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Guidelines for the Design of Clinical Studies for the Development and Validation of Therapeutically Relevant Biomarkers and Biomarker-Based Classification Systems

  • Richard M. Simon
Part of the Cancer Drug Discovery and Development book series (CDD&D)

Summary

Standards for the development of therapeutically relevant biomarkers and biomarker-based classification systems are lacking. The literature of prognostic marker studies for breast cancer is inconsistent, and few such markers have been adopted for widespread use in clinical practice. This is problematic, as many patients are over-treated and many others are treated ineffectively. The deficiencies in clinical development of biomarkers may become more severe as DNA microarrays and proteomic technologies provide many new candidate markers and therapeutics become more molecularly targeted. In this chapter we address some common problems with developmental marker studies and provide recommendations for the design of clinical studies for the development and validation of robust, reproducible, and therapeutically relevant biomarkers and biomarker-based classification systems. The design of validation studies is addressed for (1) identifying node-negative breast cancer patients who do not require systemic chemotherapy; (2) identifying node-positive breast cancer patients who do not benefit from standard chemotherapy; and (3) identifying node-positive breast cancer patients who benefit from a new molecularly targeted therapeutic.

Key Words

Biomarkers microarrays classification systems clinical trial design 

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References

  1. 1.
    Hilsenbeck SG, Clark GM, McGuire WL. Why do so many prognostic factors fail to pan out? Breast Cancer Res Treat 1992;22:197–206.PubMedCrossRefGoogle Scholar
  2. 2.
    Simon RM, Korn EL, McShane LM, Radmacher MD, Wright GW, Zhao Y. Design and analysis of DNA microarray investigations. Springer, New Yok, 2003.Google Scholar
  3. 3.
    Reiner A, Yekutieli D, Benjamini Y. Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics 2003;19:368–375.PubMedCrossRefGoogle Scholar
  4. 4.
    Kattan MW. Judging new markers by their ability to improve predictive accuracy. J Natl Cancer Inst 2003;95:634–635.PubMedGoogle Scholar
  5. 5.
    Korn EL, Simon R. Measures of explained variation for survival data. Stat Med 1990;9:487–504.PubMedCrossRefGoogle Scholar
  6. 6.
    Simon R, Radmacher MD, Dobbin K, McShane LM. Pitfalls in the analysis of DNA microarray data: class prediction methods. J Natl Cancer Inst 2003;95:14–18.PubMedCrossRefGoogle Scholar
  7. 7.
    Rosenwald A, Wright G, Chan WC, et al. The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N Engl J Med 2002;346:1937–1947.PubMedCrossRefGoogle Scholar
  8. 8.
    Radmacher MD, McShane LM, Simon R. A paradigm for class prediction using gene expression profiles. J Comput Biol 2002;9:505–511.PubMedCrossRefGoogle Scholar
  9. 9.
    Vasselli J, Shih JH, Iyengar SR, et al. Predicting survival in patients with meta-static kidney cancer by gene expression profiling in the primary tumor. Proc Natl Acad Sci USA 2003;100:6958–6963.PubMedCrossRefGoogle Scholar
  10. 10.
    Simon R, Altman DG. Statistical aspects of prognostic factor studies in oncology. Br J Cancer 1994;69:979–985.PubMedGoogle Scholar
  11. 11.
    Gasparini G, Pozza F, Harris AL. Evaluating the potential usefulness of new prognostic and predictive indicators in node-negative breast cancer patients. J Natl Cancer Inst 1993;85:1206–1219.PubMedCrossRefGoogle Scholar

Copyright information

© Humana Press Inc., Totowa, NJ 2006

Authors and Affiliations

  • Richard M. Simon
    • 1
  1. 1.Biometric Research BranchNational Cancer InstituteBethesdaUSA

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