The Use of Predictive Biomarkers in Clinical Trial Design

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The Use of Predictive Biomarkers in Clinical
Trial Design

• Richard Simon, D.Sc.
• Chief, Biometric Research Branch
• National Cancer Institute
• http//brb.nci.nih.gov

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Biometric Research Branch Websitehttp//brb.nci.n
ih.gov

• Powerpoint presentations
• Reprints
• BRB-ArrayTools software
• Web based Sample Size Planning

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Successful Phase III Clinical Trials Require

• Right treatment
• Right patient population
• Right endpoint
• Right control group
• Right sample size

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The Right Patient Population

• Predictive biomarkers
• Measured before treatment to identify who will or
  will not benefit from a particular treatment
• ER, HER2, KRAS
• Prognostic biomarkers
• Measured before treatment to indicate long-term
  outcome for patients receiving standard treatment
• Used to identify who does not require more
  intensive treatment

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Prognostic and Predictive Biomarkers in Oncology

• Single gene or protein measurement
• ER protein expression
• HER2 amplification
• KRAS mutation
• Score or classifier that summarizes expression
  levels of multiple genes
• OncotypeDx recurrence score

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Traditional Approach to Clinical Development a
New Drug

• Small phase II trials to find primary sites where
  the drug appears active
• Phase III trials with broad eligibility to test
  the null hypothesis that a regimen containing the
  new drug is not better than the control treatment
  on average for all randomized patients
• Perform post-hoc subset hypotheses
• but dont believe them

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• The established molecular heterogeneity of human
  cancer requires the use new approaches to the
  development and evaluation of therapeutics
• The current approach to evaluating predictive
  markers as post-hoc analyses of phase III trials
  is not an adequate basis for developing a
  reliable science based predictive oncology

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How Can We Develop New Drugs in a Manner More
Consistent With Modern Tumor Biology and
ObtainReliable Information About What Regimens
Work for What Kinds of Patients?
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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

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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
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Targeted Design

• Primarily for settings where the mechanism of
  action is well understood
• eg trastuzumab
• With a strong biological basis for the test, it
  may be unacceptable to expose test negative
  patients to the new drug
• Analytical validation, biological rationale and
  phase II data provide basis for regulatory
  approval of the test

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Evaluating the Efficiency of Targeted Design

• Simon R and Maitnourim A. Evaluating the
  efficiency of targeted designs for randomized
  clinical trials. Clinical Cancer Research
  106759-63, 2004 Correction and supplement
  123229, 2006
• Maitnourim A and Simon R. On the efficiency of
  targeted clinical trials. Statistics in Medicine
  24329-339, 2005.
• reprints and interactive sample size calculations
  at http//linus.nci.nih.gov

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Web Based Software for Designing RCT of Drug and
Predictive Biomarker

• http//brb.nci.nih.gov

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Objections and Risks to Using the Targeted Design

• We wont know whether test negative patients
  might also benefit
• That can be studies in a subsequent trial if the
  drug is effective for the test positive patients
• Restricting eligibility to test positive patients
  forces one to size the trial with adequate power
  for the test positive patients and avoids
  objections that the test positive subset should
  not be looked at unless the results are
  significant overall

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Objections and Risks to Using the Targeted Design

• We may have the wrong predictive biomarker and
  using it to restrict eligibility may make it more
  difficult to use archived specimens for later
  analysis with other candidate biomarkers
• e.g. cetuximab and panitumumab in advanced
  colorectal cancer
• There can be a delicate balance between
  protecting patients whom we do not expect to
  benefit from the new treatment and the desire to
  protect ourselves against having the wrong
  biomarker
• Phase II results in test negative and test
  positive patients can help us decide whether the
  targeted design is appropriate

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Biomarker Stratified Design
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• Do not use the diagnostic to restrict
  eligibility, but to structure a prospective
  analysis plan
• Having a prospective analysis plan is essential
• The analysis plan should protect the overall type
  I error without requiring that the overall
  average effect to be significant as a
  justification for evaluating the treatment effect
  in the test positive subset
• 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

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• R Simon. Using genomics in clinical trial design,
  Clinical Cancer Research 145984-93, 2008
• R Simon. Designs and adaptive analysis plans for
  pivotal clinical trials of therapeutics and
  companion diagnostics, Expert Opinion in Medical
  Diagnostics 2721-29, 2008

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Fallback Analysis Plan

• 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.

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It is difficult to have the right completely
defined predictive biomarker identified and
analytically validated by the time the phase III
trial is ready to start accrual

• Adaptive methods for the selection, refinement
  and evaluation of predictive biomarkers in the
  pivotal trials in a prospectively defined and
  non-exploratory manner

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Multiple Biomarker Design

• Have identified K candidate binary classifiers B1
  , , BK thought to be predictive of patients
  likely to benefit from T relative to C
• RCT comparing new treatment T to control C
• Eligibility not restricted by candidate
  classifiers

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• Compare the new drug to the control overall for
  all patients ignoring the classifiers
• If poverall? 0.03 claim effectiveness for the
  eligible population as a whole
• Otherwise

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• Compute statistical significance test of T vs C
  restricted to patients positive for Bk for each k
  
• Let k be the test for which the treatment effect
  is most significant when restricted to test
  positive patients
• Let S be the treatment effect for patients
  positive for test k
• Obtain the null distribution of S by randomly
  permuting the treatment labels and repeating the
  analysis of finding the subset with the largest
  treatment effect
• Evaluate whether S is significant at the 0.02
  level of this null distribution

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Measure marker
Start treatment E
Measure marker response B
Randomize to E or C Stratified by B
Biomarker Stratified Run-In Design
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Acknowledgements

• Boris Freidlin
• Yingdong Zhao
• Aboubakar Maitournam
• Wenyu Jiang