Sex as a predictor of response to cancer immunotherapy.
Sex as a predictor of response to cancer immunotherapy.
复制标题
性别作为癌症免疫治疗反应的预测因子。
DOI：
10.1016/s1470-2045(18)30517-5
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Wei,Lee-Jen
中科院分区：
文献类型：
--
作者：
Claggett,Brian;Tian,Lu;McCaw,ZacharyR;Takeuchi,Masahiro;Wei,Lee-Jen
法比奥·康福尔蒂和他的同事做了一项荟萃分析1，以评估免疫检查点抑制剂在男性和女性之间疗效的异质性。主要结果是总体存活。作者在他们的荟萃分析中纳入了20个随机对照试验，其中风险比(HR)被用来量化每个研究和性别的治疗效果。随机效应模型被用来获得男女总体治疗效果的合并HR。因此，20个个体HR被假设为从假设的超总体(即，在相似患者人群中进行这种干预的所有当前和未来的比较试验中的HR)中的随机样本，具有对数正态分布。合并HR男性为0·72(95%CI 0·65~0·79)，女性为0·86(0·79~0·93)，支持免疫检查点抑制剂。男性从免疫治疗中获得的总体生存收益明显高于女性。然而，目前还不清楚如何在临床上解释这种差异。使用HRs来评估总体治疗效果有几个局限性。首先，这种类型的免疫疗法往往具有延迟的治疗效果，表明HR不是治疗益处的充分总结衡量标准，难以在临床上解释。2-4第二，如果对数正态分布的研究水平HR的假设无效，则随机效应估计也可能无效。第三，假设选定的研究代表了大量研究中的随机样本，这种假设既不明确，也不容易理解。5即使每个研究特定的HR是适当的汇总测量，汇集的HR也不应被解释为任何特定患者群体的HR。此外，还不清楚如何证明这种超大的人力资源人群是否适用于患者
Fabio Conforti and colleagues did a meta-analysis1 to assess heterogeneity in efficacy of immune checkpoint inhibitors between men and women. The primary outcome was overall survival. The authors included 20 randomised controlled trials in their meta-analysis, in which the hazard ratio (HR) was used to quantify the treatment effect for each study and sex. A random-effects model was used to obtain a pooled HR for the overall treatment effect for each sex. Therefore, the 20 individual HRs were assumed to be a random sample from a hypothetical, super-population (ie, HRs from all current and future comparative trials of this type of intervention in similar patient populations) with a log-normal distribution. The pooled HR was 0· 72 (95% CI 0· 65–0· 79) for men and 0· 86 (0· 79–0· 93) for women, in favour of immune checkpoint inhibitors. The overall survival benefit from immunotherapy was significantly higher for men than women. However, it is not clear how to interpret this difference clinically.The use of HRs to assess overall treatment effect has several limitations. First, this type of immunotherapy often has a delayed treatment effect, indicating that a HR is not an adequate summary measure of treatment benefit and is difficult to interpret clinically. 2–4 Second, if the assumption of log-normally distributed study-level HRs is not valid, the random-effects estimate might not be valid. Third, the assumption that the selected studies represent a random sample from a super-population of studies is not well-defined or easily understood. 5 Even if each study-specific HR is an appropriate summary measure, the pooled HR should not be interpreted as the HR for any specific patient population. Moreover, it is not obvious how to justify whether this super-population of HRs would be applicable to patient