Advertisement

Developing and Evaluating Genomics- or Proteomics-Based Diagnostic Tests

Statistical Perspectives
  • Xuejun Peng
Part of the Methods in Molecular Medicine book series (MIMM, volume 129)

Abstract

The completion of the Human Genome Project and the ongoing sequencing of mouse, rat, and other genomes have led to an explosion of genetics-related technologies that are finding their way into all areas of biological research in both basic sciences and clinical applications. High-throughput genomics and proteomics technology has been quickly adapted to develop tools for clinical and pharmacological applications. Because molecular alterations usually occur much earlier than histological, physiological, and clinical abnormality, researchers hope to extend the applications of genomics and/or proteomics technology to early diagnosis of diseases and clinical outcome prognosis. Recently, some successful attempts in molecular diagnosis or prognosis have been published. However, for such tests to be translated from the bench to the bed, they must meet some rigorous standards. To develop a clinically meaningful genomics-based diagnostic test, we must have good study design, appropriate statistical analyses, and valid assessment of its clinical efficacy. In this chapter, we discuss statistical considerations on the process of developing reliable and useful genomics- or proteomics-based tests.

Key Words

Genomics-based diagnostic test proteomics-based diagnostic test molecular diagnosis medical genomics genomic medicine 

References

  1. 1.
    Collins, F. S., Green, E. D., Guttmacher, A. E., and Guyer, M. S. (2003) A vision for the future of genomics research. Nature 422, 835–847.CrossRefPubMedGoogle Scholar
  2. 2.
    Golub, T. R., Slonim, D. K., Tamayo, P., et al. (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537.CrossRefPubMedGoogle Scholar
  3. 3.
    Alizadeh, A. A., Eisen, M. B., Davis, R. E., et al. (2000) Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403, 503–511.CrossRefPubMedGoogle Scholar
  4. 4.
    Bittner, M., Meltzer, P., Chen, Y., et al. Molecular classification of cutaneous malignant melanoma by gene expression profiling. Nature 406, 536–540.Google Scholar
  5. 5.
    Perou, C. M., Sorlie, T., Eisen, M. B., et al. (2000) Molecular portraits of human breast tumors. Nature 406, 747–752.CrossRefPubMedGoogle Scholar
  6. 6.
    You, S. A., Archacki, S. R., Angheloiu, G., et al. (2003) Proteomic approach to coronary atherosclerosis shows ferritin light chain as a significant marker, evidence consistent with iron hypothesis in atherosclerosis. Physiol. Genomics 13, 25–30.PubMedGoogle Scholar
  7. 7.
    Bullinger, L., Dohner, K., Bair, E., et al. (2004) Use of gene-expression profiling to identify prognostic subclasses in adult acute myeloid leukemia. N. Engl. J. Med. 350, 1605–1616.CrossRefPubMedGoogle Scholar
  8. 8.
    Dave, S. S., Wright, G., Tan, B., et al. (2004) Prediction of survival in follicular lymphoma based on molecular features of tumor-infiltrating immune cells. N. Engl. J. Med. 351, 2159–2169.CrossRefPubMedGoogle Scholar
  9. 9.
    Holleman, A., Cheok, M. H., den Boer, M. L., et al. (2004) Gene-expression patterns in drug-resistant acute lymphoblastic leukemia cells and response to treatment. N. Engl. J. Med. 351, 533–542.CrossRefPubMedGoogle Scholar
  10. 10.
    Lossos, I. S., Czerwinski, D. K., Alizadeh, A. A., et al. (2004) Prediction of survival in diffuse large-B-cell lymphoma based on the expression of six genes. N. Engl. J. Med. 350, 1828–1837.CrossRefPubMedGoogle Scholar
  11. 11.
    Rassenti, L. Z., Huynh, L., Toy, T. L., et al. (2004) ZAP-70 Compared with immunoglobulin heavy-chain gene mutation status as a predictor of disease progression in chronic lymphocytic leukemia. N. Engl. J. Med. 351, 893–901.CrossRefPubMedGoogle Scholar
  12. 12.
    Sarwal, M., Chua, M. S., Kambham, N., et al. (2003) Molecular heterogeneity in acute renal allograft rejection identified by DNA microarray profiling. N. Engl. J. Med. 349, 125–138.CrossRefPubMedGoogle Scholar
  13. 13.
    Chang, J. C., Wooten, E. C., Tsimelzon, A., et al. (2003) Gene expression profiling for the prediction of therapeutic response to docetaxel in patients with breast cancer. Lancet 362, 362–369.CrossRefPubMedGoogle Scholar
  14. 14.
    Bueno, R., Loughlin, K. R., Powell, M. H., and Gordon, G. J. (2003) A diagnostic test for prostate cancer from gene expression profiling data. J. Urol. 171, 903–906.CrossRefGoogle Scholar
  15. 15.
    Guttmacher, A. E. and Collins, F. S. (2002) Genomic medicine—a primer. N. Engl. J. Med. 347, 1512–1520.CrossRefPubMedGoogle Scholar
