Moving from correlative clinical science to predictive medicine
Authors: Richard Simon
Journal: Clinical Cancer Research (2008)

ED01-03   New technology and biological knowledge make it increasingly feasible to predict which patients require systemic therapy and which are most likely to benefit or suffer severe adverse events from a specific reatment. Using genomic classifiers to target treatment can greatly improve the therapeutic ratio of benefit to adverse effects and reduce societal medical costs. There are, however, many challenges in effectively co-developing new drugs with predictive classifiers.  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. 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 pre-specified hypotheses (1). The purpose of a phase III 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.  My presentation will describe phase III clinical trial designs for utilizing biomarker classifiers in conjunction with new drug development. The results of Simon and Maitournam (2,3) evaluating the efficiency of enrichment designs that utilize biomarker classifiers for selecting patients for trial will be described. Randomized designs described by Simon and Wang (4) and Simon (5-7) 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. This trial design supports broad labeling indications in cases where drug activity is sufficient, and provides strong evidence of effectiveness for a prospectively defined subset where drug effects are more selective. The adaptive designs of Freidlin and Simon (4) 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 as will the adaptive design of Jiang et al. that permits adaptive identification of the best cut-point for a pre-defined predictive index. Reprints, as well as interactive software for planning targeted clinical trials is available at http://linus.nci.nih.gov/brb.   References  1.Simon R. Roadmap for developing and validating therapeutically relevant genomic classifiers. Journal of Clinical Oncology 2005;23:7332-41.  2. Simon R and Maitnourim A. Evaluating the efficiency of targeted designs for randomized clinical trials. Clinical Cancer Research 2004;10:6759-63; Supplement and correction 2006;12:3229.  3. Maitnourim A and Simon R. On the efficiency of targeted clinical trials. Statistics in Medicine 2005;24:329-39.  4. Simon R, Wang SJ. Use of genomic signatures in therapeutics development. The Pharmacogenomics Journal 2006;6:166-73.  5. Simon R. Using genomics in clinical trial design. Clinical Cancer Research (in press).  6. Simon, R. Designs and adaptive analysis plans for pivotal clinical trials of therapeutics and companion diagnostics. Expert Opinion on Medical Diagnostics (in press).  7. Simon R. Lost in translation: Problems and pitfalls in translating laboratory observations to clinical utility. European Journal of Cancer (in press).  8. 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 2005;11:7872-8.  9. Jiang W, Varma S, Simon R. Calculating confidence intervals for prediction error in microarray classification using resampling. Statistical Applications in Genetics and Molecular Biology :7(1)article 8, 2008.