好的 OS 预测。
