(26-01-08) Letting the Genome out of the Bottle ? Will We Get Our Wish?

David J. Hunter, M.B., B.S., Sc.D., M.P.H., Muin J. Khoury, M.D., Ph.D., and Jeffrey M. Drazen, M.D.
It may happen soon. A patient, perhaps one you have known for years, who is overweight and does not exercise regularly, shows up in your office with an analysis of his whole genome at multiple single-nucleotide polymorphisms (SNPs). His children, who were concerned about his health, spent $1,000 to give him the analysis as a holiday gift. The test report states that his genomic profile is consistent with an increased risk of both heart disease and diabetes, and because the company that performed the analysis stated that the test was "not a clinical service to be used as the basis for making medical decisions," he is in the office for some "medical direction." What should you do?
This year has seen a dizzying number of genomewide association studies demonstrating associations between novel gene variants or chromosomal loci and common diseases and phenotypes. These studies rely on microarrays that can assess 300,000 or more SNPs in each DNA sample; researchers use these microarrays to examine interpersonal differences in inherited genetic variability and to compare the prevalence of gene variants among patients who have a given disease with that among controls. Such studies have identified associations with many gene variants that were not previously suspected to be related to the phenotypes under consideration. The new technologies involved have been a boon to researchers who needed unbiased clues as to the causation of diseases that may be used to develop new therapeutic and preventive interventions. The test undergone by the patient described above is one of the products of this new knowledge.
As of November 2007, two companies have made available direct-to-consumer "personal genome services" (www.23andme.com) or "gene profiles" (www.decodeme.com) that rely on the same arrays of 500,000 to 1 million SNPs used in genomewide association studies. A third company (www.navigenics.com) has announced that it will offer similar services later this year. Essentially, a client sends a DNA sample to one of these firms, which analyzes the sample by means of SNP array; the data are stored in an online private account, the results are compared with allele?phenotype databases maintained and updated by the company, and the customer receives a readout of his or her levels of risk for specific conditions.
But such premature attempts at popularizing genetic testing seem to neglect key aspects of the established multifaceted evaluation of genetic tests for clinical applications. First, there is the question of a test's analytic validity, "its ability to accurately and reliably measure the genotype of interest."1 Although appropriate monitoring and oversight of the analytic validity of genetic tests remain largely unaddressed,2 most researchers report that the analytic validity of these platforms is very high. It is likely that sample-handling errors are a greater threat to the validity of results than are genotypic misclassification errors. Yet even very small error rates per SNP, magnified across the genome, can result in hundreds of misclassified variants for any individual patient. Without transparent quality-control monitoring and proficiency testing, the real-world performance of these platforms is uncertain.
Second, one must consider clinical validity, or the ability of the test to detect or predict the associated disorder.1 Components of clinical validity include the test's sensitivity, specificity, and positive and negative predictive value. This is the area in which the data are in the greatest flux, and even the ardent proponents of genomic susceptibility testing would agree that for most diseases, we are still at the early stages of identifying the full list of susceptibility-associated variants. Most of the diseases listed by the direct-to-consumer testing companies (e.g., diabetes, various cancers, and heart disease) are so-called complex diseases thought to be caused by multiple gene variants, interactions among these variants, and interactions between variants and environmental factors. Thus, a full accounting of disease susceptibility awaits the identification of these multiple variants and their interactions in well-designed studies. What we have now is recognition of a limited number of variants associated with relative risks of diseases on the order of 1.5 or lower. Risk factors with this level of relative risk clearly do a poor job of distinguishing people who will develop these diseases from those who will not.3,4
Finally, there is the issue of the test's clinical utility, or the balance of its associated risks and benefits if it were to be introduced into clinical practice.1 Measures of utility address the question at the heart of the clinical application of a test: If a patient is found to be at risk for a disease, what can be done about it? This is the arena in which there are virtually no data available on the health impact of genomewide analysis. There are very few observational studies and almost no clinical trials that demonstrate the risks and benefits associated with screening for individual gene variants ? let alone testing for many hundreds of thousands of variants. Thus, any claim to clinical utility currently rests on the assumption that interventions that have proven successful in the general population will behave the same way in a genetically at-risk population. Many of these interventions ? such as smoking cessation, weight loss, increased physical activity, and control of blood pressure ? are likely to be broadly beneficial in relation to many diseases, regardless of a person's genetic susceptibility to a specific disease.
It may be argued that knowledge of increased susceptibility to a disease, such as type 2 diabetes, for which protective lifestyle interventions exist, will motivate patients to follow relevant recommendations. Yet as intuitively appealing as this contention may be, evidence to support it, particularly in the case of low-penetrance alleles, is scanty. The flip side, of course, is that patients who test negative may be falsely reassured and thus less motivated to comply with preventive recommendations. In the absence of evidence of efficacy, this rationale for susceptibility testing should be regarded with skepticism.
So what advice should a physician offer patients? For the patient who appears with a genome map and printouts of risk estimates in hand, a general statement about the poor sensitivity and positive predictive value of such results is appropriate, but a detailed consumer report may be beyond most physicians' skill sets. For the patient asking whether these services provide information that is useful for disease avoidance, the prudent answer is "Not now ? ask again in a few years." More information is needed on the clinical utility of this information in the light of existing disease-specific opportunities for prevention or early detection and the potential value that genomic profiles can add to that of simpler tools, such as the family health history. Finally, given the risk of commercial exploitation, if patients are determined to proceed, perhaps because they are simply curious, are genetic hobbyists, or are "early adopters" of new technology, it would make sense to encourage them to enroll in formal scientific studies.
Now that the genome is out of the bottle, how will our wish for better health be granted? Just as the emergence of a commercial entity (Celera) with ambitions to sequence the human genome spurred public projects to accelerate their efforts, perhaps the emergence of commercial personalized genomic services will galvanize efforts to plan and conduct the necessary translational research5 for the rational integration of genomic information into medical training and practice. Until the genome can be put to useful work, the children of the man described above would have been better off spending their money on a gym membership or a personal trainer so that their father could follow a diet and exercise regimen that we know will decrease his risk of heart disease and diabetes.

