Predictive Biomarkers and Their Use in Clinical Trial Design

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Predictive Biomarkers and Their Use in Clinical
Trial Design

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

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BRB Websitehttp//linus.nci.nih.gov

• Powerpoint presentations and audio files
• Reprints Technical Reports
• BRB-ArrayTools software
• BRB-ArrayTools Data Archive
• Sample Size Planning for Targeted Clinical Trials

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• Many cancer treatments benefit only a small
  proportion of the patients to whom they are
  administered
• Many early stage patients dont need systemic
  treatment
• Many tumors are not sensitive to the drugs
  administered
• Targeting treatment to the right patients
• Benefits patients
• May reduce health care costs
• May improve the success rate of clinical
  development

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• Conducting a phase III trial in the traditional
  way with tumors of a specified site/stage/pre-trea
  tment category may result in a false negative
  trial
• Unless a sufficiently large proportion of the
  patients have tumors driven by the targeted
  pathway

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• Positive results in traditionally designed broad
  eligibility phase III trials may result in
  subsequent treatment of many patients who do not
  benefit

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Biomarkers

• Surrogate endpoints
• A measurement made before and after treatment to
  determine whether the treatment is working
• Prognostic markers
• A measurement made before treatment to indicate
  long-term outcome for patients untreated or
  receiving standard treatment
• Predictive classifiers
• A measurement made before treatment to select
  good patient candidates for the specific
  treatment

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Surrogate Endpoints

• It is very difficult to properly validate a
  biomarker as a surrogate for clinical outcome. It
  requires a series of randomized trials with both
  the candidate biomarker and clinical outcome
  measured
• Must demonstrate that treatment vs control
  differences for the candidate surrogate are
  concordant with the treatment vs control
  differences for clinical outcome
• It is not sufficient to demonstrate that the
  biomarker responders survive longer than the
  biomarker non-responders

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• Biomarkers for use as endpoints in phase I or II
  studies need not be validated as surrogates for
  clinical outcome
• Unvalidated biomarkers can also be used for early
  futility analyses in phase III trials

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Prognostic Factors

• Most prognostic factors are not used because they
  are not therapeutically relevant
• Most prognostic factor studies use a convenience
  sample of patients for whom tissue is available.
  Often the patients are too heterogeneous to
  support therapeutically relevant conclusions
• Prognostic factors in a focused population can be
  therapeutically useful
• Oncotype DX

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ValidationFit for Purpose

• FDA terminology of valid biomarker and
  probable valid biomarker are not applicable to
  predictive classifiers
• Validation has meaning only as fitness for
  purpose and the purpose of predictive classifiers
  are completely different than for surrogate
  endpoints

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The Roadmap

1. Develop a completely specified predictive
   classifier of the patients likely to benefit from
   a new drug
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 with a
   pre-defined analysis plan.

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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
• 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

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Predictive Classifier

• Based on biological measurements of one or more
  genes, transcripts, or protein products
• If multivariate, includes a specified form for
  combining measurements of components to provide a
  binary prediction
• Weights and cut-off for positivity specified

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Predictive Index

• Based on biological measurements of one or more
  genes, transcripts, or protein products
• If multivariate, includes a specified form for
  combining measurements of components to provide a
  multi-level or quantitative index
• Weights specified

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Development of Genomic Classifiers

• Single gene or protein based on knowledge of
  therapeutic target
• Indicates whether drug can inhibit targeted gene
  or protein and whether tumor progression is
  driven by the targeted pathway
• Empirically determined based on evaluation of a
  set of candidate genes or assays
• e.g. EGFR assays
• Empirically determined based on genome-wide
  correlating gene expression to response

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Developing Predictive Classifiers

• During phase II development or
• After failed phase III trial using archived
  specimens.
• Adaptively during early portion of phase III
  trial.

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Developing Predictive Classifiers

• To predict response from new drug using response
  data for single arm phase II trials
• To predict non-response from control regimen
  using response data for control treated patients
• To predict preferential response or delayed
  progression from randomized phase II (or phase
  III) trial data of new drug vs control

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New Drug Developmental Strategy (I)

• Develop a predictive classifier that identifies
  the patients likely to benefit from the new drug
• Develop a reproducible assay for the classifier
• Use the classifier to restrict eligibility to a
  prospectively planned evaluation of the new drug
• Demonstrate that the new drug is effective in the
  prospectively defined set of patients 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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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 a strong biological basis for the
  classifier, it may be unacceptable to expose
  classifier negative patients to the new drug
• Without strong biological basis or adequate phase
  II data, FDA may have difficulty approving the
  test based on this phase III design

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We dont think that this drug will help you
because your tumor is test negative. But we need
to show the FDA that a drug we dont think will
help test negative patients actually doesnt
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Evaluating the Efficiency of Strategy (I)

• 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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Compared two Clinical Trial Designs

• Standard design
• Randomized comparison of T to C without screening
  or selection using classifier
• Targeted design
• Obtain tissue and evaluate classifier on
  candidate patients
• Randomize only classifier patients
• Classifier patients not further studied

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• Efficiency of targeted design relative to
  standard 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
• The targeted design may require fewer or more
  screened patients than the standard design

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No treatment Benefit for Assay - Patientsnstd /
ntargeted
Proportion Assay Positive Randomized Screened
0.75 1.78 1.33
0.5 4 2
0.25 16 4
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Treatment Benefit for Assay Pts Half that of
Assay Pts nstd / ntargeted
Proportion Assay Positive Randomized Screened
0.75 1.31 0.98
0.5 1.78 0.89
0.25 2.56 0.64
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Trastuzumab

