Use of genomic signatures in therapeutics development in oncology and other diseases

Abstract

Pharmacogenomics is the science of determining how the benefits and adverse effects of a drug vary among a target population of patients based on genomic features of the patient's germ line and diseased tissue. By identifying those patients who are most likely to respond while eliminating serious adverse effects, the therapeutic index of a drug can be substantially increased. This may facilitate demonstrating the effectiveness of the drug and may avoid subsequent problems due to serious adverse events. Our objective here is to provide clinical trial designs and analysis strategies for the utilization of genomic signatures as classifiers for patient stratification or patient selection in therapeutics development. We review methods for the development of genomic signature classifiers of treatment outcome in high-dimensional settings, where the number of variables available for prediction far exceeds the number of cases. The split-sample and crossvalidation methods for obtaining estimates of prediction accuracy in developmental studies are described. We present clinical trial designs for utilizing genomic signature classifiers in therapeutics development. The purpose of the classifier is to facilitate the identification of groups of patients with a high probability of benefiting from it and avoiding serious adverse events. We distinguish exploratory analysis during the development of the genomic classifier from prospective planning and rigorous testing of therapeutic hypotheses in studies that utilize the genomic classifier in therapeutics development. We discuss a variety of clinical trial designs including those utilizing specimen collection and assay prospectively for newly accrued patients and those involving a prospectively planned analysis of archived specimens from a previously conducted clinical trial. Our discussion of the development and use of classifiers of efficacy is mostly focused on applications in oncology using classifiers based on biomarkers measured in tumors. Some of the same considerations apply, however, to development of efficacy and safety classifiers in nononcologic diseases based on single-nucleotide germline polymorphisms.