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

With advances in high-throughput biological techniques, huge numbers of potentially predictive biomarkers are becoming routinely collected in modern clinical trials. However, current
designs do not make best use of these data, and there is potential for better approaches that
will 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 was
developed 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 variable
selection 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.