A Vision for the Future of Genomics Research: A Blueprint for the Genomic Era

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Part IV

Grand Challenge II-3: Develop genome-based approaches to prediction of disease susceptibility and drug response, early detection of illness, and molecular taxonomy of disease states




The discovery of variants that affect risk for disease could potentially be used in individualized preventive medicine — including diet, exercise, lifestyle, and pharmaceutical intervention — to maximize the likelihood of staying well. For example, the discovery of variants that correlate with successful outcomes of drug therapy, or with unfortunate side effects, could potentially be rapidly translated into clinical practice. Turning this vision into reality will require the following: (1) unbiased determination of the risk associated with a particular gene variant, often overestimated in initial studies31; (2) technological advances to reduce the cost of genotyping (Box 2; and see "Quantum leaps," which will appear in the final part of this series); (3) research on whether this kind of personalized genomic information will actually alter health behaviours (see Grand Challenge II-5); (4) oversight of the implementation of genetic tests to ensure that only those with demonstrated clinical validity are applied outside of the research setting (Box 5); and (5) education of healthcare professionals and the public to be well-informed participants in this new form of preventive medicine (Box 6).







The time is right for a focused effort to understand, and potentially to reclassify, all human illnesses on the basis of detailed molecular characterization. Systematic analyses of somatic mutations, epigenetic modifications, gene expression, protein expression, and protein modification should allow the definition of a new molecular taxonomy of illness, which would replace our present, largely empirical, classification schemes and advance both disease prevention and treatment. The reclassification of neuromuscular diseases32 and certain types of cancer33 provides striking initial examples, but many more such applications are possible.

Such a molecular taxonomy would be the basis for the development of better methods for the early detection of disease, which often allows more effective and less costly treatments. Genomics and other large-scale approaches to biology offer the potential for developing new tools to detect many diseases earlier than is currently feasible. Such "sentinel" methods might include analysis of gene expression in circulating leukocytes, proteomic analysis of body fluids, and advanced molecular analysis of tissue biopsies. An example would be the analysis of gene expression in peripheral blood leukocytes to predict drug response. A focused effort to use a genomic approach to characterize serum proteins exhaustively in health and disease might also be highly rewarding.

Grand Challenge II-4: Use new understanding of genes and pathways to develop powerful new therapeutic approaches to disease

Pharmaceuticals on the market target fewer than 500 human gene products34. Even though not all of the 30,000 or so human protein-coding genes7 will have products targetable for drug development, this suggests that there is an enormous untapped pool of human gene-based targets for therapeutic intervention. In addition, the new understanding of biological pathways provided by genomics (see Grand Challenge I-2) should contribute even more fundamentally to therapeutic design.

The information needed to determine the therapeutic potential of a gene generally overlaps heavily with the information that reveals its function. The success of imatinib mesylate (Gleevec), an inhibitor of the BCRABL tyrosine kinase, in treating chronic myelogenous leukemia relied on a detailed molecular understanding of the disease's genetic cause35. This example offers promise that therapies based on genomic information will be particularly effective. Grand Challenge I-1 describes the "functionation" of the genome, which will increasingly be the critical first step in the development of new therapeutics. But stimulating basic scientists to approach biomedical problems with a genomic attitude is not enough. A therapeutic mindset, lacking in much of academic biomedical research and training, must be explicitly encouraged, and tools developed and provided for its implementation.

A particularly promising example of the gene-based approach to therapeutics is the application of "chemical genomics"25. This strategy uses libraries of small molecules (natural compounds, aptamers, or the products of combinatorial chemistry) and high-throughput screening to advance understanding of biological pathways and to identify compounds that act as positive or negative regulators of individual gene products, pathways, or cellular phenotypes. Although the pharmaceutical industry applies this approach widely as the first step in drug development, few academic investigators have access to this methodology or are familiar with its use.

Providing such access more broadly, through one or more centralized facilities, could lead to the discovery of a host of useful probes for biological pathways that would serve as new reagents for basic research and/or starting points for the development of new therapeutic agents (the "hits" from such library screens will generally require medicinal chemistry modifications to yield therapeutically usable compounds).

Also needed are new, more powerful technologies for generating deep molecular libraries, especially ones tagged to allow the ready determination of precise molecular targets. A centralized database of screening results should lead to further important biological insights. Generating molecular probes for exploring the basic biology of health and disease in academic laboratories would not supplant the major role of biopharmaceutical companies in drug development, but could contribute to the start of the drug development pipeline. The private sector would doubtless find many of these molecular probes of interest for further exploration through optimization by medicinal chemistry, target validation, lead compound identification, toxicological studies and, ultimately, clinical trials.

Academic pursuit of this first step in drug development could be particularly valuable for the many rare mendelian diseases, in which often the gene defect is known but the small market size limits the private sector's motivation to shoulder the expense of effective pharmaceutical development. Such translational research in academic laboratories, combined with incentives such as the US Orphan Drug Act, could profoundly increase the availability of effective treatments for rare genetic diseases in the next decade. Further, the development of therapeutic approaches to single-gene disorders might provide valuable insights into applying genomics to reveal the biology of more common disorders and developing more effective treatments for them (in the way that, for example, the search for compounds that target the presenilins has led to general therapeutic strategies for late-onset Alzheimer's disease36).

