Use of Prognostic

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Use of Prognostic Predictive Genomic
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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BRB Websitebrb.nci.nih.gov

• Powerpoint presentations
• Reprints
• BRB-ArrayTools software
• Data archive
• Q/A message board
• Web based Sample Size Planning
• Clinical Trials
• Optimal 2-stage phase II designs
• Phase III designs using predictive biomarkers
• Phase II/III designs
• Development of gene expression based predictive
  classifiers

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Prognostic Predictive Biomarkers

• Most cancer treatments benefit only a minority of
  patients to whom they are administered
• Being able to predict which patients are likely
  to benefit would
• Save patients from unnecessary toxicity, and
  enhance their chance of receiving a drug that
  helps them
• Control medical costs
• Improve the success rate of clinical drug
  development

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Different Kinds of Biomarkers

• Endpoint
• Measured before, during and after treatment to
  monitor treatment effect
• Pharmacodynamic
• Intermediate
• Phase II
• Futility analysis in phase III
• Patient management
• Surrogate for clinical outcome

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Surrogate Endpoints

• It is extremely 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

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Intermediate Endpoints in Phase I and II Trials

• Biomarkers used as endpoints in phase I or phase
  II studies need not be validated surrogates of
  clinical outcome
• The purposes of phase I and phase II trials are
  to determine whether to perform a phase III
  trial, and if so, with what dose, schedule,
  regimen and on what population of patients
• Claims of treatment effectiveness should be based
  on phase III results

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Different Kinds of Biomarkers

• Predictive biomarkers
• Measured before treatment to identify who will or
  will not benefit from a particular treatment
• Prognostic biomarkers
• Measured before treatment to indicate long-term
  outcome for patients untreated or receiving
  standard treatment

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Prognostic and Predictive Biomarkers in Oncology

• Single gene or protein measurement
• Expression of drug target
• Activation of pathway
• Scalar index or classifier that summarizes
  expression levels of multiple genes
• Disease classification

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Types of Validation for Prognostic and Predictive
Biomarkers

• Analytical validation
• Accuracy, reproducibility, robustness
• Clinical validation
• Does the biomarker predict a clinical endpoint or
  phenotype
• Clinical utility
• Does use of the biomarker result in patient
  benefit
• By informing treatment decisions
• Is it actionable

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Pusztai et al. The Oncologist 8252-8, 2003

• 939 articles on prognostic markers or
  prognostic factors in breast cancer in past 20
  years
• ASCO guidelines only recommend routine testing
  for ER, PR and HER-2 in breast cancer

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• Most prognostic markers or prognostic models are
  not used because although they correlate with a
  clinical endpoint, they do not facilitate
  therapeutic decision making i.e. they have no
  demonstrated medical utility
• Most prognostic marker studies are based on a
  convenience sample of heterogeneous patients,
  often not limited by stage or treatment.
• The studies are not planned or analyzed with
  clear focus on an intended use of the marker
• Retrospective studies of prognostic markers
  should be planned and analyzed with specific
  focus on intended use of the marker
• Design of prospective studies depends on context
  of use of the biomarker
• Treatment options and practice guidelines
• Other prognostic factors

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OncotypeDx as a Model for Development of a
Therapeutically Relevant Gene Expression Signature

• lt10 of node negative ER breast cancer patients
  require or benefit from the cytotoxic
  chemotherapy that they receive
• Identify patients with node negative ER breast
  cancer who have low risk of recurrence on
  tamoxifen alone

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B-14 ResultsRelapse-Free Survival
Paik et al, SABCS 2003
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Key Features of OncotypeDx Development

• Focus on important therapeutic decision context
• Staged development and validation
• Separation of data used for test development from
  data used for test validation
• Development of robust analytically validated assay

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Potential Uses of a Prognostic Biomarker

• Identify patients who have very good prognosis on
  standard treatment and do not require more
  intensive regimens
• Identify patients who have poor prognosis on
  standard chemotherapy who are good candidates for
  experimental regimens

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Predictive Biomarkers
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Predictive Biomarkers

• In the past often studied as exploratory post-hoc
  subset analyses of RCTs.
• Numerous subsets examined
• No pre-specified hypotheses
• No control of type I error
• Led to conventional wisdom
• Only hypothesis generation
• Only valid if overall treatment difference is
  significant

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Prospective Co-Development of Drugs and Companion
Diagnostics

• Develop a completely specified genomic classifier
  of the patients likely to benefit from a new drug
• Establish analytical validity of the classifier
• Use the completely specified classifier in the
  primary analysis plan of a phase III trial of the
  new drug

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Guiding Principle

• The data used to develop the classifier should be
  distinct from the data used to test hypotheses
  about treatment effect in subsets determined by
  the classifier
• Developmental studies can be exploratory
• Studies on which treatment effectiveness claims
  are to be based should not be exploratory

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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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BRB-ArrayTools

• Architect R Simon
• Developer Emmes Corporation
• Contains wide range of analysis tools that I have
  selected
• Designed for use by biomedical scientists
• Imports data from all gene expression and
  copy-number platforms
• Automated import of data from NCBI Gene Express
  Omnibus
• Highly computationally efficient
• Extensive annotations for identified genes
• Integrated analysis of expression data, copy
  number data, pathway data and data other
  biological data

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Predictive Classifiers in BRB-ArrayTools

• Classifiers
• Diagonal linear discriminant
• Compound covariate
• Bayesian compound covariate
• Support vector machine with inner product kernel
• K-nearest neighbor
• Nearest centroid
• Shrunken centroid (PAM)
• Random forrest
• Tree of binary classifiers for k-classes
• Survival risk-group
• Supervised pcs
• With clinical covariates
• Cross-validated K-M curves
• Predict quantitative trait
• LARS, LASSO

• Feature selection options
• Univariate t/F statistic
• Hierarchical random variance model
• Restricted by fold effect
• Univariate classification power
• Recursive feature elimination
• Top-scoring pairs
• Validation methods
• Split-sample
• LOOCV
• Repeated k-fold CV
• .632 bootstrap
• Permutational statistical significance

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BRB-ArrayToolsJune 2009

• 10,000 Registered users
• 68 Countries
• 1000 Citations

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Acknowledgements

• NCI Biometric Research Branch
• Kevin Dobbin
• Alain Dupuy
• Boris Freidlin
• Wenyu Jiang
• Aboubakar Maitournam
• Michael Radmacher
• Jyothi Subramarian
• George Wright
• Yingdong Zhao
• BRB-ArrayTools Development Team
• Soon Paik, NSABP
• Daniel Hayes, U. Michigan