Current Breast Cancer Reports

, Volume 1, Issue 4, pp 216–221 | Cite as

Advances in clinical trial designs for predictive biomarker discovery and validation

  • Richard Simon


Cancers of the same primary site are in many cases heterogeneous in molecular pathogenesis, clinical course, and treatment responsiveness. Current approaches for treatment development, evaluation, and use result in treatment of many patients with ineffective drugs and lead to the conduct of large clinical trials to identify small, average treatment benefits for heterogeneous groups of patients. New genomic and proteomic technologies provide powerful tools for the identification of patients who require systemic or aggressive treatment and the selection of those likely or unlikely to benefit from a specific regimen. In spite of the large literature on developing prognostic and predictive biomarkers and on statistical methodology for analysis of high dimensional data, there is considerable uncertainty about proper approaches for the validation of biomarker-based diagnostic tests. This article attempts to clarify these issues and provide a guide to recent publications on the design of clinical trials for evaluating the clinical utility and robustness of prognostic and predictive biomarkers.


Trastuzumab Clinical Trial Design Predictive Index Diagonal Linear Discriminant Analysis Enrichment Design 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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References and Recommended Reading

  1. 1.
    Pusztai L: Perspectives and challenges of clinical pharmacogenomics in cancer. Pharmacogenomics 2004, 5:451–454.CrossRefPubMedGoogle Scholar
  2. 2.
    Hayes DF: Prognostic and predictive factors revisited. Breast 2005, 14:493–499.CrossRefPubMedGoogle Scholar
  3. 3.
    Gennari A, Sormani MP, Pronzato P, et al.: HER2 status and efficacy of adjuvant anthracyclines in early breast cancer: a pooled analysis of randomized clinical trials. J Natl Cancer Inst 2008, 100:14–20.PubMedCrossRefGoogle Scholar
  4. 4.
    Hayes DF, Thor AD, Dressler LG, et al.: HER2 and response to paclitaxel in node-positive breast cancer. N Engl J Med 2007, 357:1496–1506.CrossRefPubMedGoogle Scholar
  5. 5.
    Amado RG, Wolf M, Peeters M, et al.: Wild-type KRAS is required for panitumumab efficacy in patients with metastatic colorectal cancer. J Clin Oncol 2008, 26:1626–1634.CrossRefPubMedGoogle Scholar
  6. 6.
    Sawyers CL: The cancer biomarker problem. Nature 2008, 452:548–552.CrossRefPubMedGoogle Scholar
  7. 7.
    van’t-Veer LJ, Paik S, Hayes DF: Gene expression profiling of breast cancer: a new tumor marker. J Clin Oncol 2005, 23:1631–1635.CrossRefPubMedGoogle Scholar
  8. 8.
    Dudoit S, Fridlyand J, Speed TP: Comparison of discrimination methods for the classification of tumors using gene expression data. J Am Stat Assoc 2002, 97:77–87.CrossRefGoogle Scholar
  9. 9.
    Dupuy A, Simon R: Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. J Natl Cancer Inst 2007, 99:147–157.CrossRefPubMedGoogle Scholar
  10. 10.
    Potti A, Dressman HK, Bild A, et al.: Genomic signatures to guide the use of chemotherapeutics. Nat Med 2006, 12:1294–1300.CrossRefPubMedGoogle Scholar
  11. 11.
    Bennefoi H, Potti A, Delorenzi M, et al.: Validation of gene signatures that predict the response of breast cancer to neoadjuvant chemotherapy: a substudy of the EORTC 10994/BIG00-01 clinical trial. Lancet Oncol 2007, 8:1071–1078.CrossRefGoogle Scholar
  12. 12.
    Coombes KR, Wang J, Baggerly KA: Microarrays: retracing steps. Nat Med 2007, 13:1276–1277.CrossRefPubMedGoogle Scholar
  13. 13.
    Baggerly K, Coombes K, Neeley E: Run batch effects potentially compromise the usefulness of genomic signatures for ovarian cancer. J Clin Oncol 2008, 26:1186–1187.CrossRefPubMedGoogle Scholar
  14. 14.
    Smollen G, Sordella R, Muir B, et al.: Amplification of MET may identify a subset of cancers with extreme sensitivity to the selective tyrosine kinase inhibitor PHA-665752. Proc Natl Acad Sci U S A 2006, 103:2316–2321.CrossRefGoogle Scholar
