A systematic review and network meta-analysis of immunotherapy and targeted therapy for advanced melanoma

晚期黑色素瘤免疫疗法和靶向疗法的系统评价和网络荟萃分析

阅读:1

Abstract

Immune and BRAF-targeted therapies have changed the therapeutic scenario of advanced melanoma, turning the clinical decision-making a challenging task. This Bayesian network meta-analysis assesses the role of immunotherapies and targeted therapies for advanced melanoma. We retrieved randomized controlled trials testing immune, BRAF- or MEK-targeted therapies for advanced melanoma from electronic databases. A Bayesian network model compared therapies using hazard ratio (HR) for overall survival (OS), progression-free survival (PFS), and odds ratio (OR) for response rate (RR), along with 95% credible intervals (95% CrI), and probabilities of drugs outperforming others. We assessed the impact of PD-L1 expression on immunotherapy efficacy. Sixteen studies evaluating eight therapies in 6849 patients were analyzed. For OS, BRAF-MEK combination and PD-1 single agent ranked similarly and outperformed all other treatments. For PFS, BRAF-MEK combination surpassed all other options, including CTLA-4-PD-1 dual blockade hazard ratio (HR: 0.56; 95% CrI: 0.33-0.97; probability better 96.2%), whereas BRAF single agent ranked close to CTLA-4-PD-1 blockade. For RR, BRAF-MEK combination was superior to all treatments including CTLA-4-PD-1 (OR: 2.78; 1.18-6.30; probability better 97.1%). No OS data were available for CTLA-4-PD-1 blockade at the time of systematic review, although PFS and RR results suggested that this combination could also bring meaningful benefit. PD-L1 expression, as presently defined, failed to inform patient selection to PD-1-based immunotherapy. BRAF-MEK combination seemed an optimal therapy for BRAF-mutated patients, whereas PD-1 inhibitors seemed optimal for BRAF wild-type patients. Longer follow-up is needed to ascertain the role of CTLA-4-PD-1 blockade. Immunotherapy biomarkers remain as an unmet need.

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。