Grand Challenge II-5: Investigate how genetic risk information is conveyed in clinical settings, how that information influences health strategies and behaviours, and how these affect health outcomes and costs

Understanding how genetic factors affect health is often cited as a major goal of genomics, on the assumption that applying such understanding in the clinical setting will improve health. But this assumption actually rests on relatively few examples and data, and more research is needed to provide sufficient guidance about how to use genomic information optimally for improving individual or public health.

Theoretically, the steps by which genetic risk information would lead to improved health are: (1) an individual obtains genome-based information about his/her own health risks; (2) the individual uses this information to develop an individualized prevention or treatment plan; (3) the individual implements that plan; (4) this leads to improved health; and (5) healthcare costs are reduced. Scrutiny of these assumptions is needed, both to test them and to determine how each step could best be accomplished in different clinical settings.

Research is also required that critically evaluates new genetic tests and interventions in terms of parameters such as benefits, access, and cost. Such research should be interdisciplinary and use the tools and expertise of many fields, including genomics, health education, health behaviour research, health outcomes research, healthcare delivery analysis, and healthcare economics. Some of these fields have historically paid little attention to genomics, but high-quality research of this sort could provide important guidance in clinical decision-making — as the work of several disciplines has already been helpful in caring for people with an increased risk of colon cancer as a result of mutations in FAP or HNPCC37.

Grand Challenge II-6: Develop genome-based tools that improve the health of all

Disparities in health status constitute a significant global issue, but can genome-based approaches to health and disease help to reduce this problem? Social and other environmental factors are major contributors to health disparities; indeed, some would question whether heritable factors have any significant role. But population differences in allele frequencies for some disease-associated variants could be a contributing factor to certain disparities in health status, so incorporating this information into preventive and/or public-health strategies would be beneficial. Research is needed to understand the relationship between genomics and health disparities by rigorously evaluating the diverse contributions of socioeconomic status, culture, discrimination, health behaviours, diet, environmental exposures, and genetics.

It is also important to explore applications of genomics in the improvement of health in the developing world, where both human and non-human genomics will play significant roles. If we take malaria as an example, a better understanding of human genetic factors that influence susceptibility and response to the disease, and to the drugs used to treat it, could have a significant global impact. So too could a better understanding of the malarial parasite itself and of its mosquito vector, which the recently reported genome sequences 38,39 should provide. It will be necessary to determine the appropriate roles of governmental and non-governmental organizations, academic institutions, industry, and individuals to ensure that genomics produces clinical benefits for resource-poor nations, and is used to produce robust local research expertise.

To ensure that genomics research benefits all, it will be critical to examine how genomics-based health care is accessed and used. What are the barriers to equitable access, and how can they be removed? This is relevant not only in resource-poor nations, but also in wealthier countries where segments of society, such as indigenous populations, the uninsured, or rural and inner city communities, have traditionally not received adequate health care.

References
  1. Mendel,G.Versuche über Pflanzen-Hybriden.Verhandlungen des naturforschenden Vereines,Abhandlungen, Brünn 4, 3-47 (1866).

  2. Avery,O. T.,MacLeod,C.M.& McCarty, M. Studies of the chemical nature of the substance inducing transformation of pneumococcal types. Induction of transformation by a desoxyribonucleic acid fraction isolated from Pneumococcus Type III. J. Exp.Med. 79, 137-158 (1944).

  3. Watson, J.D. & Crick, F. H. C.Molecular structure of nucleic acids: A structure for deoxyribose nucleic acid. Nature 171, 737 (1953).

  4. Nirenberg, M.W. The genetic code: II. Sci.Am. 208, 80-94 (1963).

  5. Jackson,D. A., Symons, R. H. & Berg, P. Biochemical method for inserting new genetic information into DNA of Simian Virus 40: circular SV40 DNA molecules containing lambda phage genes and the galactose operon of Escherichia coli. Proc.Natl Acad. Sci. USA 69, 2904-2909 (1972).

  6. Cohen, S. N., Chang,A.C., Boyer, H.W. & Helling,R. B. Construction of biologically functional bacterial plasmids in vitro. Proc.Natl Acad. Sci. USA 70, 3240-3244 (1973).

  7. The International Human Genome Sequencing Consortium. Initial sequencing and analysis of the human genome. Nature 409, 860-921 (2001).

  8. Sanger, F.& Coulson, A. R. A rapid method for determining sequences in DNA by primed synthesis with DNA polymerase. J.Mol. Biol. 94, 441-448 (1975).

