Moving from Correlative Studies to Predictive Medicine: How to Include Biomarkers in Clinical Trials
Authors: Richard M. Simon
Journal: Clinical Cancer Research (2006)

PL-27   New technology and biological knowledge make it increasingly feasible to predict which patients are most likely to benefit or suffer severe adverse events from a new treatment. Using genomic classifiers to target treatment can greatly improve the therapeutic ratio of benefit to adverse effects. This results in smaller clinical trials, improved likelihood that a treated patient benefits, and economic benefit for society.Much of the conventional wisdom about how to develop and utilize predictive biomarker classifiers is flawed and does not lead to definitive evidence of treatment benefit for a well defined population. Leadership is needed for establishing specific guidelines for the design and analysis of adequate clinical trials that test new treatments in patient populations pre-defined based on completely specified diagnostic classifiers. However, over-regulating genomic classifier development or not providing sponsors with a clear and practical roadmap of what is required could slow utilization. Some FDA guidances do not adequately distinguish biomarkers used as surrogate endpoints from those used for treatment selection and are inappropriate for the latter applications. For composite classifiers used for treatment selection, there should be no requirement that the classifier or any of its components are validated biomarkers of disease status.The data used to develop a predictive classifier must be distinct from the data used to test hypotheses about treatment effect in subsets determined by the classifier. Developmental studies are exploratory, but studies on which treatment effectiveness claims are to be based should be definitive studies that test a treatment hypothesis in a patient population completely pre-specified by the classifier. The purpose of a pivotal clinical trial of a new drug that utilizes a genomic classifier is to determine whether the new drug provides benefit to a population of patients defined based on the pre-defined classifier. The purpose is not to refine the classifier or to demonstrate that repeating the classifier development process on independent data results in the same classifier.Phase III clinical trial designs should provide reliable evidence that a treatment provides benefit for a well defined population of patients, but guidelines should avoid establishing strong disincentives for utilization of genomic classifiers in new drug development. Trial designs are available that will support broad labeling indications in cases where drug activity is sufficient, and provide strong evidence of effectiveness for a prospectively defined subset where appropriate.Prospectively specified analysis plans for phase III data are essential to achieve reliable results. Biomarker analysis does not mean exploratory analysis except in developmental studies. Biomarker classifiers used in pivotal studies of new drugs should be completely specified based on external data. In some cases, definitive evidence can be achieved from prospective analysis of patients in previously conducted clinical trials with extensive archival of pre-treatment specimens.This presentation will describe clinical trial designs for utilizing biomarker classifiers in conjunction with new drug development. The results of Simon and Maitournam evaluating the efficiency of designs that utilize biomarker classifiers in selecting patients for trial will be described. Randomized designs described by Simon and Wang that do not restrict eligibility but permit evaluating the treatment overall for all randomized patients as well as for one pre-defined biomarker determined subset of patients will be described. The adaptive design of Freidlin and Simon that permits development of a biomarker classifier during the initial phase of a phase III trial and then testing the treatment overall and for the identified subset will also be discussed.Reprints as well as interactive software for planning targeted clinical trials is available at http://linus.nci.nih.gov/brb .Simon R and Maitnourim A. Evaluating the efficiency of targeted designs for randomized clinical trials. Clinical Cancer Research 10:6759-63, 2004..Maitnourim A and Simon R. On the efficiency of targeted clinical trials. Statistics in Medicine 24:329-339, 2005.Freidlin B and Simon R. Adaptive signature design: An adaptive clinical trial design for generating and prospectively testing a gene expression signature for sensitive patients. Clinical Cancer Research 11:7872-8, 2005Simon R, Wang SJ. Use of genomic signatures in therapeutics development. The Pharmacogenomics Journal 6:166-173, 2006.Simon R. Roadmap for developing and validating therapeutically relevant genomic classifiers. Journal of Clinical Oncology 23:7332-41, 2005.