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Medical applications of microarray technologies: a regulatory science perspective

Nature Genetics volume 32, pages 474–479 (2002)Cite this article

Abstract

The potential medical applications of microarrays have generated much excitement, and some skepticism, within the biomedical community. Some researchers have suggested that within the decade microarrays will be routinely used in the selection, assessment, and quality control of the best drugs for pharmaceutical development, as well as for disease diagnosis and for monitoring desired and adverse outcomes of therapeutic interventions. Realizing this potential will be a challenge for the whole scientific community, as breakthroughs that show great promise at the bench often fail to meet the requirements of clinicians and regulatory scientists. The development of a cooperative framework among regulators, product sponsors, and technology experts will be essential for realizing the revolutionary promise that microarrays hold for drug development, regulatory science, medical practice and public health.

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References

  1. Warrington, J.A., Dee, S. & Trulson, M. Large-scale genomic analysis using Affymetrix GeneChipR probe arrays. in Microarray Biochip Technology (ed. Schena, M.) 119–148 (BioTechniques Books, Natick, MA, 2000).

    Google Scholar 

  2. Tefferi, A., Bolander, M.E., Ansell, S.M., Wieben, E.D. & Spelsberg, T.C. Primer on medical genomics part III: microarray experiments and data analysis. Mayo Clin. Proc. 77, 927–940 (2002).

    Article  CAS  Google Scholar 

  3. Chee, M. et al. Accessing genetic information with high-density DNA arrays. Science 274, 610–614 (1996).

    Article  CAS  Google Scholar 

  4. Cronin, M.T., Fucini, R.V., Kim, S.M., Masino, R.S., Wespi, R.M. & Miyada, C.G. Cystic fibrosis mutation detection by hybridization to light-generated DNA probe arrays. Hum. Mutat. 7, 244–255 (1996).

    Article  CAS  Google Scholar 

  5. Hacia, J.G., Brody, L.C., Chee, M.S., Fodor, S.P.A. & Collins, F.S. Detection of heterzygous mutations in BRCA1 using high density oligonucleotide arrays and two-colour fluorescence analysis. Nature Genet. 14, 444–447 (1996).

    Article  Google Scholar 

  6. Hacia, J.G. et al. Strategies for mutational analysis of the large multiexon ATM gene using high-density oligonucleotide arrays. Genome Res. 8, 1245–1258 (1998).

    Article  CAS  Google Scholar 

  7. Favis, R. & Barany, F. Mutation detection in K-ras, BRCA1, BRCA2, and p53 using PCR/LDR and a universal DNA microarray. Ann. NY Acad. Sci. 906, 39–43 (2000).

    Article  CAS  Google Scholar 

  8. Mei, R. et al. Genome-wide detection of allelic imbalance using human SNPs and high-density DNA arrays. Genome Res. 10, 1126–1137 (2000).

    Article  CAS  Google Scholar 

  9. Cutler, D.J. et al. High-throughput variation detection and genotyping using microarrays. Genome Res. 11, 1913–1925 (2001).

    Article  CAS  Google Scholar 

  10. Raitio, M. et al. Y-chromosomal SNPs in Finn-Ugric-speaking populations analyzed by minisequencing on microarrays. Genome Res. 11, 471–482 (2001).

    Article  CAS  Google Scholar 

  11. Schena, M., Shalon, D., Davis, R.W. & Brown, P.O. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270, 467–470 (1995).

    Article  CAS  Google Scholar 

  12. Lockhart, D.J. et al. Expression monitoring by hybridization to high-density oligonucleotide arrays. Nature Biotech. 14, 1675–1680 (1996).

    Article  CAS  Google Scholar 

  13. Shalon, D., Smith, S.J. & Brown, P.O. A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization. Genome Res. 6, 639–345 (1996).

    Article  CAS  Google Scholar 

  14. Brown, P.O. & Botstein, D. Exploring the new world of the genome with DNA microarrays. Nature Genet. 21 (1 Suppl), 33–37 (1999).

    Article  CAS  Google Scholar 

  15. DeRisi, J. et al. Use of a cDNA microarray to analyse gene expression patterns in human cancer. Nature Genet. 14, 457–460 (1996).

