Suppr超能文献

广泛期小细胞肺癌一线治疗的多维比较评估:临床疗效和安全性的系统评价与网络荟萃分析

Multidimensional comparative evaluation of first-line therapies for extensive-stage small cell lung cancer: a systematic review and network meta-analysis of clinical efficacy and safety profiles.

作者信息

Jiang Ziyao, Zhao Fangrui, Li Butuo, He Junyi, Yang Huiwen, Ji Yuhan, Zou Bing, Yu Jinming, Wang Linlin

机构信息

Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, 250117, Shandong, China.

Department of Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, 250117, Shandong, China.

出版信息

BMC Cancer. 2025 Aug 9;25(1):1292. doi: 10.1186/s12885-025-14750-4.

Abstract

BACKGROUND

The first-line treatment for extensive-stage small cell lung cancer (ES-SCLC) has evolved from chemotherapy alone to chemoimmunotherapy. However, the improvements in overall survival (OS) and progression-free survival (PFS) have been modest. Therefore, this study employs a comprehensive multidimensional evaluation framework to identify optimized therapeutic combinations with enhanced efficacy and improved safety profiles in the immunotherapy era.

METHODS

An adaptive search strategy was employed to retrieve all relevant literature from electronic databases, including PubMed, Embase, Web of Science, and the Cochrane Library, from database inception to November 2024. The retrieved studies were carefully screened according to pre-designed inclusion and exclusion criteria. Clinical research articles and their supplementary materials that met the criteria were obtained and thoroughly reviewed. Manual data extraction was conducted, with the safety data and efficacy outcomes. A network meta-analysis of all acquired data for each outcome was performed. The study protocol was pre-registered in PROSPERO, CRD42024612944.

RESULTS

This network meta-analysis included 6,473 patients from 14 head-to-head randomized controlled trials (RCTs). Compared with etoposide-platinum chemotherapy combined with a programmed cell death ligand 1 inhibitor (the Chemo + PD-L1 regimen), the addition of anlotinib (the Chemo + PD-L1 + Anlo regimen) resulted in better PFS (hazard ratio (HR), 0.42; 95% confidence interval (CI), 0.33-0.54) and objective response rate (ORR) (odds ratio (OR), 1.81; 95% CI, 1.13-2.91). Moreover, adding BMS-986012 (anti-fucosyl-GM1 antibodies) to the Chemo + PD-L1 regimen ranked first in the surface under the cumulative ranking curve (SUCRA, 0.96) analysis for OS. Compared with the Chemo + PD-L1 regimen, the addition of an anti-CTLA-4 inhibitor (the Chemo + PD-L1 + CTLA-4 regimen) was associated with an increased risk of treatment-related adverse events (TRAEs) of grades ≥ 3 (risk ratio (RR), 1.19; 95%CI, 1.04-1.36).

CONCLUSIONS

Incorporating anlotinib into the Chemo + PD-L1 regimen can be a viable first-line option for patients with high tumor burden, but cannot fully replace the current first-line standard-of-care (SOC). Chemoimmunotherapy combined with immune-related targeting drugs demonstrates the potential to improve overall survival.

摘要

背景

广泛期小细胞肺癌(ES-SCLC)的一线治疗已从单纯化疗演变为化疗联合免疫治疗。然而,总生存期(OS)和无进展生存期(PFS)的改善并不显著。因此,本研究采用综合多维评估框架,以确定在免疫治疗时代具有更高疗效和更好安全性的优化治疗组合。

方法

采用适应性检索策略,从电子数据库(包括PubMed、Embase、Web of Science和Cochrane图书馆)中检索从数据库建立至2024年11月的所有相关文献。根据预先设计的纳入和排除标准对检索到的研究进行仔细筛选。获取符合标准的临床研究文章及其补充材料并进行全面审查。进行人工数据提取,包括安全性数据和疗效结果。对每个结局的所有获取数据进行网络荟萃分析。研究方案已在PROSPERO(CRD42024612944)中预先注册。

结果

该网络荟萃分析纳入了来自14项头对头随机对照试验(RCT)的6473例患者。与依托泊苷-铂类化疗联合程序性细胞死亡配体1抑制剂(化疗+PD-L1方案)相比,添加安罗替尼(化疗+PD-L1+安罗替尼方案)可带来更好的PFS(风险比(HR),0.42;95%置信区间(CI),0.33-0.54)和客观缓解率(ORR)(优势比(OR),1.81;95%CI,1.13-2.91)。此外,在化疗+PD-L1方案中添加BMS-986012(抗岩藻糖基-GM1抗体)在OS的累积排序曲线下面积(SUCRA,0.96)分析中排名第一。与化疗+PD-L1方案相比,添加抗CTLA-4抑制剂(化疗+PD-L1+CTLA-4方案)与≥3级治疗相关不良事件(TRAEs)风险增加相关(风险比(RR),1.19;95%CI,1.04-1.36)。

结论

将安罗替尼纳入化疗+PD-L1方案对于肿瘤负荷高的患者可能是一种可行的一线选择,但不能完全取代当前的一线标准治疗(SOC)。化疗联合免疫治疗与免疫相关靶向药物显示出改善总生存期的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d32f/12335103/8d3a89be6540/12885_2025_14750_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验