With lowering costs of sequencing and genetic profiling techniques, genetic drivers can now be detected readily in tumors but current prognostic models for Natural-killer/T cell lymphoma (NKTCL) have yet to fully leverage on them for prognosticating patients. Here, we used next-generation sequencing to sequence 260 NKTCL tumors, and trained a genomic prognostic model (GPM) with the genomic mutations and survival data from this retrospective cohort of patients using LASSO Cox regression. The GPM is defined by the mutational status of 13 prognostic genes and is weakly correlated with the risk-features in International Prognostic Index (IPI), Prognostic Index for Natural-Killer cell lymphoma (PINK), and PINK-Epstein–Barr virus (PINK-E). Cox-proportional hazard multivariate regression also showed that the new GPM is independent and significant for both progression-free survival (PFS, HR: 3.73, 95% CI 2.07–6.73; p < .001) and overall survival (OS, HR: 5.23, 95% CI 2.57–10.65; p = .001) with known risk-features of these indices. When we assign an additional risk-score to samples, which are mutant for the GPM, the Harrell's C-indices of GPM-augmented IPI, PINK, and PINK-E improved significantly (p < .001, χ² test) for both PFS and OS. Thus, we report on how genomic mutational information could steer toward better prognostication of NKTCL patients.

中文翻译：

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国际队列中自然杀伤/T 细胞淋巴瘤患者生存的基因组增强多变量预后模型

随着测序和基因分析技术成本的降低，现在可以很容易地在肿瘤中检测到遗传驱动因素，但目前的自然杀伤/T 细胞淋巴瘤 (NKTCL) 预后模型尚未充分利用它们来预测患者。在这里，我们使用下一代测序对 260 个 NKTCL 肿瘤进行测序，并使用 LASSO Cox 回归训练了一个基因组预后模型 (GPM)，其中包含来自该回顾性患者队列的基因组突变和生存数据。GPM 由 13 个预后基因的突变状态定义，并且与国际预后指数 (IPI)、自然杀伤细胞淋巴瘤 (PINK) 预后指数和 PINK-EB 病毒 (PINK -E).p  < .001) 和总生存期（OS，HR：5.23，95% CI 2.57–10.65；p  = .001）以及这些指数的已知风险特征。当我们为 GPM 突变的样本分配额外的风险评分时，GPM 增强的 IPI、PINK 和 PINK-E 的 Harrell C 指数显着改善（p < .001，χ 2检验 ）^两种PFS和操作系统。因此，我们报告了基因组突变信息如何引导更好地预测 NKTCL 患者。