Methods for using high-dimensional biomarker information prospectively in clinical trials

随着高通量生物技术的进步，在现代临床试验中，大量的潜在预测生物标志物正变得司空见惯。然而，目前的设计并没有最大限度地利用这些数据，而且有可能出现更好的方法，提供更多关于哪些患者亚组受益，哪些患者不受益的信息。Freidlin和Simon1的自适应签名设计(ASD)是一项试验设计，旨在更好地利用生物标记物数据。它的目标是：1)开发一种预测性生物标记物标记，将患者归类为对治疗敏感或不敏感；2)测试敏感患者的治疗效果；以及3)测试所有患者的治疗效果。另一种方法是适应性浓缩设计(AED)，其中患者的资格标准在试验中根据观察到的生物标记物亚组的疗效进行调整。在这个项目中，学生将从适应性临床试验和高维统计分析领域学习最先进的统计技术。然后，他们将致力于将这些领域结合起来，以提出能够改进ASD和AED的设计。目前提出的ASD方法，通过分别测试治疗分配和每个生物标记物之间的相互作用来开发预测性生物标记物签名。当有许多相关的生物标志物可供选择时，这项技术的性能是已知的次优。与相同的潜在因果关系相关的生物标记物很可能被错误地包括在内。这导致签名的预测能力较低和过度自信的预测。我们将寻求用最先进的变量选择方法来更新ASD，例如贝叶斯稀疏回归2，这样ASD对于相关的高维生物标记数据具有良好的性能。然后，我们将致力于应用类似的方法来扩展AED，以便它也可以用于高维生物标记信息。这样的试验设计将在中期分析中开发一种生物标记物分类器，可以用来确定未来的患者是受益于治疗还是受到伤害。这打开了可能性，比如不招募可能受到伤害的患者，或者将患者分配到更有可能受益的治疗(当有多种实验性治疗可用时)。这样做的好处是，患者得到了更合乎道德的治疗，试验将更有效(因为招募的患者可能会有更高的治疗效果)。然而，我们也将调查这种方法的潜在缺陷。

With advances in high-throughput biological techniques, huge numbers of potentially predictive biomarkers are becoming routinely collected in modern clinical trials. However, currentdesigns do not make best use of these data, and there is potential for better approaches thatwill provide more information on which subgroups of patients benefit and which don't.The adaptive signature design (ASD) of Freidlin and Simon1 is a trial design that wasdeveloped to make better use of biomarker data. It aims to: 1) develop a predictive biomarker signature that classifies patients as 'sensitive' or 'non-sensitive' to the treatment; 2) test the treatment effect in sensitive patients; and 3) test the treatment effect in all patients. An alternative approach is the adaptive enrichment design (AED), in which the eligibility criteria of patients are adapted within the trial according to observed efficacy in biomarker subgroups. Proposed methodology for AEDs is limited to the use of one pre-specified biomarker.During this project, the student will learn about state-of-the-art statistical techniques from the fields of adaptive clinical trials and high-dimensional statistical analysis. They will then work on combining these fields in order to propose designs that can improve on the ASD and AED.The ASD method, as currently proposed, develops the predictive biomarker signature by testing for interaction between treatment assignment and each biomarker separately. This technique is known to have sub-optimal properties when there are many correlated biomarkers to choose from. Biomarkers that are associated with the same underlying causal effect are likely to be incorrectly included. This leads to lower predictive ability of the signature and over- confident predictions. We will seek to modernise the ASD with state of the art variableselection methods such as Bayesian sparse regression2, so that the ASD has good performance for correlated high-dimensional biomarker data.We will then work on applying similar methodology to extend the AED so that it can also be used with high-dimensional biomarker information. Such a trial design would develop a biomarker classifier at an interim analysis that can be used to determine whether future patients would benefit or be harmed by treatment. This opens up possibilities such as not recruiting patients who would likely be harmed, or allocating patients to treatments that are more likely to benefit them (when there are multiple experimental treatments available). The benefits of this are that patients are more ethically treated and the trial will be more efficient (as the recruited patients will likely have a higher treatment effect). However we will also investigate potential drawbacks of this approach.