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Novel Clinical Trial Designs for Oncology Richard Simon, D.Sc. Biometric Research Branch National Cancer Institute rsimon@nih.gov
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BRB Website http://brb.nci.nih.gov Powerpoint presentations & reprints BRB-ArrayTools software Web based sample size planning
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How can therapeutics development be successful if tumors contain dozens, hundreds or thousands of mutations, with substantial intra-tumor heterogeneity? How should we modify our paradigms for clinical development in light of tumor genomic heterogeneity?
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Success Likely Requires Inhibiting pathways deregulated by early oncogenic mutations Using combinations of molecularly targeted drugs Treating early Before mutational meltdown Treating the right tumors with the right drugs
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Co-development of drugs and companion diagnostics increases the complexity of drug development It does not make drug development simpler, cheaper and quicker But it may make development more successful and it has great potential value for patients and for the economics of health care
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Roadmap for Co-Development of New Drugs with Companion Diagnostics Develop during phase II a completely specified genomic classifier of the patients likely to benefit from a new drug Single gene/protein or composite gene expression classifier Develop an analyticly validated assay (reproducibe and robust) for the classifier Use the completely specified classifier to design and analyze a phase III clinical trial to evaluate effectiveness of the new treatment with a pre-defined analysis plan.
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Targeted (Enrichment) Design Restrict entry to the phase III trial based on the binary predictive classifier
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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
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Applicability of Targeted Design Primarily for settings where the drug effect is specific, the biology of the target is well understood, and an accurate assay is available Advantage of design is that the target population is clear and trial clearly must be sized for the test+ patients With a strong biological basis for the test and a drug with potentially serious toxicity, it may be unacceptable to expose test negative patients to the drug Analytical validation, biological rationale and phase II data provide basis for regulatory approval of the test, if needed
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Relative efficiency of targeted design depends on proportion of patients test positive effectiveness of new drug (compared to control) for test negative patients When less than half of patients are test positive and the drug has little or no benefit for test negative patients, the targeted design requires dramatically fewer randomized patients
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Biomarker Stratified Design
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Biomarker Stratified Design Do not use the diagnostic to restrict eligibility, but to structure a prospective analysis plan Having a prospective analysis plan for how the test will be used in the analysis and having the trial appropriately sized are essential The purpose of the study is to evaluate the new treatment overall and for the pre-defined subsets
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R Simon. Using genomics in clinical trial design, Clinical Cancer Research 14:5984-93, 2008
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Analysis Plan B (Limited confidence in test) 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 p+ ≤0.02 claim effectiveness for the classifier + patients.
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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
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Does the RCT Need to Be Significant Overall for the Treatment Comparison to Justify the Pre-planned Focused Subset Analysis? No That requirement has been traditionally used to protect against data dredging. It is inappropriate for focused trials of a treatment with a companion test with a pre-planned subset analysis if the analysis plan protects the overall type I error at 5%. .
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Marker Strategy Design
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Marker Strategy Design
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Phase III RCT of new regimen T vs control C Multiple candidate predictive biomarkers or whole genome expression profiling Prospectively specified classifier development algorithm
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Apply the algorithm A to the full dataset to develop a classifier function that provides a prediction of whether the new treatment T is better than the control C for a patient with covariate vector x. e.g. let h(t;x,z) denote a proportional hazards model fit to the data providing estimate of hazard of failure for patient with covariate vector x receiving treatment z (1 for T, 0 for C)
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e.g. Classify a patient with covariate vector x as likely to benefit from the new treatment if h(t;x,z=1)/h(t;x,z=0) < 0.8 Evaluate the effectiveness of the classifier by imbedding the classification algorithm A into a K-fold cross-validation
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Phase II Trials for Molecularly Targeted Drugs The purpose of phase II trials is to decide what phase III trials to do and how to do them Phase II trials today need to identify the right predictive biomarker for use in phase III and develop an analytically validated assay for it’s measurement Larger phase II trials are needed to achieve the new objectives
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Phase II trials should not serve as the basis for medical practice except in unusual circumstances which we hope become more frequent The design and analysis of phase II trials can be less restrictive and more exploratory than for phase III trials
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Phase II trials can be used to exclude clearly unpromising regimens Can use “conditional surrogate endpoints” Effect of treatment on a conditional surrogate is necessary but not sufficient for effect on clinical outcome Phase II trials can be used to screen for large anti-tumor effects in genomically defined sub-populations
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Single arm phase II trials are efficient and interpretable for screening single agents for activity in shrinking tumors for identifying genomically characterized subsets where anti-tumor activity is maximized
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Progression Delay Evaluating progression delay is inherently comparative Rate of tumor progression untreated is often highly variable Stable disease definitions are frequently not documented as being interpretable as a drug effect
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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
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Number of total events to observe in two-arm randomized phase II trial comparing progression-free survival 1-sided significance.
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Seamless phase II/III trials Randomized between 1 or more new regimens and a control arm Interim analysis based on phase II endpoint for either selecting among new regimens or terminating accrual for futility Final analysis based on phase III endpoint using all randomized patients
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Copyright restrictions may apply. Parmar, M. K. B. et al. J. Natl. Cancer Inst. 2008 100:1204-1214; doi:10.1093/jnci/djn267 Hypothetical randomized trial showing a multi-arm, two-stage design
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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
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Concurrent 2-Stage Phase II Designs for K Marker Strata Can use with time to progression endpoint Can use with early stopping criteria
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K Arms (Strata) Optimal Two-Stage Phase II Design for each arm α=0.10 β=0.20
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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
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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
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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
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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
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Acknowledgement Boris Freidlin Abboubakar Maitournam Wenyu Jiang