    Article  CAS  Google Scholar 

  16. Golub, T.R. et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999).

    Article  CAS  Google Scholar 

  17. Bubendorf, L. et al. Hormone therapy failure in human prostate cancer: analysis by complementary DNA and tissue microarrays. J. Natl. Cancer Inst. 91, 1758–1764 (1999).

    Article  CAS  Google Scholar 

  18. Alizadeh, A.A. et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403, 503–511 (2000).

    Article  CAS  Google Scholar 

  19. Stamey, T.A. et al. Molecular genetic profiling of Gleason grade 4/5 prostate cancers compared to benign prostatic hyperplasia. J. Urology 166, 2171–2177 (2001).

    Article  CAS  Google Scholar 

  20. Luo, J. et al. Human prostate cancer and benign prostatic hyperplasia: molecular dissection by gene expression profiling. Cancer Research 61, 4683–4688 (2001).

    CAS  Google Scholar 

  21. West, M. et al. Predicting the clinical status of human breast cancer by using gene expression profiles. Proc. Natl. Acad. Sci. USA 98, 11462–11467 (2001).

    Article  CAS  Google Scholar 

  22. Hedenfalk, I. et al. Gene expression profiles in hereditary breast cancer. N. Engl. J. Med. 244, 539–548 (2001).

    Article  Google Scholar 

  23. Shirota, Y., Kaneko, S., Honda, M., Hawai, H.F. & Kobayashi, K. Identification of differentially expressed genes in hepatocellular carcinoma with cDNA microarrays. Heptatology 33, 832–840 (2001).

    Article  CAS  Google Scholar 

  24. Okabe, H. et al. Genome-wide analysis of gene expression in human hepatocellular carcinomas using cDNA microarray: identification of genes involved in viral carcinogenesis and tumor progression. Cancer Res. 61, 2129–2137 (2001).

    CAS  Google Scholar 

  25. van't Veer, L.J. et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530–536 (2002).

    Article  CAS  Google Scholar 

  26. Rosenwald, A. et al. The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N. Engl. J. Med. 346, 1937–1947 (2002).

    Article  Google Scholar 

  27. Shipp, M. et al. Diffuse large B-cell lymphoma outcome prediction by gene expression profiling and supervised machine learning. Nature Med. 8, 68–74 (2002).

    Article  CAS  Google Scholar 

  28. Bittner, M. et al. Molecular classification of cutaneous malignant melanoma by gene expression profiling. Nature 406, 536–540 (2000).

    Article  CAS  Google Scholar 

  29. Pomeroy, S.L. et al. Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature 415, 436–442 (2002).

    Article  CAS  Google Scholar 

  30. Armstrong, S.A. et al. MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia. Nature Genet. 30, 41–47 (2002).

    Article  CAS  Google Scholar 

  31. Alevizos, I. et al. Oral cancer in vivo gene expression profiling assisted by laser capture microdissection and microarray analysis. Oncogene 20, 6196–6204 (2001).

    Article  CAS  Google Scholar 

  32. Welsch, P.L. et al. BRCA1 transcriptionally regulates genes involved in breast tumorigenesis. Proc. Natl Acad. Sci. USA 99, 7560–7565 (2002).

    Article  Google Scholar 

  33. Sreekumar, A. et al. Profiling of cancer cells using protein microarrays: discovery of novel radiation-regulated proteins. Cancer Res. 61, 7585–7593 (2001).

    CAS  Google Scholar 

  34. MacBeath, G. Proteomics comes to the surface. Nature Biotech. 19, 828–829 (2001).

    Article  CAS  Google Scholar 

  35. Walter, G., Bussow, K., Lueking, A. & Glokler, J. High-throughput protein arrays: prospects for molecular diagnostics. Trends Mol. Med. 8, 250–253 (2002).

    Article  CAS  Google Scholar 

  36. Kuruvilla, F.G., Shamji, A.F., Sternson, S.M., Hergenrother, P.J. & Schreiber, S.L. Dissecting glucose signalling with diversity-oriented synthesis and small-molecule microarrays. Nature 416, 653–657 (2002).

    Article  CAS  Google Scholar 

  37. Paweletz, C.P. et al. Reverse phase protein microarrays which capture disease progression show activation of pro-survival pathways at the cancer invasion front. Oncogene 20, 1981–1989 (2001).

