研究动态
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化学免疫治疗后局部放疗治疗的新发转移性鼻咽癌的递归分区分析模型。

Recursive partitioning analysis model for de novo metastatic nasopharyngeal carcinoma treated with locoregional radiotherapy following chemoimmunotherapy.

发表日期:2024 Oct 18
作者: D Wen, L Gu, H Long, S Liu, M Luo, R Li, R Liu, J Lin, J Jin, L Xiong, L Tang, H Mai, L Liu, Y Liang, Q Chen, S Guo
来源: Cell Death & Disease

摘要:

化学免疫疗法是新发转移性鼻咽癌(dmNPC)的一线治疗方法,额外的局部放射治疗(LRRT)可显着延长患者的生存期。然而,新发转移性鼻咽癌表现出相当大的异质性,导致患者结果存在显着差异。我们为接受一线化学免疫治疗加 LRRT 的患者开发并验证了一种预后工具,并评估局部治疗 (LT) 对不同风险水平的远处转移的益处。我们研究了 364 名接受初始铂类化疗和抗程序化治疗的 dmNPC 患者。细胞死亡蛋白 1 免疫疗法随后进行 LRRT。患者被随机分为训练组和验证组(7:3 比例)。主要终点是无进展生存期(PFS)。使用递归分区分析 (RPA) 开发了 PFS 预后模型。RPA 模型根据转移病灶数量、肝转移状态和治疗后 Epstein-Barr 病毒 DNA 水平将患者分为五个预后组。生存分析确定了三个不同的风险组。与中风险组和低风险组相比,高风险患者的 PFS 显着较差(2 年 PFS 率:训练队列:13.7% vs 69.4% vs 94.4%,P < 0.001;验证队列:7.8% vs 65.1% vs 87.3 %,P < 0.001)。我们调查了 LT 对这些风险组中远处转移的影响,发现只有中危组的患者才能从 LT 中获益(2 年 PFS 率:77.5% 对比 64.0%;风险比 = 0.535,95% 置信区间0.297-0.966,P = 0.035)。相反,在低风险(P = 0.218)和高风险亚组(P = 0.793)中没有观察到 LT 对远处转移的生存获益。我们基于 RPA 的预后模型整合了转移病灶的数量、肝转移状态和治疗后 Epstein-Barr 病毒 DNA 水平可预测接受化学免疫治疗加 LRRT 的 dmNPC 患者的 PFS。该模型提供了个性化的治疗指导,表明中危组的患者可能会从远处转移的 LT 中受益,而高危组和低危组的患者可能不会受益。版权所有 © 2024 作者。由爱思唯尔有限公司出版。保留所有权利。
Chemoimmunotherapy is the first-line treatment of de novo metastatic nasopharyngeal carcinoma (dmNPC), with additional locoregional radiotherapy (LRRT) significantly prolonging patient survival. De novo metastatic nasopharyngeal carcinoma, however, demonstrates considerable heterogeneity, resulting in significant variability in patient outcomes. We developed and validated a prognostic tool for patients undergoing first-line chemoimmunotherapy plus LRRT and to evaluate the benefit of local therapy (LT) for distant metastases across different risk levels.We studied 364 dmNPC patients receiving initial platinum-based chemotherapy and anti-programmed cell death protein 1 immunotherapy followed by LRRT. Patients were randomly divided into training and validation cohorts (7 : 3 ratio). The primary endpoint was progression-free survival (PFS). A prognostic model for PFS was developed using recursive partitioning analysis (RPA).An RPA model categorized patients into five prognostic groups based on number of metastatic lesions, liver metastasis status, and post-treatment Epstein-Barr virus DNA levels. Survival analysis identified three distinct risk groups. High-risk patients had significantly poorer PFS compared with medium- and low-risk groups (2-year PFS rate: training cohort: 13.7% versus 69.4% versus 94.4%, P < 0.001; validation cohort: 7.8% versus 65.1% versus 87.3%, P < 0.001). We investigated the impact of LT for distant metastases across these risk groups and found that only patients in the medium-risk group derived benefit from LT (2-year PFS rate: 77.5% versus 64.0%; hazard ratio = 0.535, 95% confidence interval 0.297-0.966, P = 0.035). Conversely, no survival benefit from LT for distant metastases was observed in the low-risk (P = 0.218) and high-risk subgroups (P = 0.793).Our RPA-based prognostic model integrates number of metastatic lesions, liver metastasis status, and post-treatment Epstein-Barr virus DNA levels to predict PFS in dmNPC patients undergoing chemoimmunotherapy plus LRRT. This model offers personalized treatment guidance, suggesting that patients in the medium-risk group may benefit from LT for distant metastases, while those in high- and low-risk groups may not.Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.