Predictive and prognostic significance of M descriptors of the 8th TNM classification for advanced NSCLC patients treated with immune checkpoint inhibitors

第八版TNM分期中M描述符对接受免疫检查点抑制剂治疗的晚期非小细胞肺癌患者的预测和预后意义

阅读:1

Abstract

BACKGROUND: A strong association between M descriptors and prognosis of non-small cell lung cancer (NSCLC) has been demonstrated recently. However, its predictive and prognostic significance for advanced NSCLC patients treated with immune checkpoint inhibitors (ICIs) remain unclear. In this study, we aimed at investigating the impact of M descriptors on clinical outcomes in those patients. METHODS: A retrospective analysis was conducted. Patients treated with more than two cycles of ICIs were included. Detailed characteristics and clinical response after immunotherapy were recorded. M descriptors were classified into M1a, M1b, and M1c according to the 8th TNM classification. RESULTS: A total of 103 patients were enrolled, including 42 with M1a disease, 16 with M1b disease and 45 with M1c disease. Patients with M1a disease demonstrated significant longer median progress-free survival (PFS) (11.9 vs. 4.1 and 3.2 months, respectively, P=0.0002) and overall survival (OS) (35 vs. 22.1 and 12 months, P=0.02) than those with M1b and M1c disease. Patients with M1a disease showed higher objective response rate (ORR) (28.6% vs. 14.8%, P=0.08) and disease control rate (DCR) (81% vs. 59%, P=0.02) compared with those with M1b and M1c disease. Multivariate analysis identified M1a stage as being independently associated with prolonged PFS and had better OS than those with M1c disease (P=0.05) but not M1b disease (P=0.06). CONCLUSIONS: The current study demonstrated a clear association between M descriptors and the therapeutic response to ICIs and confirmed its prognostic role in advanced patients treated with ICIs monotherapy. M descriptors may need to be stratified in future study design.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。