Pharmacogenomic Biomarker Classifiers

Much of the discussion about disease biomarkers is in the context of markers that measure some aspect of disease status, extent, or activity. Such biomarkers are often proposed for use in early detection of disease or as a surrogate endpoint for evaluating prevention or therapeutic interventions. The validation of such biomarkers is difficult for a variety of reasons, but particularly because the molecular pathogenesis of many diseases is incompletely understood, and hence it is not possible to establish the biological relevance of a measure of disease status.

A pharmacogenomic biomarker is any pre-treatment measurable quantity that can be used to select treatment; for example, the result of an immunohistochemical assay for a single protein, the abundance of a protein in serum, the abundance of mRNA transcripts for a gene in a sample of disease tissue, or the presence/absence status of a specified germline polymorphism or tumor mutation. A pharmacogenomic biomarker classifier is a mathematical function that translates the biomarker values to a set of prognostic categories. These categories generally correspond to levels of predicted clinical outcome. With the advent of gene expression profiling, it is increasingly common to define composite pharmacogenomic biomarker classifiers based on the levels of expression of dozens of genes. For a fully specified classifier, however, all of the parameters and cutpoints are specified for determining how to weight the different components and how to map the multivariate data into a defined set of categories. A completely defined classifier can be used to select patients and stratify patients for therapy in clinical trials that enable the clinical value of the classifier to be evaluated. Specifying only the genes involved does not enable one to structure prospective clinical validation experiments in which patients are assigned or stratified in prospectively well-defined ways.

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