  16. 16.
    Burke, W. (2002) Genomic medicine: genetic testing. N. Engl. J. Med. 347, 1867–1875.CrossRefPubMedGoogle Scholar
  17. 17.
    Khoury, M. J., McCabe, L. L., and McCabe, E. R. B. (2003) Genomic medicine: population screening in the age of genomic medicine. N. Engl. J. Med. 348, 50–58.CrossRefPubMedGoogle Scholar
  18. 18.
    Goldstein, D. B. (2003) Pharmacogenetics in the laboratory and the clinic. N. Engl. J. Med. 348, 553–556.CrossRefPubMedGoogle Scholar
  19. 19.
    Evans, W. E. and McLeod, H. L. (2003) Drug therapy: pharmacogenomics—drug disposition, drug targets, and side effects. N. Engl. J. Med. 348, 538–549.CrossRefPubMedGoogle Scholar
  20. 20.
    Weinshilboum, R. (2003) Genomic medicine: inheritance and drug response. N. Engl. J. Med 348, 529–537.CrossRefPubMedGoogle Scholar
  21. 21.
    Lynch, H. T. and de la Chapelle, A. (2003) Genomic medicine: hereditary colorectal cancer. N. Engl. J. Med. 348, 919–932.CrossRefPubMedGoogle Scholar
  22. 22.
    Nussbaum, R. L. and Ellis, C. E. (2003) Genomic medicine: Alzheimer’s disease and Parkinson’s disease. N. Engl. J. Med. 348, 1356–1364.CrossRefPubMedGoogle Scholar
  23. 23.
    Staudt, L. M. (2003) Genomic medicine: molecular diagnosis of the hematologic cancers. N. Engl. J. Med. 348, 1777–1785.CrossRefPubMedGoogle Scholar
  24. 24.
    Wooster, R. and Weber, B. L. (2003) Genomic medicine: breast and ovarian cancer. N. Engl. J. Med. 348, 2339–2347.CrossRefPubMedGoogle Scholar
  25. 25.
    Nabel, E. G. (2003) Genomic medicine: cardiovascular disease. N. Engl. J. Med. 349, 60–72.CrossRefPubMedGoogle Scholar
  26. 26.
    Burke, W. (2003) Genomic medicine: genomics as a probe for disease biology. N. Engl. J. Med. 349, 969–974.CrossRefPubMedGoogle Scholar
  27. 27.
    Guttmacher, A. E., Collins, F. S., and Carmona, R. H. (2004) The family history—more important than ever. N. Engl. J. Med. 351, 2333–2336.CrossRefPubMedGoogle Scholar
  28. 28.
    Ansell, S. M., Ackerman, M. J., Black, J. L., Roberts, L. R., and Tefferi, A. (2003) A Primer on medical genomics. Part VI, Genomics and molecular genetics in clinical practice. Mayo Clin. Proc. 78, 307–317.CrossRefPubMedGoogle Scholar
  29. 29.
    Sackett, D. L., Rosenberg, W. M., Gray, J. A., Haynes, R. B., and Richardson, W. S. (1996) Evidence-based medicine, what it is and what it isn’t. Br. Med. J. 312, 71, 72.Google Scholar
  30. 30.
    Sox, Jr., H. C., Blatt, M. A., Higgins, M. C., and Marton, K. I. (1989) Med. Decis. Making. Butterworths-Heinemann, Boston, MA.Google Scholar
  31. 31.
    McNeil, B. J. and Adelstein, S. J. (1976) Determining the value of diagnostic and screening tests. J. Nucl. Med. 17, 439–448.PubMedGoogle Scholar
  32. 32.
    Zhou, X. H., Obuchowski, N. A., and McClish, D. K. (2002) Statistical Methods in Diagnostic Medicine. John Wiley and Sons, New York, NY.CrossRefGoogle Scholar
  33. 33.
    Metz, C. E. (1989) Some practical issues of experimental design and data analysis in radiological ROC studies. Invest. Radiol. 24, 234–245.CrossRefPubMedGoogle Scholar
  34. 34.
    Hastie, T., Tibshirani, R., and Friedman, J. (2001) The Elements of Statistical Learning, Data Mining, Inference, and Prediction. Springer-Verlag, New York, NY.Google Scholar
  35. 35.
    Fryback, D. G. and Thornbury, J. R. (1991) The efficacy of diagnostic imaging. Med. Decis. Making 11, 88–94.CrossRefPubMedGoogle Scholar
  36. 36.
    Tan, P. K., Downey, T. J., Spitznagel, Jr., E. L., et al. (2003) Evaluation of gene expression measurements from commercial microarray platforms. Nucleic Acids Res. 31, 5676–5684.CrossRefPubMedGoogle Scholar
  37. 37.
    Woo, Y., Affourtit, J., Daigle, S., et al. (2004) A comparison of cDNA, oligonucleotide, and Affymetrix GeneChip gene expression microarray platforms. J. Biomol. Tech. 15, 276–284.PubMedGoogle Scholar
  38. 38.
    Bammler, T., Beyer, R. P., Bhattacharya, S., et al. (2005) Standardizing global gene expression analysis between laboratories and across platforms. Nat. Methods 2, 351–356.CrossRefPubMedGoogle Scholar
  39. 39.
    Larkin, J. E., Frank, B. C., Gavras, H., Sultana, R., and Quackenbush, J. (2005) Independence and reproducibility across microarray platforms. Nat. Methods 2, 337–344.CrossRefPubMedGoogle Scholar
  40. 40.
    Irizarry, R. A., Warren, D., Spencer, F., et al. (2005) Multiple-laboratory comparison of microarray platforms. Nat. Methods 2, 345–350.CrossRefPubMedGoogle Scholar

Copyright information

© Humana Press Inc. 2006

Authors and Affiliations

  • Xuejun Peng
    • 1
  1. 1.Department of Quantitative Health SciencesThe Cleveland Clinic FoundationCleveland

Personalised recommendations