Using Genomics in the Design of Phase III Clinical Trials


Published on

  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Using Genomics in the Design of Phase III Clinical Trials

  1. 1. Using Genomics in the Design of Phase III Clinical Trials Richard Simon, D.Sc. Chief, Biometric Research Branch National Cancer Institute Nothing to disclose
  2. 2. BRB Website • Powerpoint presentations • Reprints & Technical Reports • BRB-ArrayTools software • BRB-ArrayTools Data Archive • Sample Size Planning for Targeted Clinical Trials
  3. 3. Randomize Discontinuation Design • B Freidlin & R Simon. Evaluation of the randomized disconuation design, Journal of Clinical Oncology 23, 2005. • RDD may be more efficient than an up-front randomized phase II design if the following conditions hold: – Disease is rapidly progressive – Most tumors are resistant to the drug – Continuous multi-course treatment is required for sensitive tumors – No predictive biomarker available for sensitive tumors
  4. 4. • Many cancer treatments benefit only a small proportion of the patients to which they are administered • By targeting treatment to the right patients – Treated patients benefit – Treatment more cost-effective for society – More informative and successful clinical trials
  5. 5. Conducting a phase III trial in the traditional way with broad eligibility • May result in a false negative trial – Unless a sufficiently large proportion of the patients have tumors driven by the targeted pathway • May result in a positive trial – With overall results driven by a subset of patients – Resulting in subsequent treatment of many patients who do not benefit
  6. 6. The Roadmap 1. Develop a completely specified candidate genomic classifier of the patients most likely to benefit from a new drug • Classifier maps biological measurements to test + or test – classes • Single gene/protein or multivariate 2. Establish reproducibility of measurement of the classifier 3. Use the completely specified classifier to design and analyze a new clinical trial to evaluate effectiveness of the new treatment in patient subsets defined by the classifier.
  7. 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 are 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. 8. Test • How does this approach differ from conducting a RCT comparing a new treatment to a control and then performing numerous post-hoc subset analyses?
  9. 9. New Drug Developmental Strategy I • Restrict entry to the phase III trial based on the binary classifier, i.e. targeted design
  10. 10. Using phase II data, develop predictor of response to new drugDevelop Predictor of Response to New Drug Patient Predicted Responsive New Drug Control Patient Predicted Non-Responsive Off Study
  11. 11. 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 • Without strong biological rationale or adequate phase II data, FDA may have difficulty approving the test
  12. 12. 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. • Maitnourim A and Simon R. On the efficiency of targeted clinical trials. Statistics in Medicine 24:329-339, 2005.
  13. 13. Two Clinical Trial Designs • Un-targeted design – Randomized comparison of T to C without measuring the classifier • Targeted design – Randomize only test + patients
  14. 14. • Efficiency relative to trial of unselected patients depends on proportion of patients test positive, and effectiveness of 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
  15. 15. No treatment Benefit for Test - Patients nstd / ntargeted Proportion Test Positive Randomized Screened 0.75 1.78 1.33 0.5 4 2 0.25 16 4
  16. 16. Treatment Benefit for Test – Pts Half that of Test + Pts nstd / ntargeted Proportion Test Positive Randomized Screened 0.75 1.31 0.98 0.5 1.78 0.89 0.25 2.56 0.64
  17. 17. Trastuzumab • Metastatic breast cancer • HER2 + (IHC) patients randomized (25%) • 234 HER2 + randomized patients per arm • 90% power for 13.5% improvement in 1-year survival over 67% baseline at 2-sided .05 level • If HER2 – patients had been randomized and test not measured, 4025 patients per arm would have been required – overall improvement in survival would have been 3.375% if no benefit for test - patients • If HER2 – patients benefited half as much, 627 patients per arm would have been required
  18. 18. Web Based Software for Comparing Sample Size Requirements •
  19. 19. Developmental Strategy (II) Develop Predictor of Response to New Rx Test -Test + To New Rx Control New RX Control New RX
  20. 20. 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 ensures that all randomized patients will have tissue available • 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
  21. 21. Analysis Plan A • Compare the new drug to the control for classifier positive patients – If p+>0.05 make no claim of effectiveness – If p+≤ 0.05 claim effectiveness for the classifier positive patients and • Compare new drug to control for classifier negative patients using 0.05 threshold of significance
  22. 22. Sample size for Analysis Plan A • 88 events in classifier + patients needed to detect 50% reduction in hazard at 5% two-sided significance level with 90% power • 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
  23. 23. • Study-wise false positivity rate is limited to 5% with analysis plan A • It is not appropriate to require that the treatment vs control difference be significant overall before doing the analysis within subsets
  24. 24. 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.
  25. 25. • This analysis strategy is designed to not penalize sponsors or investigators for having developed a classifier • It provides sponsors with an incentive to develop genomic classifiers
  26. 26. Sample size for Analysis Plan B • To have 90% power for detecting uniform 33% reduction in overally 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
  27. 27. 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
  28. 28. 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 25% of patients are positive, then when there are 88 events in positive patients there will be about 264 events in negative patients – Using int=0.10, 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 – If the treatment reduces hazard by 33% uniformly, the interaction test is negative and the overall test is significant in 87% of cases
  29. 29. Use of Archived Samples • From a non-targeted “negative” clinical trial to develop a binary classifier of a subset thought to benefit from treatment • Test that subset hypothesis in a separate clinical trial – Prospective targeted type I trial – Using archived specimens from a second previously conducted clinical trial
  30. 30. Development of Genomic Classifiers • Single gene or protein based on knowledge of therapeutic target • Empirically determined based on evaluation of a set of candidate classifiers – e.g. EGFR assays • Empirically determined based on genome- wide correlating gene expression, copy number variation or genotype to patient outcome after treatment
  31. 31. Development of Genomic Classifiers • During phase II development or • After failed phase III trial using archived specimens. • Adaptively during early portion of phase III trial.
  32. 32. Conclusions • New technology makes it increasingly feasible to identify which patients are likely or unlikely to benefit from a specified treatment • Developing drugs in a prospectively targeted way increases the complexity of development but has benefits for patients and society
  33. 33. Conclusions • Some of the conventional wisdom about how to develop predictive classifiers and how to use them in clinical trial design and analysis is flawed • Prospectively specified analysis plans for randomized phase III studies are essential to achieve reliable results – Biomarker analysis does not mean exploratory analysis except in developmental studies – Prospective analysis of previously conducted trials can provide reliable conclusions
  34. 34. Conclusions • Moving from correlative science to predictive oncology requires paradigm changes in some aspects of design and analysis of clinical trials
  35. 35. Collaborators • Boris Freidlin • Aboubakar Maitournam • Kevin Dobbin • Wenu Jiang • Yingdong Zhao