Source Information
Dr. Hunter is a professor in the Departments of Epidemiology and Nutrition at the Harvard School of Public Health, Boston, and a statistical consultant to the Journal. Dr. Khoury is the director of the National Office of Public Health Genomics at the Centers for Disease Control and Prevention, Atlanta. Dr. Drazen is the editor-in-chief of the Journal. The opinions expressed in this article do not necessarily reflect the views of the Department of Health and Human Services.

An interview with Dr. Muin Khoury, director of the National Office of Public Health Genomics at the Centers for Disease Control and Prevention, can be heard at www.nejm.org
Fonte: NEJM..
References
1. Haddow JE, Palomaki GE. ACCE: a model process for evaluating data on emerging genetic tests. In: Khoury MJ, Little J, Burke W, eds. Human genome epidemiology: a scientific foundation for using genetic information to improve health and prevent disease. New York: Oxford University Press, 2004:217-33.

2. Draft report of the Secretary's Advisory Committee on Genetics, Health, and Society. U.S. System of Oversight of Genetic Testing 11-5-2007. (Accessed December 12, 2007, at http://www4.od.nih.gov/oba/sacghs/reports.)
3. Ware JH. The limitations of risk factors as prognostic tools. N Engl J Med 2006;355:2615-2617. [Free Full Text]
4. Wald NJ, Hackshaw AK, Frost CD. When can a risk factor be used as a worthwhile screening test? BMJ 1999;319:1562-1565. [Free Full Text]
5. Khoury MJ, Gwinn M, Yoon PW, Dowling N, Moore CA, Bradley L. The continuum of translation research in genomic medicine: how can we accelerate the appropriate integration of human genome discoveries into health care and disease prevention? Genet Med 2007;9:665-674. [ISI][Medline]

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