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Using Genomics in the Design of Phase III Clinical Trials

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Using Genomics in the Design of Phase III Clinical Trials Using Genomics in the Design of Phase III Clinical Trials Presentation Transcript

  • Using Genomics in the Design of Phase III Clinical Trials Richard Simon, D.Sc. Chief, Biometric Research Branch National Cancer Institute http://linus.nci.nih.gov Nothing to disclose
  • BRB Website http://linus.nci.nih.gov
    • Powerpoint presentations
    • Reprints & Technical Reports
    • BRB-ArrayTools software
    • BRB-ArrayTools Data Archive
    • Sample Size Planning for Targeted Clinical Trials
  • 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
    • 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
  • 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
  •  
  •  
  •  
  • The Roadmap
    • 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
    • Establish reproducibility of measurement of the classifier
    • 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.
  • 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
  • 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?
  • New Drug Developmental Strategy I
    • Restrict entry to the phase III trial based on the binary classifier, i.e. targeted design
  • 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
  • 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
  • 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.
  • Two Clinical Trial Designs
    • Un-targeted design
      • Randomized comparison of T to C without measuring the classifier
    • Targeted design
      • Randomize only test + patients
    • 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
  • No treatment Benefit for Test - Patients n std / n targeted 4 16 0.25 2 4 0.5 1.33 1.78 0.75 Screened Randomized Proportion Test Positive
  • Treatment Benefit for Test – Pts Half that of Test + Pts n std / n targeted 0.64 2.56 0.25 0.89 1.78 0.5 0.98 1.31 0.75 Screened Randomized Proportion Test Positive
  • 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
  • Web Based Software for Comparing Sample Size Requirements
    • http://linus.nci.nih.gov
  • Developmental Strategy (II) Develop Predictor of Response to New Rx Test - Test + To New Rx Control New RX Control New RX
  • 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
  • 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
  • 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
    • 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
  • Analysis Plan B
    • 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.
    • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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.
  •  
  • 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
  • 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
  • Conclusions
    • Moving from correlative science to predictive oncology requires paradigm changes in some aspects of design and analysis of clinical trials
  • Collaborators
    • Boris Freidlin
    • Aboubakar Maitournam
    • Kevin Dobbin
    • Wenu Jiang
    • Yingdong Zhao