Clinical trial design for microarray predictive marker discovery and assessment

Abstract

Transcriptional profiling technologies that simultaneously measure the expression of thousands of mRNA species represent a powerful new clinical research tool. Similar to previous laboratory analytical methods including immunohistochemistry, PCR and in situ hybridization, this new technology may also find its niche in routine diagnostics. Outcome predictors discovered by these methods may be quite different from previous single-gene markers. These novel tests will probably combine the information embedded in the expression of multiple genes with mathematical prediction algorithms to formulate classification rules and predict outcome. The performance of machine learning-algorithm-based diagnostic tests may improve as they are trained on larger and larger sets of samples, and several generations of tests with improving accuracy may be introduced sequentially. Several gene-expression profiling-technology platforms are mature enough for clinical testing. The most important next step that is needed for further progress is the development and validation of multigene predictors in prospectively designed clinical trials to determine the true accuracy and clinical value of this new technology. This manuscript reviews methodological and statistical issues relevant to clinical trial design to discover and validate multigene predictors of response to therapy.