  15. 15.
    van’t-Veer L, Bernards R: Enabling personalized cancer medicine through analysis of gene-expression patterns. Nature 2008, 452:564–570.CrossRefPubMedGoogle Scholar
  16. 16.
    Pusztai L, Anderson K, Hess KR: Pharmacogenomic predictor discovery in phase II clinical trials for breast cancer. Clin Cancer Res 2007, 13:6080–6086.CrossRefPubMedGoogle Scholar
  17. 17.
    Dobbin K, Simon R: Sample size planning for developing classifiers using high dimensional DNA expression data. Biostatistics 2007, 8:101–117.CrossRefPubMedGoogle Scholar
  18. 18.
    Dobbin KK, Zhao Y, Simon RM: How large a training set is needed to develop a classifier for microarray data? Clin Cancer Res 2008, 14:108–114.CrossRefPubMedGoogle Scholar
  19. 19.
    Simon R: Randomized clinical trials: principles and obstacles. Cancer 1994, 74:2614–2619.CrossRefPubMedGoogle Scholar
  20. 20.
    Jorgensen JT: From blockbuster medicine to personalized medicine. Personalized Med 2008, 5:55–63.CrossRefGoogle Scholar
  21. 21.
    Pusztai L, Hess KR: Clinical trial design for microarray predictive marker discovery and assessment. Ann Oncol 2004, 15:1731–1737.CrossRefPubMedGoogle Scholar
  22. 22.
    Sargent D, Allegra C: Issues in clinical trial design for tumor marker studies. Semin Oncol 2002, 3:222–230.CrossRefGoogle Scholar
  23. 23.
    Simon R, Wang SJ: Use of genomic signatures in therapeutics development. Pharmacogenomics J 2006, 6:1667–1673.CrossRefGoogle Scholar
  24. 24.
    Bogaerts J, Cardoso F, Buyse M, et al.: Gene signature evaluation as a prognostic tool: challenges in the design of the MINDACT trial. Nat Clin Practice: Oncol 2006, 3:540–551.CrossRefGoogle Scholar
  25. 25.
    Pusztai L, Broglio K, Andre F, et al.: Effect of molecular disease subsets on disease-free survival in randomized adjuvant chemotherapy trials for estrogen-receptor positive breast cancer. J Clin Oncol 2008, 26:4679–4683.CrossRefPubMedGoogle Scholar
  26. 26.
    Simon R, Maitournam A: Evaluating the efficiency of targeted designs for randomized clinical trials. Clin Cancer Res 2005, 10:6759–6763.CrossRefGoogle Scholar
  27. 27.
    Simon R, Maitournam A: Evaluating the efficiency of targeted designs for randomized clinical trials: supplement and correction. Clin Cancer Res 2006, 12:3229.CrossRefGoogle Scholar
  28. 28.
    Maitournam A, Simon R: On the efficiency of targeted clinical trials. Stat Med 2005, 24:329–339.CrossRefPubMedGoogle Scholar
  29. 29.
    Biometric Research Branch: Division of Cancer Treatment and Diagnosis. Available at Accessed July 17, 2009.
  30. 30.
    Simon R: Using genomics in clinical trial design. Clin Cancer Res 2008, 14:5984–5993.CrossRefPubMedGoogle Scholar
  31. 31.
    Simon R: Designs and adaptive analysis plans for pivotal clinical trials of therapeutics and companion diagnostics. Expert Rev Mol Diag 2008, 2:721–729.CrossRefGoogle Scholar
  32. 32.
    Wang SJ, O’Neill RT, Hung HM: Approaches to evaluation of treatment effect in randomized clinical trials with genomic subset. Pharm Stat 2007, 6:227–244.CrossRefPubMedGoogle Scholar
  33. 33.
    Liu A, Li Q, Yu KF, Yuan VW: A threshold sample-enrichment approach in a clinical trial with heterogeneous subpopulations. Stat Med 2009 (in press).Google Scholar
  34. 34.
    Jiang W, Freidlin B, Simon R: Biomarker adaptive threshold design: a procedure for evaluating treatment with possible biomarker-defined subset effect. J Natl Cancer Inst 2007, 99:1036–1043.CrossRefPubMedGoogle Scholar
  35. 35.
    Freidlin B, Simon R: Adaptive signature design: an adaptive clinical trial design for generating and prospectively testing a gene expression signature for sensitive patients. Clin Cancer Res 2005, 11:7872–7878.CrossRefPubMedGoogle Scholar
  36. 36.
    Song Y, Chi GY: A method for testing a prespecified subgroup in clinical trials. Stat Med 2007, 26:3535–3549.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Biometric Research BranchNational Cancer InstituteBethesdaUSA

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