1. Advanced Clinical Trial Educational Session Richard Simon, D.Sc. Biometric Research Branch National Cancer Institute rsimon@nih.gov

2. BRB Website http://brb.nci.nih.gov Powerpoint presentations & reprints BRB-ArrayTools software Accelerated titration design software Web based sample size planning –Optimal 2-stage phase II designs –Phase II/III trials –Clinical Trials with Predictive Biomarkers –Development of prognostic signatures

3. Discovery of key biological mechanisms Identify key drug-able molecular targets Develop compound to inhibit target Clinical evaluation of compound

4. Can the compound be administered safely at a dose that inhibits the target? Does the drug have sufficient anti-tumor activity in a defined target population to warrant phase III Trial? Does the drug have medical utility for a target population? How should the drug be used in practice?

5. Phase III Clinical Trials Should Test the null hypothesis of no treatment benefit for the new treatment vs control for the identified target population(s) while preserving the type I error Provide physicians with predictive tools and measures of uncertainty to assist in applying the results to individual patients –Method of Freidlin, Jiang, Simon to develop and internally validate algorithms for individualizing therapy

7. Objectives of Phase II Trials Does the drug inhibit the putative molecular target Can a promising predictive biomarker be determined for identifying patients whose tumors are sensitive to the drug Does the drug or a regimen containing the drug have sufficient anti-tumor activity in a targeted population to warrant phase III testing?

8. Success May Require Inhibit pathways deregulated by key oncogenic mutations Treat the right tumors with the right drugs Use combinations of molecularly targeted drugs Treat early –Before mutational meltdown –Adjuvant studies Use sensitive endpoints in phase II to optimize regimens and predictive markers

9. Endpoints for Phase II Objective response rate Tumor growth delay –Stable disease –Proportion without progression at 8 weeks –Progression-free survival

10. Single Arm Optimal Two-Stage Phase II Design  =0.10  =0.20 http://brb.nci.nih.gov Null response probability Desirable response probabiliity Stop early if  r 1 responses in n 1 patients Reject drug if  r responses in n patients Average sample size Probability of early stopping p0p0 p1p1 r 1 /n 1 r/nEN(p 0 )PET(p 0 ).05.250/62/2310.5.74.10.300/73/1812.7.48.20.402/127/2517.7.56.30.505/1512/3220.7.72

11. Single Arm Optimal Two-Stage Phase II Design  =0.10  =0.20 http://brb.nci.nih.gov Null response probability Desirable response probabiliity Stop early if  r 1 responses in n 1 patients Reject drug if  r responses in n patients Average sample size Probability of early stopping p0p0 p1p1 r 1 /n 1 r/nEN(p 0 )PET(p 0 ).05.200/92/2414.5.63.10.251/135/3421.0.62.20.352/1312/4630.0.50.30.456/2020/5533.7.62

12. Single Arm Phase II With Specific Historical Controls Patients accrued at the same center or group with same work-up and response evaluation Matched for prognostic comparability or post-stratified for comparability

13. Single arm phase II trial Comparing response rate to specific historical controls Number of new patients required to detect 20% absolute difference. alpha=0.10, beta=0.20

15. Progression Delay Evaluating progression delay is inherently comparative –Rate of tumor progression untreated is often highly variable Stable disease definitions are rarely documented as being interpretable as a drug effect

16. Randomized Specific historical controls or duration documented based on historical controls Undocumented duration representing stability with no controls

17. Rixe and Fojo Clin Cancer Res 2007;13:7280 “… agents we now refer to as cytotoxic have for decades been observed to cause cytostasis but were never described in those terms. Indeed, in most cases cytostasis was viewed as ineffective and disregarded. …many if not all of these [molecularly targeted] compounds possess cytotoxic activity that is at least as important if not more so than any cytostatic property…pure cytostatic agents may not exist…when cytostatis occurs it will usually be followed by either cytotoxicity or cellular escape from the stasis.”

19. Limitations of Randomized Phase II Clinical Trials Increased sample size Reduced accrual rate Reduced number of new drugs and regimens that can be screened Difficulty in accruing to subsequent phase III trials

20. Randomized Phase II Trials: What Does Randomization Gain H. Samuel Wieand JCO 23:1794, 2005 “Johnson et al presented the results of a randomized phase II design that called for a formal comparison of regimens at the conclusion of the trial. This design is used less frequently because it requires roughly four times as many patients as a single arm trial … the design for the Johnson et al. trial required 33 patients for each of the three arms but was powered for a huge difference in response rates…”

21. Number of total events to observe in two-arm randomized phase II trial comparing progression-free survival 1-sided significance.