    Article  CAS  Google Scholar 

  38. Torhorst, J. et al. Tissue microarrays for rapid linking of molecular changes to clinical endpoints. Am. J. Pathol. 159, 2249–2256 (2001).

    Article  CAS  Google Scholar 

  39. Figeys, D. & Pinto, D. Proteomics on a chip: promising developments. Electrophoresis 22, 208–216 (2001).

    Article  CAS  Google Scholar 

  40. Petricoin, E.F., Zoon, K.C., Kohn, E.C., Barrett, J.C. & Liotta, L.A. Clinical proteomics: translating benchside practice into bedside reality. Nature Rev. Drug Disc. 1, 663–686 (2002).

    Article  Google Scholar 

  41. Eisen, M.B., Spellman, P.T., Brown, P.O. & Botstein, D. Cluster analysis and display of genome-wide expression patterns. Proc. Natl Acad. Sci. USA 95, 14863–14868 (1998).

    Article  CAS  Google Scholar 

  42. Khan, J. et al. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature Med. 7, 673–679 (2001).

    Article  CAS  Google Scholar 

  43. Petricoin, E.F. et al. Use of proteomic patterns in serum to identify ovarian cancer. Lancet 359, 572–577 (2002).

    Article  CAS  Google Scholar 

  44. Roses, A.D. Genome-based pharmacogenetics and the pharmaceutical industry. Nature Rev. Drug Disc. 1, 541–548 (2002).

    Article  CAS  Google Scholar 

  45. Seungchan, K. et al. Multivariate measurement of gene expression relationships. Genomics 67, 201–209 (2000).

    Article  Google Scholar 

  46. Altman, R.B. & Klein, T.E. Challenges for biomedical informatics and pharmacogenomics. Ann. Rev. Pharm. Toxicol. 42, 113–133 (2002).

    Article  CAS  Google Scholar 

  47. Lee, M.-L.T., Kuo, F.C., Whitmore, G.A. & Sklar, J. Importance of replication in microarray gene expression studies: statistical methods and evidence from repetitive cDNA hybridizations. Proc. Natl Acad. Sci. USA 97, 9834–9839 (2000).

    Article  CAS  Google Scholar 

  48. Tseng, G.C., Oh, M.-K., Rohlin, L., Liao, J.C. & Wong, W.H. Issues in cDNA microarray analysis: quality filtering, channel normailization, models of variations and assessment of gene effects. Nucleic Acids Res. 29, 2549–2557 (2001).

    Article  CAS  Google Scholar 

  49. Mills, J.C. & Gordon, J.I. A new approach for filtering noise from high-density oligonucleotide microarray datasets. Nucleic Acids Res. 29, e72 (2001).

    Article  CAS  Google Scholar 

  50. Bakay, M., Chen, Y.-W., Borup, R., Zhao, P., Nagaraju, K. & Hoffman, E.P. Sources of variability and effect of experimental approach on expression profiling data interpretation. BMC Bioinformatics 3, 4 (2002).

    Article  Google Scholar 

  51. King, H.C. & Sinha, A.A. Gene expression profile analysis by DNA microarrays: promise and pitfalls. JAMA 286, 2280–2288 (2001).

    Article  CAS  Google Scholar 

  52. Nuwaysir, E.F., Bittner, M., Trent, J., Barrett, J.C. & Afshari, C.A. Microarrays and toxicology: the advent of toxicogenomics. Mol. Carcinogenesis 24, 153–159 (1999).

    Article  CAS  Google Scholar 

  53. Waring, J.F. et al. Clustering of hepatotoxins based on mechanism of toxicity using gene expression profiles. Nature Genet. 29, 365–371 (2001).

    Article  Google Scholar 

  54. Hamadeh, H.K. et al. Gene expression analysis reveals chemical-specific profiles. Toxicol. Sci. 67, 219–231 (2002).

    Article  CAS  Google Scholar 

  55. Thomas, R.S. et al. Identification of toxicologically predictive gene sets using cDNA microarrays. Mol. Pharmacol. 60, 1189–1194 (2001).

