Journal of the National Cancer Institute Advance Access published online on June 27, 2007
JNCI Journal of the National Cancer Institute, doi:10.1093/jnci/djm022
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
© The Author 2007. Published by Oxford University Press.
ARTICLES |
Biomarker-Adaptive Threshold Design: A Procedure for Evaluating Treatment With Possible Biomarker-Defined Subset Effect
Affiliation of authors: Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD
Correspondence to: Boris Freidlin, PhD, Biometric Research Branch, Division of Cancer Treatment and Diagnosis, EPN-8122, National Cancer Institute, Bethesda, MD 20892 (e-mail: freidlinb{at}ctep.nci.nih.gov).
Background: Many molecularly targeted anticancer agents entering the definitive stage of clinical development benefit only a subset of treated patients. This may lead to missing effective agents by the traditional broad-eligibility randomized trials due to the dilution of the overall treatment effect. We propose a statistically rigorous biomarker-adaptive threshold phase III design for settings in which a putative biomarker to identify patients who are sensitive to the new agent is measured on a continuous or graded scale.
Methods: The design combines a test for overall treatment effect in all randomly assigned patients with the establishment and validation of a cut point for a prespecified biomarker of the sensitive subpopulation. The performance of the biomarker-adaptive design, relative to a traditional design that ignores the biomarker, was evaluated in a simulation study. The biomarker-adaptive design was also used to analyze data from a prostate cancer trial.
Results: In the simulation study, the biomarker-adaptive design preserved the power to detect the overall effect when the new treatment is broadly effective. When the proportion of sensitive patients as identified by the biomarker is low, the proposed design provided a substantial improvement in efficiency compared with the traditional trial design. Recommendations for sample size planning and implementation of the biomarker-adaptive design are provided.
Conclusions: A statistically valid test for a biomarker-defined subset effect can be prospectively incorporated into a randomized phase III design without compromising the ability to detect an overall effect if the intervention is beneficial in a broad population.
| CONTEXT AND CAVEATS Prior knowledge Many molecularly targeted anticancer agents have the potential to benefit only a subset of patients, that is, those whose levels of the target exceed a certain threshold level. When a biomarker for the target is available but a cutoff to distinguish sensitive from insensitive patients has not been defined, a clinical trial will include insensitive as well as sensitive patients, and any effect of the agent on the subset of sensitive patients may therefore be missed. Study design A phase III trial design was developed that combines a test for treatment effect in all patients with the identification and validation of a cutoff point for a prospectively chosen biomarker. The design was tested in a simulation study and was also used to analyze data from an existing trial. Contribution In the simulation study, the design allowed a benefit to be seen both when the agent was effective in a broad patient population and when it was effective in a smaller, biomarker-defined subset. For example, when the treatment causes a 79% reduction in hazard in just 10% of the patients, the trial design has a power of 63% whereas a standard design would have a power of only 24%. Implications Using this design, it should be possible to prospectively incorporate validation of a biomarker for identifying sensitive patients into a randomized phase III trial design in such a way that an overall effect can still be detected if one exists. Limitations The approach has not yet been tested in an actual clinical trial. Some increase in sample size may be necessary. The approach requires that a quantitative biomarker for sensitivity has already been identified.
|
Manuscript received November 2, 2006; revised April 27, 2007; accepted May 18, 2007.
Related Article in JNCI
CiteULike 
Connotea 
Del.icio.us What's this?
J Natl Cancer Inst 2007 99: 981.
This article has been cited by other articles:
|
|
 |
|
  R. M. Simon, S. Paik, and D. F. Hayes Use of Archived Specimens in Evaluation of Prognostic and Predictive Biomarkers J Natl Cancer Inst, November 4, 2009; 101(21): 1446 - 1452. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. E. Taube, G. M. Clark, J. E. Dancey, L. M. McShane, C. C. Sigman, and S. I. Gutman A Perspective on Challenges and Issues in Biomarker Development and Drug and Biomarker Codevelopment J Natl Cancer Inst, November 4, 2009; 101(21): 1453 - 1463. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 S. J. Mandrekar and D. J. Sargent Clinical Trial Designs for Predictive Biomarker Validation: Theoretical Considerations and Practical Challenges J. Clin. Oncol., August 20, 2009; 27(24): 4027 - 4034. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
L. Pusztai, K. Broglio, F. Andre, W. F. Symmans, K. R. Hess, and G. N. Hortobagyi Effect of Molecular Disease Subsets on Disease-Free Survival in Randomized Adjuvant Chemotherapy Trials for Estrogen Receptor-Positive Breast Cancer J. Clin. Oncol., October 1, 2008; 26(28): 4679 - 4683. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 R. Simon The Use of Genomics in Clinical Trial Design Clin. Cancer Res., October 1, 2008; 14(19): 5984 - 5993. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
L. J. Goldstein, R. Gray, S. Badve, B. H. Childs, C. Yoshizawa, S. Rowley, S. Shak, F. L. Baehner, P. M. Ravdin, N. E. Davidson, et al. Prognostic Utility of the 21-Gene Assay in Hormone Receptor-Positive Operable Breast Cancer Compared With Classical Clinicopathologic Features J. Clin. Oncol., September 1, 2008; 26(25): 4063 - 4071. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. Hoering, M. LeBlanc, and J. J. Crowley Randomized Phase III Clinical Trial Designs for Targeted Agents Clin. Cancer Res., July 15, 2008; 14(14): 4358 - 4367. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 R. Labianca, M. Garassino, and V. Torri Predicting response of molecular targeted therapies: a still possible challenge? Ann. Onc., May 1, 2008; 19(5): 829 - 830. [Full Text] [PDF]
|
|