Steps on the Road to Predictive Medicine

1. Steps on the Road to Predictive Medicine Richard Simon, D.Sc. Chief, Biometric Research Branch National Cancer Institute http://brb.nci.nih.gov

2. BRB Websitebrb.nci.nih.gov • Powerpoint presentations • Reprints & Presentations Reports • BRB-ArrayTools software • Web based Sample Size Planning • Clinical Trials using predictive biomarkers • Development of gene expression based predictive classifiers

3. Many cancer treatments benefit only a minority of patients to whom they are administered • Particularly true for molecularly targeted drugs • Being able to predict which patients are likely to benefit would • save patients from unnecessary toxicity, and enhance their chance of receiving a drug that helps them • Help control medical costs • Improve the success rate of clinical drug development

4. Biomarkers • Prognostic • Measured before treatment to indicate long-term outcome for patients untreated or receiving standard treatment • Predictive • Measured before treatment to select good patient candidates for a particular treatment

5. Prognostic and Predictive Biomarkers in Oncology • Single gene or protein measurement • HER2 protein staining 2+ or 3+ • HER2 amplification • KRAS mutation • Index or classifier that summarizes contributions of multiple genes/proteins • Empirically determined based on genome-wide correlating gene expression to patient outcome after treatment

6. Prospective Co-Development of Drugs and Companion Diagnostics • Develop a completely specified genomic classifier of the patients likely to benefit from a new drug • Establish analytical validity of the test • Design a pivotal RCT evaluating the new treatment with sample size, eligibility, and analysis plan prospectively based on use of the completely specified classifier/test.

7. 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 • Developmental studies can be exploratory • 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

8. New Drug Developmental Strategy I • Restrict entry to the phase III trial based on the binary predictive classifier, i.e. targeted design

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

10. Applicability of Design I • Primarily for settings where the classifier is based on a single gene whose protein product is the target of the drug • eg Herceptin • With substantial biological basis for the classifier, it may be unacceptable ethically to expose classifier negative patients to the new drug • Strong biological rationale or phase II data on unselected patients needed for approval of test

11. Evaluating the Efficiency of Strategy (I) • 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

12. Web Based Software for Comparing Sample Size Requirements • http://brb.nci.nih.gov

18. DevelopPredictor of Response to New Rx Predicted Responsive To New Rx Predicted Non-responsive to New Rx New RX Control New RX Control Developmental Strategy (II)

19. Developmental Strategy (II) • Do not use the test to restrict eligibility, but to structure a prospective analysis plan • Having a prospective analysis plan is essential • “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

20. 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

21. Analysis Plan B • Compare the new drug to the control overall for all patients ignoring the classifier. • If poverall 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 psubset 0.02 claim effectiveness for the classifier + patients.

22. This analysis strategy is designed to not penalize sponsors for having developed a classifier • It provides sponsors with an incentive to develop genomic classifiers

23. Sample size for Analysis Plan B • To have 90% power for detecting uniform 33% reduction in overall hazard at 3% two-sided level requires 297 events (instead of 263 for similar power at 5% level) • If 25% of patients are positive, then when there are 297 total events there will be approximately 75 events in positive patients • 75 events provides 75% power for detecting 50% reduction in hazard at 2% two-sided significance level • By delaying evaluation in test positive patients, 80% power is achieved with 84 events and 90% power with 109 events

24. Analysis Plan C • Test for interaction between treatment effect in test positive patients and treatment effect in test negative patients • If interaction is significant at level int then compare treatments separately for test positive patients and test negative patients • Otherwise, compare treatments overall

25. Sample Size Planning for Analysis Plan C • 88 events in classifier + patients needed to detect 50% reduction in hazard at 5% two-sided significance level with 90% power • If test is predictive but not prognostic, and if 25% of patients are positive, then when there are 88 events in positive patients there will be about 264 events in negative patients • 264 events provides 90% power for detecting 33% reduction in hazard at 5% two-sided significance level