    Article  CAS  Google Scholar 

  56. Huang, Q. et al. Assessment of cisplatin-induced nephrotoxicity by microarray technology. Toxicol. Sci 63, 196–207 (2001).

    Article  CAS  Google Scholar 

  57. Brown, P.O. & Hartwell, L. Genomics and human disease—variations on variation. Nature Genet. 18, 91–93 (1998).

    Article  CAS  Google Scholar 

  58. He, B., Munson, A.E. & Meade, B.J. Analysis of gene expression induced by irritant and sensitizing chemicals using oligonucleotide arrays. Internat. Immunopharm. 1, 867–879 (2001).

    Article  CAS  Google Scholar 

  59. Ramakrishnan, R. et al. An assessment of Motorola CodeLink™ microarray performance for gene expression profiling applications. Nucleic Acids Res. 30, e30 (2002).

    Article  Google Scholar 

  60. Modrek, B. & Lee, C. A genomic view of alternative splicing. Nature Genet. 30, 13–19 (2002).

    Article  CAS  Google Scholar 

  61. Brem, R.B., Yuert, G., Clinton, R. & Kruglyak, L. Genetic dissection of transcriptional regulation in budding yeast. Science 296, 752–755 (2002).

    Article  CAS  Google Scholar 

  62. Cantor, C.R. Pharmacogenetics becomes pharmacogenomics: wake up and get ready. Mol. Diagnostics 4, 287–288 (1999).

    CAS  Google Scholar 

  63. Hamadeh, H.K., Bushel, P., Tucker, C.J., Martin, K., Paules, R. & Afshari, C.A. Detection of diluted gene expression alterations using cDNA arrays. Biotechniques 32, 322–329 (2002).

    CAS  Google Scholar 

  64. Stoeckert, C. et al. Microarray databases standards and ontologies. Nature Genet. 32, 469–473 (2002).

    Article  CAS  Google Scholar 

  65. Brazma, A. et al. Minimal information about a microarray experiment (MIAME)—toward standards for microarray data. Nature Genet. 29, 365–371 (2001).

    Article  CAS  Google Scholar 

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Author information

Authors and Affiliations

  1. Division of Therapeutic Products, Office of Therapeutics Research and Review, Center for Biologics Evaluation and Research, FDA, Bethesda, 20892, Maryland, USA

    Emanuel F. Petricoin III

  2. Division of Clinical Laboratory Devices, Office of Device Evaluation, Center for Devices and Radiological Health, Rockville, 20850, Maryland, USA

    Joseph L. Hackett

  3. Office of Clinical Pharmacology and Biopharmaceutics, Center for Drug Evaluation and Research, FDA, Rockville, 20857, Maryland, USA

    Lawrence J. Lesko

  4. Division of Cell and Gene Therapy, Office of Therapeutics Research and Review, Center for Biologics Evaluation and Research, FDA, Bethesda, 20892, Maryland, USA

    Raj K. Puri

  5. Division of Clinical Laboratory Devices, Office of Device Evaluation, Center for Devices and Radiological Health, FDA, Rockville, 20850, Maryland, USA

    Steven I. Gutman

  6. Division of Viral Products, Office of Vaccines Research and Review, Center for Biologics Evaluation and Research, FDA, Bethesda, 20892, Maryland, USA

    Konstantin Chumakov

  7. Office of the Director, Center for Drug Evaluation and Research, FDA, Rockville, 20852, Maryland, USA

    Janet Woodcock

  8. Office of the Director, Center for Devices and Radiological Health, FDA, Rockville, 20850, Maryland, USA

    David W. Feigal Jr.

  9. Office of the Director, Center for Biologics Evaluation and Research, FDA, Bethesda, 20892, Maryland, USA

    Kathryn C. Zoon

  10. Division of Applied Pharmacology Research, Office of Testing and Research, Center for Drug Evaluation and Research, FDA, Laurel, 20708, Maryland, USA

    Frank D. Sistare

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  1. Emanuel F. Petricoin III
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Correspondence to Emanuel F. Petricoin III or Frank D. Sistare.

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Petricoin, E., Hackett, J., Lesko, L. et al. Medical applications of microarray technologies: a regulatory science perspective. Nat Genet 32 (Suppl 4), 474–479 (2002). https://doi.org/10.1038/ng1029

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