Betting on biomarkers.
Betting on biomarkers.
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押注于生物标志物。
DOI：
10.1176/appi.ajp.2010.10121738
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
Perlis,RoyH
中科院分区：
文献类型：
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作者：
Perlis,RoyH
在这期杂志中，约翰逊及其同事（1）报道了一项关于昂丹司琼（一种5-羟色胺受体拮抗剂）治疗酒精依赖患者的研究。如果他们进行了一项标准的随机安慰剂对照试验，他们将发现治疗组之间在主要结局指标上没有统计学显著差异，并就此停止。也就是说，平均而言，接受昂丹司琼的研究参与者饮酒的可能性并没有明显低于接受安慰剂的参与者。然而，相反，作者做了一个重要而冒险的赌注，选择根据常见的遗传变异对随机化进行分层，然后在遗传定义的亚组中分析他们的结果。通过使用生物标记物，原本模糊的治疗反应图像变得更加清晰，指出了一种潜在的新型饮酒干预措施-但仅限于一个患者亚组。生物标志物可以以多种方式用于随机试验，研究设计分为几大类。第一种也是最传统的是事后分析（有时称为回顾性-前瞻性分析[2]），其中生物标志物仅作为协变量或调节剂进行分析。在事后分析中，假阳性结果的风险很高，并且随着检查的潜在主持人的数量而增加，但是这种方法只需要最少的假设，只需要有远见地收集要研究的标记。最近在轻度至中度阿尔茨海默病的bapineuzumab 2期研究中证明了这种方法的潜在力量（3）。这项研究作为一个整体未能在其主要终点上将药物与安慰剂分开，在过去，这一结果可能导致该化合物被搁置。然而，事后分析发现APOE 4 β-4非携带者亚组的影响更大，导致下一步研究集中在该患者亚组。第二类是生物标志物富集设计，其中研究人员在特定亚组中做出较大效应的“强”假设，并选择仅从该亚组中招募受试者。虽然生物标志物用于此目的相对新颖，但丰富设计的概念并不新颖;事实上，一代人的抗抑郁试验已经探索了严重性阈值，结果好坏参半。丰富设计应该更有效，允许较小的样本量来证明给定的效应量。另一方面，当感兴趣的组不太常见或当生物标志物的鉴定更劳动密集时，这种富集设计可能不可行。此外，从监管和科学的角度来看，它们几乎总是需要进行后续研究，以确定效果的特异性。也就是说，如果治疗在标志物阳性组中有效，那么它在标志物阴性组中是否有效？解决这些限制的第三类是生物标记分层设计，这是约翰逊和同事在这里使用的设计。在这种方法中，研究人员再次假设标记物将与差异反应相关。然而，研究人员并没有排除标记阴性的受试者，
In this issue, Johnson and colleagues (1) report on a study of ondansetron, a serotonin receptor antagonist, for alcohol-dependent patients. Had they conducted a standard randomized placebo-controlled trial, they would have found no statistically significant difference between treatment groups on the primary outcome measure and stopped there. That is, on average, their study participants were not significantly less likely to drink if they received ondansetron than if they received placebo. Instead, however, the authors made an important and risky bet, choosing to stratify randomization according to a common genetic variation and then to analyze their results within genetically defined subgroups. With the use of a biological marker, an otherwise murky picture of treatment response became clearer, pointing to a potential novel intervention for drinking—but only in one patient subgroup. Biomarkers may be employed in randomized trials in a variety of ways, and study designs doing so fall into several broad categories. The first and most traditional is the post hoc analysis (sometimes referred to as a retrospective-prospective analysis [2]), in which the biomarker is simply analyzed as a covariate or moderator. The risk of false positive findings in post hoc analyses is high, and it increases with the number of potential moderators examined, but this approach requires the fewest assumptions a priori—only the foresight to collect the marker to be studied. The potential power of this approach was recently demonstrated in a phase 2 study of bapineuzumab in mild to moderate Alzheimer’s disease (3). The study as a whole failed to separate drug from placebo on its primary endpoint, and in the past this result might have led the compound to be shelved. However, post hoc analysis identified larger effects in the subgroup of APOE4 epsilon-4 noncarriers, leading to next-step studies focusing on this patient subgroup. A second category is the biomarker-enriched design, in which the researchers make the “strong” assumption of larger effect in a particular subgroup and elect to enroll subjects only from that subgroup. While the use of a biomarker for this purpose is relatively novel, the concept of an enriched design is not; indeed, a generation’s worth of antidepressant trials have explored severity thresholds, with mixed results. Enriched designs should be more efficient, allowing smaller sample sizes to demonstrate a given effect size. On the other hand, such enriched designs may not be feasible when the group of interest is less common or when identification of the biomarkers is more labor intensive. Also, from a regulatory as well as a scientific perspective, they will almost always entail a follow-up study to establish specificity of effect. That is, if the treatment works in the marker-positive group, does it work in the marker-negative group? A third category, which addresses these limitations, is the biomarker-stratified design, which is the one used by Johnson and colleagues here. In this approach, the investigators again assume that a marker will be associated with a differential response. However, rather than excluding marker-negative subjects, the researchers simply stratify