1. The Use of Predictive Biomarkers in Clinical Trial Design Richard Simon, D.Sc. Chief, Biometric Research Branch National Cancer Institute http://brb.nci.nih.gov

2. Biometric Research Branch Website http://brb.nci.nih.gov Powerpoint presentations Reprints BRB-ArrayTools software Web based Sample Size Planning

3. Successful Phase III Clinical Trials Require Right treatment Right patient population Right endpoint Right control group Right sample size

4. The Right Patient Population Predictive biomarkers –Measured before treatment to identify who will or will not benefit from a particular treatment ER, HER2, KRAS Prognostic biomarkers –Measured before treatment to indicate long-term outcome for patients receiving standard treatment Used to identify who does not require more intensive treatment

5. Prognostic and Predictive Biomarkers in Oncology Single gene or protein measurement –ER protein expression –HER2 amplification –KRAS mutation Score or classifier that summarizes expression levels of multiple genes –OncotypeDx recurrence score

6. Traditional Approach to Clinical Development a New Drug Small phase II trials to find primary sites where the drug appears active Phase III trials with broad eligibility to test the null hypothesis that a regimen containing the new drug is not better than the control treatment on average for all randomized patients Perform post-hoc subset hypotheses – but don’t believe them

7. The established molecular heterogeneity of human cancer requires the use new approaches to the development and evaluation of therapeutics The current approach to evaluating predictive markers as post-hoc analyses of phase III trials is not an adequate basis for developing a reliable science based predictive oncology

8. How Can We Develop New Drugs in a Manner More Consistent With Modern Tumor Biology and Obtain Reliable Information About What Regimens Work for What Kinds of Patients?

9. Guiding Principle The data used to develop the classifier must be distinct from the data used to test hypotheses about treatment effect in subsets determined by the classifier

10. Using phase II data, develop predictor of response to new drug Develop Predictor of Response to New Drug Patient Predicted Responsive New Drug Control Patient Predicted Non-Responsive Off Study

11. Targeted Design Primarily for settings where the mechanism of action is well understood –eg trastuzumab With a strong biological basis for the test, it may be unacceptable to expose test negative patients to the new drug Analytical validation, biological rationale and phase II data provide basis for regulatory approval of the test

12. Evaluating the Efficiency of Targeted Design Simon R and Maitnourim A. Evaluating the efficiency of targeted designs for randomized clinical trials. Clinical Cancer Research 10:6759-63, 2004; Correction and supplement 12:3229, 2006 Maitnourim A and Simon R. On the efficiency of targeted clinical trials. Statistics in Medicine 24:329-339, 2005. reprints and interactive sample size calculations at http://linus.nci.nih.gov

13. Web Based Software for Designing RCT of Drug and Predictive Biomarker http://brb.nci.nih.gov

14. Objections and Risks to Using the Targeted Design We won’t know whether test negative patients might also benefit –That can be studies in a subsequent trial if the drug is effective for the test positive patients –Restricting eligibility to test positive patients forces one to size the trial with adequate power for the test positive patients and avoids objections that the test positive subset should not be looked at unless the results are significant overall

15. Objections and Risks to Using the Targeted Design We may have the wrong predictive biomarker and using it to restrict eligibility may make it more difficult to use archived specimens for later analysis with other candidate biomarkers –e.g. cetuximab and panitumumab in advanced colorectal cancer –There can be a delicate balance between protecting patients whom we do not expect to benefit from the new treatment and the desire to protect ourselves against having the wrong biomarker –Phase II results in test negative and test positive patients can help us decide whether the targeted design is appropriate

16. Biomarker Stratified Design Develop Predictor of Response to New Rx Predicted Non- responsive to New Rx Predicted Responsive To New Rx Control New RXControl New RX

17. Do not use the diagnostic to restrict eligibility, but to structure a prospective analysis plan Having a prospective analysis plan is essential The analysis plan should protect the overall type I error without requiring that the overall average effect to be significant as a justification for evaluating the treatment effect in the test positive subset “Stratifying” (balancing) the randomization is useful to ensure that all randomized patients have tissue available but is not a substitute for a prospective analysis plan The purpose of the study is to evaluate the new treatment overall and for the pre-defined subsets; not to modify or refine the classifier The purpose is not to demonstrate that repeating the classifier development process on independent data results in the same classifier

18. R Simon. Using genomics in clinical trial design, Clinical Cancer Research 14:5984-93, 2008 R Simon. Designs and adaptive analysis plans for pivotal clinical trials of therapeutics and companion diagnostics, Expert Opinion in Medical Diagnostics 2:721-29, 2008

20. Fallback Analysis Plan Compare the new drug to the control overall for all patients ignoring the classifier. –If p overall  0.03 claim effectiveness for the eligible population as a whole Otherwise perform a single subset analysis evaluating the new drug in the classifier + patients –If p subset  0.02 claim effectiveness for the classifier + patients.

21. It is difficult to have the right completely defined predictive biomarker identified and analytically validated by the time the phase III trial is ready to start accrual –Adaptive methods for the selection, refinement and evaluation of predictive biomarkers in the pivotal trials in a prospectively defined and non-exploratory manner

23. Multiple Biomarker Design Have identified K candidate binary classifiers B 1, …, B K thought to be predictive of patients likely to benefit from T relative to C RCT comparing new treatment T to control C Eligibility not restricted by candidate classifiers

24. Compare the new drug to the control overall for all patients ignoring the classifiers –If p overall  0.03 claim effectiveness for the eligible population as a whole Otherwise

25. Compute statistical significance test of T vs C restricted to patients positive for B k for each k –Let k* be the test for which the treatment effect is most significant when restricted to test positive patients –Let S* be the treatment effect for patients positive for test k* Obtain the null distribution of S* by randomly permuting the treatment labels and repeating the analysis of finding the subset with the largest treatment effect Evaluate whether S* is significant at the 0.02 level of this null distribution

26. Measure markerStart treatment E Measure marker response B Randomize to E or C Stratified by B Biomarker Stratified Run-In Design

27. Acknowledgements –Boris Freidlin –Yingdong Zhao –Aboubakar Maitournam –Wenyu Jiang