Ferrara Roberto, Pilotto Sara, Caccese Mario, Grizzi Giulia, Sperduti Isabella, Giannarelli Diana, Milella Michele, Besse Benjamin, Tortora Giampaolo, Bria Emilio
Department of Medical Oncology, Gustave Roussy, Villejuif, France.
U.O.C. Oncology, University of Verona, Comprehensive Cancer Center, Azienda Ospedaliera Universitaria Integrata, Verona, Italy.
J Thorac Dis. 2018 May;10(Suppl 13):S1564-S1580. doi: 10.21037/jtd.2018.01.131.
Immune checkpoint inhibitors (ICI) have widely reshaped the treatment paradigm of advanced cancer patients. Although multiple studies are currently evaluating these drugs as monotherapies or in combination, the choice of the most accurate statistical methods, endpoints and clinical trial designs to estimate the benefit of ICI remains an unsolved methodological issue. Considering the unconventional patterns of response or progression [i.e., pseudoprogression, hyperprogression (HPD)] observed with ICI, the application in clinical trials of novel response assessment tools (i.e., iRECIST) able to capture delayed benefit of immunotherapies and/or to quantify tumor dynamics and kinetics over time is an unmet clinical need. In addition, the proportional hazard model and the conventional measures of survival [i.e., median overall or progression free survival (PFS) and hazard ratios (HR)] might usually result inadequate in the estimation of the long-term benefit observed with ICI. For this reason, innovative methodologies such as milestone analysis, restricted mean survival time (RMST), parametric models (i.e., Weibull distribution, weighted log rank test), should be systematically investigated in clinical trials in order to adequately quantify the fraction of patients who are "cured", represented by the tails of the survival curves. Regarding predictive biomarkers, in particular PD-L1 expression, the integration and harmonization of the existing assays are urgently needed to provide clinicians with reliable diagnostic tests and to improve patient selection for immunotherapy. Finally, developing original and high-quality study designs, such as adaptive or basket biomarker enriched clinical trials, included in large collaborative platforms with multiple active sites and cross-sector collaboration, represents the successful strategy to optimally assess the benefit of ICI in the next future.
免疫检查点抑制剂（ICI）已广泛重塑了晚期癌症患者的治疗模式。尽管目前多项研究正在评估这些药物作为单一疗法或联合疗法的效果，但选择最准确的统计方法、终点指标和临床试验设计来评估ICI的获益仍然是一个未解决的方法学问题。考虑到ICI观察到的非常规反应或进展模式[即假性进展、超进展（HPD）]，能够捕捉免疫疗法延迟获益和/或随时间量化肿瘤动态和动力学的新型反应评估工具（即iRECIST）在临床试验中的应用是一项未满足的临床需求。此外，比例风险模型和传统的生存指标[即中位总生存期或无进展生存期（PFS）以及风险比（HR）]在评估ICI观察到的长期获益时通常可能不足。因此，应在临床试验中系统研究里程碑分析、受限平均生存时间（RMST）、参数模型（即威布尔分布、加权对数秩检验）等创新方法，以便充分量化由生存曲线尾部代表的“治愈”患者比例。关于预测生物标志物，特别是PD-L1表达，迫切需要整合和统一现有检测方法，为临床医生提供可靠的诊断测试，并改善免疫治疗的患者选择。最后，开发原创且高质量的研究设计，如适应性或生物标志物富集的篮子临床试验，纳入具有多个活跃位点和跨部门合作的大型协作平台，是在未来最佳评估ICI获益的成功策略。