22. Heymach, J. V. et al. J Clin Oncol; 25:4270-4277 2007 Fig 1. Study design

25. Process and/or flow or approaches for determination of phase II trial design recommendations. Seymour L et al. Clin Cancer Res 2010;16:1764-1769 ©2010 by American Association for Cancer Research

26. Randomized Phase II Selection Designs K experimental arms, no control arm Select arm with highest response rate or disease control rate further development Simon, Wittes, Ellenberg; Cancer Treatment Reports 69:1375, 1985

27. Randomized Phase II Selection Designs Can use with time to progression endpoint Can use with early stopping criteria –e.g. Use optimal 2-stage phase II design separately for each treatment arm; at end of trial select arm with highest observed response rate Can be used with cross-over to secondary treatment to obtain additional information Can be used within genomically defined strata

28. K Experimental Arms Optimal Two-Stage Phase II Design for each arm  =0.10  =0.20 Null response probability Desirable response probabiliity Stop early if  r 1 responses in n 1 patients Reject drug if  r responses in n patients Probability of Correct Selection p0p0 p1p1 r 1 /n 1 r/n K=2 armsK=3 arms.05.250/62/230.810.80.10.300/73/180.860.82.20.402/127/250.870.83.30.505/1512/320.830.80

32. Optimizing the Number and Size of Phase II Trials for a Horizon of 1000 patients

33. Are There That Many Worthy Candidate Treatment Regimens? Combinations of molecularly targeted agents Genomic subsets of tumors

34. Marker Based Phase II Design Pusztai, Anderson, Hess (ClinCancerRes 2007) Two stage design, treat all comers of a given primary site If the overall number of responders at the end of stage 1 is adequate, then continue and complete trial based on overall analysis If the overall number of responders at the end of stage 1 is not adequate, then start separate two stage phase II trials for each marker stratum

36. Bayesian Adaptive Design BATTLE Study in NSCLC Randomized phase II trial with 4 experimental regimens, no control group –Erlotinib, sorafenib, vandetanib, erlotinib+bexarotene Tumor biopsy at entry, assayed for candidate predictive biomarkers –EGFR mutation or amplification –KRAS or BRAF mutation –VEGFr2 over-expression –Cyclin D1 over-expression Endpoint is freedom from progression at 8 weeks

37. Bayesian Adaptive Design BATTLE Study in NSCLC First 97 patients were randomized equally to the 4 arms Then, the randomization was weighted based on estimated of effectiveness of each regimen for patients in each biomarker stratum

38. Bayesian Adaptive Design BATTLE Study in NSCLC As data accumulates, for each treament i and marker stratum k the probability that 8 week disease control is > 0.5 is computed If that probability becomes <0.10 for some treatment i and stratum k, then use of that treatment in that stratum is suspended

39. Bayesian Adaptive Design BATTLE Study in NSCLC Results not yet published Approximately 255 patients randomized Erlotinib performed best against EGFR mutant tumors Sorafinib performed best against KRAS mutant tumors Vandetanib performed best against tumors that over-expressed VEGFr2 Erlotinib+bexarotene performed best against tumors that over-expressed cyclin D1

40. Simpler Approach to Evaluating Treatments Within Pre-specified Biomarker Strata Randomized phase II trial starting with 4 treatment arms Evaluate outcome for each treatment in each of 4 biomarker strata separately using an optimal two-stage design –Tumor control at 8 weeks is considered response

41. Optimal Two-Stage Phase II Design for Each Regimen in Each Marker Stratum  =0.10  =0.10 Null response probability Desirable response probability Stop early if  r 1 responses in n 1 patients Reject drug if  r responses in n patients Average sample size Probability of early stopping Probability of correct selection p0p0 P1P1 r 1 /n 1 r/nEN(p 0 )PET(p 0 )PCS K=4 arms 0.300.602/88/2013.40.550.89

42. Expected sample size with one treatment effective in each of 4 strata –4*(3*13.4 + 1*20) = 240 patients At end of trial, compare treatment arms with regard to disease control at 8 weeks Other possible simplifications –Use diagnostic biopsy for assaying candidate biomarkers

43. Conclusions The purpose of phase II trials is to decide what phase III trials to do and how to do them Phase II trials should not serve as the basis for medical practice (except in unusual circumstances) The design and analysis of phase II trials can be less restrictive and more exploratory than for phase III trials

44. Conclusions If phase II trials were accurate predictors of phase III results there would be no need for phase III trials Phase II trials can be used to exclude clearly unpromising regimens and to screen for large anti-tumor effects in genomically defined sub- populations Making phase II trials very large with phase III endpoints is not an effective screening strategy –Such trials might best be designed as seamless phase II/III designs

45. Conclusions Randomized phase II screening trials are useful for controlling inter-study variation in patient selection and response assessment –Multiple new treatments with or without control arm –One new treatment with control arm –Control may serve as common reference to calibrate the activity of the new regimen for comparison across trials –Small randomized trials are often no more effective than small single arm trials for identifying treatments with moderate effects

46. Conclusions Single arm phase II trials are efficient and interpretable for –screening single agents for activity –for identifying genomically characterized subsets where anti-tumor activity is maximized –when historical databases are available and inter-study variability in response or disease control is accounted for by known prognostic factors

47. Conclusions Phase II trials should evaluate candidate biomarkers and seek genomically defined subsets of patients whose tumors appear sensitive to the new drug Phase II trials of combinations of molecularly targeted agents are essential