American Journal of Pharmacogenomics

, Volume 5, Issue 5, pp 317–325 | Cite as

Clinical Trial Designs for Prospective Validation of Biomarkers

  • Sumithra J. Mandrekar
  • Axel Grothey
  • Matthew P. Goetz
  • Daniel J. Sargent


Traditionally, anatomic staging systems have been used to determine predictions of individual patient outcome and, to a lesser extent, guide the choice of treatment in patients with cancer. With new targeted therapies, the role of biomarkers is increasingly promising, suggesting an integrated approach using the genetic make-up of the tumor and the genotype of the patient for treatment selection and patient management. Specifically, biomarkers can aid in patient stratification (risk assessment), treatment response identification (surrogate markers), or in differential diagnosis (identifying individuals who are likely to respond to specific drugs). To be clinically useful, a marker must favorably affect clinical outcomes such as decreased toxicity, increased overall and/or disease-free survival, or improved quality of life.

This paper focuses on possible clinical trial designs for assessing the utility of a predictive marker(s) for toxicity or clinical efficacy. We consider the scenario of single and multiple markers as well as present alternative solutions based on the prevalence of a marker. Our designs rest on the assumption that the methods for assessment of the biomarker are established and the initial results show promise with regard to the predictive ability of a marker. Additional research is clearly warranted to achieve the goal of ‘predictive oncology’.


Tamoxifen Irinotecan Cetuximab Recurrence Score Epidermal Growth Factor Receptor Gene 
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.



Supported in part by National Cancer Institute grants: Mayo Clinic Cancer Center (CA-15083) and the North Central Cancer Treatment Group (CA-25224). The authors have no potential conflicts of interest directly relevant to the contents of this review.


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Copyright information

© Adis Data Information BV 2005

Authors and Affiliations

  • Sumithra J. Mandrekar
    • 1
  • Axel Grothey
    • 2
  • Matthew P. Goetz
    • 2
  • Daniel J. Sargent
    • 1
  1. 1.Division of BiostatisticsMayo ClinicRochesterUSA
  2. 2.Department of OncologyMayo ClinicRochesterUSA

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