  9. Maxam, A. M. & Gilbert,W. A new method for sequencing DNA. Proc.Natl Acad. Sci. USA 74, 560-564 (1977).

  10. Smith, L. M. et al. Fluorescence detection in automated DNAsequence analysis. Nature 321, 674-679 (1986).

  11. The Mouse Genome Sequencing Consortium. Initial sequencing and comparative analysis of the mouse genome. Nature 420, 520-562 (2002).

  12. The Chipping Forecast II. Nature Genet. 32, 461-552 (2002).

  13. Guttmacher,A. E.& Collins, F. S. Genomic medicine — A primer. N. Engl. J. Med. 347, 1512-1520 (2002).

  14. National Research Council. Mapping and Sequencing the Human Genome (National Academy Press,Washington DC, 1988).

  15. US Department of Health and Human Services, US DOE. Understanding Our Genetic Inheritance. The US Human Genome Project: The First Five Years. NIH Publication No. 90-1590 (National Institutes of Health, Bethesda, MD, 1990).

  16. Collins, F. & Galas,D. A new five-year plan for the US Human Genome Project. Science 262, 43-46 (1993).

  17. Collins, F. S. et al.New goals for the US Human Genome Project: 1998-2003. Science 282, 682-689 (1998).

  18. Hilbert, D.Mathematical problems. Bull. Am. Math. Soc. 8, 437-479 (1902).

  19. Aparicio, S. et al.Whole-genome shotgun assembly and analysis of the genome of Fugu rubripes. Science 297, 1301-1310 (2002).

  20. Sidow, A. Sequence first.Ask questions later. Cell 111, 13-16 (2002).

  21. Zhang,M.Q.Computational prediction of eukaryotic proteincoding genes. Nature Rev. Genet. 3, 698-709 (2002).

  22. Banerjee, N. & Zhang,M.X. Functional genomics as applied to mapping transcription regulatory networks.Curr.Opin. Microbiol. 5, 313-317 (2002).

  23. Van der Weyden, L.,Adams,D. J. & Bradley, A. Tools for targeted manipulation of the mouse genome. Physiol.Genomics 11, 133-164 (2002).

  24. Hannon, G. J.RNA interference. Nature 418, 244-251 (2002).

  25. Stockwell, B. R. Chemical genetics: Ligand-based discovery of gene function. Nature Rev. Genet. 1, 116-125 (2000).

  26. Gavin, A. C. et al. Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 415, 141-147 (2002).

  27. Tyson, J. J.,Chen, K. & Novak, B.Network dynamics and cell physiology. Nature Rev.Mol. Cell Biol. 2, 908-916 (2001).

  28. Sachidanandam, R. et al. A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms. Nature 409, 928-933 (2001).

  29. Gabriel, S. B. et al. The structure of haplotype blocks in the human genome. Science 296, 2225-2229 (2002).

  30. Reich,D. E. & Lander, E. S. On the allelic spectrum of human disease. Trends Genet. 17, 502-510 (2001).

  31. Hirschhorn, J.N., Lohmueller, K., Byrne, E. & Hirschhorn,K.A comprehensive review of genetic association studies. Genet. Med. 4, 45-61 (2002).

  32. Wagner,K.R.Genetic diseases of muscle. Neurol. Clin. 20, 645-678 (2002).

  33. Golub, T.R.Genomic approaches to the pathogenesis of hematologic malignancy. Curr.Opin.Hematol. 8, 252-261 (2001).

  34. Drews, J.& Ryser, S. The role of innovation in drug development. Nature Biotechnol. 15, 1318-1319 (1997).

  35. Druker, B. J. Imatinib alone and in combination for chronic myeloid leukemia. Semin. Hematol. 40, 50-8 (2003).

  36. Selkoe,D. J.Alzheimer's disease: genes, proteins, and therapy. Physiol. Rev. 81, 741-66 (2001).

  37. Lynch, H. T. & de la Chapelle, A. Genomic medicine: hereditary colorectal cancer. N. Engl. J. Med. 348, 919-932 (2003).

  38. Gardner, M. J. et al. Genome sequence of the human malaria parasite Plasmodium falciparum. Nature 419, 498-511 (2002).

  39. Holt, R. A. et al. The genome sequence of the malaria mosquito Anopheles gambiae. Science 298, 129-149 (2002).

  40. Anderlik, M. R. & Rothstein, M.A. Privacy and confidentiality of genetic information: What rules for the new science? Annu. Rev. Genom.Hum.Genet. 2, 401-433 (2001).

  41. Hudson, K. L.,Rothenberg,K. H.,Andrews, L. B.,Kahn, M. J. E. & Collins, F. S. Genetic discrimination and health-insurance — An urgent need for reform. Science 270, 391-393 (1995).

  42. Rothenberg, K. et al. Genetic information and the workplace: Legislative approaches and policy challenges. Science 275, 1755-1757 (1997).

  43. Fuller, B. P. et al. Policy forum: Ethics — privacy in genetics research. Science 285, 1359-1361 (1999).

  44. Miller, P. S. Is there a pink slip in my genes? J.Health Care Law Policy 3, 225-265 (2000).

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