26. Simulation Results for Analysis Plan C • Using int=0.10, the interaction test has power 93.7% when there is a 50% reduction in hazard in test positive patients and no treatment effect in test negative patients • A significant interaction and significant treatment effect in test positive patients is obtained in 88% of cases under the above conditions • If the treatment reduces hazard by 33% uniformly, the interaction test is negative and the overall test is significant in 87% of cases

30. Biomarker Adaptive Threshold Design Wenyu Jiang, Boris Freidlin & Richard Simon JNCI 99:1036-43, 2007

31. Biomarker Adaptive Threshold Design • Randomized trial of T vs C • Have identified a univariate biomarker index B thought to be predictive of patients likely to benefit from T relative to C • Eligibility not restricted by biomarker • No threshold for biomarker determined • Biomarker value scaled to range (0,1) • Time-to-event data

32. Procedure A • Compare T vs C for all patients • If results are significant at level .04 claim broad effectiveness of T • Otherwise proceed as follows

33. Procedure A • Test T vs C restricted to patients with biomarker B > b • Let S(b) be log likelihood ratio statistic • Repeat for all values of b • Let S* = max{S(b)} • Compute null distribution of S* by permuting treatment labels • If the data value of S* is significant at 0.01 level, then claim effectiveness of T for a patient subset • Compute point and interval estimates of the threshold b

34. Procedure B • S(b)=log likelihood ratio statistic for treatment effect in subset of patients with Bb • S*=max{S(0)+R, max{S(b)}} • Compute null distribution of T by permuting treatment labels • If the data value of T is significant at 0.05 level, then reject null hypothesis that T is ineffective • Compute point and interval estimates of the threshold b

35. Estimation of Threshold

37. Prostate Cancer Data

38. Prostate Cancer Data

41. Sample Size Planning (A) • Standard broad eligibility trial is sized for 80% power to detect reduction in hazard D at significance level 5% • Biomarker adaptive threshold design is sized for 80% power to detect same reduction in hazard D at significance level 4% for overall analysis

42. Estimated Power of Broad Eligibility Design (n=386 events) vs Adaptive Design A (n=412 events) 80% power for 30% hazard reduction

43. Sample Size Planning (B) • Estimate power of procedure B relative to standard broad eligibility trial based on Table 1 for the row corresponding to the expected proportion of sensitive patients ( ) and the target hazard ratio for sensitive patients • e.g. =25% and =.4 gives RE=.429/.641=.67 • When B has power 80%, overall test has power 80*.67=53% • Use formula B.2 to determine the approximate number of events needed for overall test to have power 53% for detecting =.4 limited to =25% of patients

44. Events needed to Detect Hazard Ratio  With Proportional Hazards

45. Events (D’) Needed for Overall Test to Detect Hazard Ratio  Limited to Fraction 

46. Example Sample Size Planning for Procedure B • Design a trial to detect =0.4 (60% reduction) limited to =25% of patients • Relative efficiency from Table 1 .429/.641=.67 • When procedure B has power 80%, standard test has power 80%*.67=53% • Formula B.2 gives D’=230 events to have 53% power for overall test and thus approximate 80% power for B • Overall test needs D=472 events for 80% power for detecting the diluted treatment effect

48. Adaptive Signature Design Boris Freidlin and Richard Simon Clinical Cancer Research 11:7872-8, 2005

49. Adaptive Signature DesignEnd of Trial Analysis • Compare T to C for all patients at significance level overall • If overall H0 is rejected, then claim effectiveness of T for eligible patients • Otherwise

50. Otherwise: • Using only the first half of patients accrued during the trial, develop a binary classifier that predicts the subset of patients most likely to benefit from the new treatment T compared to control C • Compare T to C for patients accrued in second stage who are predicted responsive to E based on classifier • Perform test at significance level 0.05 - overall • If H0 is rejected, claim effectiveness of T for subset defined by classifier