Personalized medicine: Advantages and shortcomings of genomics versus metabonomics
Authors: Daniel Nebert
Journal: Clinical Cancer Research (2008)

PL02-01  Sequencing of the entire human genome, the mapping of common haplotypes of single-nucleotide polymorphisms (SNPs) by HapMap Phases I and II, the Encyclopaedia of DNA Elements (ENCODE) Project, and cost-effective genotyping technologies leading to genome-wide association studies (GWAS) have led recently to incredible advances in the fields of human genomics and pharmacogenomics. These breakthroughs have combined convincingly in the past several years to demonstrate the requirements needed for all genotype-phenotype association studies, and to separate true associations from the plethora of false positives.  While research in human genetics has moved from monogenic to oligogenic to complex diseases, its pharmacogenetics branch has followed, usually a few years behind. The continuous discoveries, even today, of new surprises about our genome cause us to question the large number of reviews declaring that “personalized medicine is almost here” or that “individualized drug therapy will soon be a reality.” After the 2007 publication of the ENCODE data-- in which ~1% of selected portions of the human genome was characterized and studied in-depth including comparative genomics with the chimpanzee, mouse, and pufferfish genomes-- it has now become apparent that we no longer know what “a gene” is. In recent years, the definition of “a gene” had been:  “A segment of DNA, including all regulatory sequences for that DNA, which encodes a functional product-- whether it is a protein, small peptide, or one of the many classes of regulatory RNA.”  Among the massive amounts of data discovered by ENCODE was the finding that regulatory sequences for a transcript on one chromosome can be located on a second and/or third chromosome. Moreover, genes that are located on chromosomes geographically next to the nuclear membrane are more likely to exhibit high expression levels than genes located on chromosomes further inside the nucleus. It has also become increasingly apparent that complex diseases reflect the contributions of hundreds of genes, wherein each gene usually contributes no more than 1% to the overall phenotype (1). Thus, the current proposed definition of “a gene” is: “A union of genomic sequences encoding a coherent set of potentially overlapping functional products.” This definition sidesteps all the complexities of regulation and transcription (2).  The exploding fields of epigenomics and copy number variants (CNVs) have also thrown a monkey wrench into our hopes for simplifying personalized medicine and/or individualized drug therapy. Even monozygotic (“identical”) twins show differences in methylated DNA and carry varying numbers of CNVs, perhaps explaining why one monozygotic twin may develop, e.g., leukemia at age 55 while the other might never be affected by this disease.  In summary, dozens of reasons exist to show that an “unequivocal genotype” or even an “unequivocal phenotype” is virtually impossible to achieve in current limited-size studies of human populations. This problem (of insufficiently stringent criteria) leads to a decrease in statistical power and, consequently, equivocal interpretation of most genotype-phenotype association studies.  What is the best approach for the future? GWAS, using populations of one thousand or more per group, is a possible portion of the answer. It remains unclear, however, whether personalized medicine or individualized drug therapy will ever be achievable by means of DNA testing alone. It is therefore proposed that the future solution might lie in some combination of genomics, transcriptomics, proteomics, and especially metabonomics. This latter developing field, i.e., studying urinary metabolite profiles, seems very promising. If there is any drawback, it is because metabonomics is extremely sensitive: metabolite profiles can be altered easily by modest exercise, menstrual cycle, diet, and even use of an antibiotic that disturbs the intestinal flora. However, by examining and comparing urinary metabolites in the same patient-- year after year, decade after decade-- one should be able to capture not only changes that reflect epigenomics and differences in CNV number, but also any other forms of non-Mendelian traits caused by the environment (changes in life-style, occupation, diet, chronic disease, etc.).  References  1. Nebert DW, Zhang G, Vesell ES. From human genetics and genomics to pharmacogenetics and pharmacogenomics: Past lessons, future directions. Drug Metab Rev 2008;40:187-224.  2. Gerstein MB, Bruce C, Rozowsky JS, Zheng D, Du J, Korbel JO, Emanuelsson O, Zhang ZD, Weissman S, Snyder M.  What is a gene, post-ENCODE? History and updated definition. Genome Res 2007;17:669-81.