Utility of high dimensional genomic composite biomarkers in therapeutic and/or diagnostic development

The recent advances of the high throughput biotechnology have made the genome-wide scanning, single nucleotide polymorphisms (SNP) profiling and proteomic pattern profiling possible, in addition to the more traditional candidate genes approach. In this paper, we collectively use the term genomics to refer to the various technologies. As a result, a massive exciting biological data generated from the genomic technology has increased the complexity of the research questions we seek to answer. The added characteristics of using genomics in therapeutic drug development and in the co-development of genomic biomarker diagnostic test have been publicly discussed and have received much attention. Pharmacogenomics (PG) is the science of determining how the clinical benefits and related adverse effects of a drug vary among a population of patients targeted based on genomic features of the patient's disease tissue, blood sample or biological specimen. The genomic features obtained from microarrays, SNP-arrays, proteomic arrays are to be selected to determine the genomic composite biomarker (GCB) classifier. The bioinformatics involved has helped brought down the high dimensional genomics to a much reduced dimension in selecting the GCB classifier. The dimension reduction has contributed to the designing of an adaptive and flexible pharmacogenomic clinical trial contrasting the more conventional well-controlled double-blind randomized clinical trials.