• Metastatic breast cancer
• 234 randomized patients per arm
• 90 power for 13.5 improvement in 1-year
  survival over 67 baseline at 2-sided .05 level
• If benefit were limited to the 25 assay
  patients, overall improvement in survival would
  have been 3.375
• 4025 patients/arm would have been required
• If assay patients benefited half as much, 627
  patients per arm would have been required

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Comparison of Targeted to Untargeted DesignSimon
R, Development and Validation of Biomarker
Classifiers for Treatment Selection, JSPI
Treatment Hazard Ratio for Marker Positive Patients Number of Events for Targeted Design Number of Events for Traditional Design Number of Events for Traditional Design Number of Events for Traditional Design
Percent of Patients Marker Positive Percent of Patients Marker Positive Percent of Patients Marker Positive
20 33 50
0.5 74 2040 720 316

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Web Based Software for Comparing Sample Size
Requirements

• http//linus.nci.nih.gov

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Developmental Strategy (II)
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Developmental Strategy (II)

• Do not use the diagnostic to restrict
  eligibility, but to structure a prospective
  analysis plan
• Having a prospective analysis plan is essential
• Stratifying (balancing) the randomization is
  not sufficient but 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

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Analysis Plan A (confidence in classifier)

• Compare the new drug to the control for
  classifier positive patients
• If pgt0.05 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

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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

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• Study-wise false positivity rate is limited to 5
  with analysis plan A
• It is not necessary or appropriate to require
  that the treatment vs control difference be
  significant overall before doing the analysis
  within subsets

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Analysis Plan B(confidence in overall effect)

• 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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• This analysis strategy is designed to not
  penalize sponsors for having developed a
  classifier
• It provides sponsors with an incentive to develop
  genomic classifiers

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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, 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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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

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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, 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

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Simulation Results for Analysis Plan C

• Using ?int0.10, the interaction test has power
  93.7 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 under the above
  conditions
• If the treatment reduces hazard by 33 uniformly,
  the interaction test is negative and the overall
  test is significant in 87 of cases

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Web Based Software for Designing Stratified
Trials Using Predictive Biomarkers

• http//linus.nci.nih.gov

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The Roadmap

1. Develop a completely specified genomic classifier
   of the patients likely to benefit from a new drug
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 with a
   pre-defined analysis plan.

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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
• Developmental studies are exploratory
• And not closely regulated by FDA
• 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

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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?

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Use of Archived Samples

• Develop a binary classifier of the patients most
  likely to benefit from the new treatment using
  archived specimens from a negative phase III
  clinical trial
• Evaluate the new treatment compared to control
  treatment in the classifier positive subset in a
  separate clinical trial
• Prospective targeted type I trial
• Using archived specimens from a second previously
  conducted clinical trial

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Biomarker Adaptive Threshold Design

• Wenyu Jiang, Boris Freidlin Richard Simon
• JNCI 991036-43, 2007

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Biomarker Adaptive Threshold Design

• Randomized phase III trial comparing new
  treatment E to control C
• Survival or DFS endpoint

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Biomarker Adaptive Threshold Design

• Have identified a predictive index B thought to
  be predictive of patients likely to benefit from
  E relative to C
• Eligibility not restricted by biomarker
• No threshold for biomarker determined

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Analysis Plan

• S(b)log likelihood ratio statistic for treatment
  versus control comparison in subset of patients
  with B?b
• Compute S(b) for all possible threshold values
• Determine TmaxS(b)
• Compute null distribution of T by permuting
  treatment labels
• Permute the labels of which patients are in which
  treatment group
• Re-analyze to determine T for permuted data
• Repeat for 10,000 permutations

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• If the data value of T is significant at 0.05
  level using the permutation null distribution of
  T, then reject null hypothesis that E is
  ineffective
• Compute point and bootstrap confidence interval
  estimates of the threshold b

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Adaptive Biomarker Threshold Design

• Sample size planning methods described by Jiang,
  Freidlin and Simon, JNCI 991036-43, 2007

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Adaptive Signature Design An adaptive design for
generating and prospectively testing a gene
expression signature for sensitive patients

• Boris Freidlin and Richard Simon
• Clinical Cancer Research 117872-8, 2005

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Adaptive Signature DesignEnd of Trial Analysis

• Compare E to C for all patients at significance
  level 0.03
• If overall H0 is rejected, then claim
  effectiveness of E for eligible patients
• Otherwise

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• Otherwise
• Using only the first half of patients accrued
  during the trial, develop a binary classifier
  that predicts the subset of patients most likely
  to benefit from the new treatment E compared to
  control C
• Compare E to C for patients accrued in second
  stage who are predicted responsive to E based on
  classifier
• Perform test at significance level 0.02
• If H0 is rejected, claim effectiveness of E for
  subset defined by classifier

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Treatment effect restricted to subset.10 of
patients sensitive, 10 sensitivity genes, 10,000
genes, 400 patients.
Test Power
Overall .05 level test 46.7
Overall .04 level test 43.1
Sensitive subset .01 level test (performed only when overall .04 level test is negative) 42.2
Overall adaptive signature design 85.3
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Conclusions

• New technology makes it increasingly feasible to
  identify which patients are likely or unlikely to
  benefit from a specified treatment
• Targeting treatment can benefit patients, reduce
  health care costs and improve the success rate of
  new drug development

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Conclusions

• Some of the conventional wisdom about
  biomarkers, how to develop predictive
  classifiers and how to use them in clinical
  trials is seriously flawed
• Prospectively specified analysis plans for phase
  III studies are essential to achieve reliable
  results
• Biomarker analysis does not mean exploratory
  analysis except in developmental studies

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Conclusions

• Achieving the potential of new technology
  requires paradigm changes in correlative
  science and in important aspects of design and
  analysis of clinical trials

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Collaborators

• Boris Freidlin
• Aboubakar Maitournam
• Kevin Dobbin
• Wenu Jiang
• Yingdong Zhao