use of prognostic & predictive genomic biomarkers in clinical trial design
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Use of Prognostic & Use of Prognostic & Predictive Genomic Predictive Genomic
Biomarkers in Clinical Biomarkers in Clinical Trial DesignTrial Design
Richard Simon, D.Sc.Richard Simon, D.Sc.Chief, Biometric Research BranchChief, Biometric Research Branch
National Cancer InstituteNational Cancer Institutehttp://brb.nci.nih.govhttp://brb.nci.nih.gov
BRB WebsiteBRB Websitebrb.nci.nih.govbrb.nci.nih.gov
Powerpoint presentationsPowerpoint presentations ReprintsReprints BRB-ArrayTools softwareBRB-ArrayTools software
Data archiveData archive Q/A message boardQ/A message board
Web based Sample Size Planning Web based Sample Size Planning Clinical TrialsClinical Trials
Optimal 2-stage phase II designsOptimal 2-stage phase II designs Phase III designs using predictive biomarkersPhase III designs using predictive biomarkers Phase II/III designsPhase II/III designs
Development of gene expression based predictive Development of gene expression based predictive classifiersclassifiers
Prognostic & Predictive Prognostic & Predictive BiomarkersBiomarkers
Most cancer treatments benefit only a minority Most cancer treatments benefit only a minority of patients to whom they are administeredof patients to whom they are administered
Being able to predict which patients are likely Being able to predict which patients are likely to benefit would to benefit would Save patients from unnecessary toxicity, and Save patients from unnecessary toxicity, and
enhance their chance of receiving a drug that helps enhance their chance of receiving a drug that helps themthem
Control medical costs Control medical costs Improve the success rate of clinical drug Improve the success rate of clinical drug
developmentdevelopment
Different Kinds of Different Kinds of BiomarkersBiomarkers
EndpointEndpoint Measured before, during and after treatment Measured before, during and after treatment
to monitor treatment effectto monitor treatment effect PharmacodynamicPharmacodynamic IntermediateIntermediate
Phase IIPhase II Futility analysis in phase IIIFutility analysis in phase III Patient managementPatient management
Surrogate for clinical outcomeSurrogate for clinical outcome
Surrogate EndpointsSurrogate Endpoints
It is extremely difficult to properly It is extremely difficult to properly validate a biomarker as a surrogate validate a biomarker as a surrogate for clinical outcome. It requires a for clinical outcome. It requires a series of randomized trials with both series of randomized trials with both the candidate biomarker and clinical the candidate biomarker and clinical outcome measuredoutcome measured
Intermediate Endpoints in Intermediate Endpoints in Phase I and II TrialsPhase I and II Trials
Biomarkers used as endpoints in phase I Biomarkers used as endpoints in phase I or phase II studies need not be validated or phase II studies need not be validated surrogates of clinical outcomesurrogates of clinical outcome
The purposes of phase I and phase II The purposes of phase I and phase II trials are to determine whether to trials are to determine whether to perform a phase III trial, and if so, with perform a phase III trial, and if so, with what dose, schedule, regimen and on what dose, schedule, regimen and on what population of patientswhat population of patients Claims of treatment effectiveness should be Claims of treatment effectiveness should be
based on phase III resultsbased on phase III results
Different Kinds of Different Kinds of BiomarkersBiomarkers
Predictive biomarkersPredictive biomarkers Measured before treatment to identify Measured before treatment to identify
who will or will not benefit from a who will or will not benefit from a particular treatmentparticular treatment
Prognostic biomarkersPrognostic biomarkers Measured before treatment to indicate Measured before treatment to indicate
long-term outcome for patients untreated long-term outcome for patients untreated or receiving standard treatmentor receiving standard treatment
Prognostic and Predictive Prognostic and Predictive Biomarkers in OncologyBiomarkers in Oncology
Single gene or protein measurementSingle gene or protein measurement Expression of drug targetExpression of drug target Activation of pathwayActivation of pathway
Scalar index or classifier that Scalar index or classifier that summarizes expression levels of summarizes expression levels of multiple genesmultiple genes
Disease classificationDisease classification
Types of Validation for Types of Validation for Prognostic and Predictive Prognostic and Predictive
BiomarkersBiomarkers Analytical validationAnalytical validation
Accuracy, reproducibility, robustnessAccuracy, reproducibility, robustness Clinical validationClinical validation
Does the biomarker predict a clinical Does the biomarker predict a clinical endpoint or phenotypeendpoint or phenotype
Clinical utilityClinical utility Does use of the biomarker result in Does use of the biomarker result in
patient benefitpatient benefit By informing treatment decisionsBy informing treatment decisions Is it actionableIs it actionable
Pusztai et al. The Oncologist 8:252-8, Pusztai et al. The Oncologist 8:252-8, 20032003
939 articles on “prognostic markers” 939 articles on “prognostic markers” or “prognostic factors” in breast or “prognostic factors” in breast cancer in past 20 yearscancer in past 20 years
ASCO guidelines only recommend ASCO guidelines only recommend routine testing for ER, PR and HER-routine testing for ER, PR and HER-2 in breast cancer2 in breast cancer
Most prognostic markers or prognostic models are not Most prognostic markers or prognostic models are not used because although they correlate with a clinical used because although they correlate with a clinical endpoint, they do not facilitate therapeutic decision endpoint, they do not facilitate therapeutic decision making; i.e. they have no demonstrated medical utilitymaking; i.e. they have no demonstrated medical utility
Most prognostic marker studies are based on a Most prognostic marker studies are based on a “convenience sample” of heterogeneous patients, “convenience sample” of heterogeneous patients, often not limited by stage or treatment. often not limited by stage or treatment.
The studies are not planned or analyzed with clear The studies are not planned or analyzed with clear focus on an intended use of the markerfocus on an intended use of the marker
Retrospective studies of prognostic markers should be Retrospective studies of prognostic markers should be planned and analyzed with specific focus on intended planned and analyzed with specific focus on intended use of the markeruse of the marker
Design of prospective studies depends on context of Design of prospective studies depends on context of use of the biomarkeruse of the biomarker Treatment options and practice guidelinesTreatment options and practice guidelines Other prognostic factorsOther prognostic factors
OncotypeDx as a Model for OncotypeDx as a Model for Development of a Development of a
Therapeutically Relevant Gene Therapeutically Relevant Gene Expression SignatureExpression Signature
<10% of node negative ER+ breast <10% of node negative ER+ breast cancer patients require or benefit from cancer patients require or benefit from the cytotoxic chemotherapy that they the cytotoxic chemotherapy that they receivereceive
Identify patients with node negative Identify patients with node negative ER+ breast cancer who have low risk of ER+ breast cancer who have low risk of recurrence on tamoxifen alonerecurrence on tamoxifen alone
B-14 Results—Relapse-Free B-14 Results—Relapse-Free SurvivalSurvival
338 pts
149 pts
181 pts
0 2 4 6 8 10 12 14 16Time (yrs)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Relapse-Free Survival
Low R isk (RS < 18) In termediate R isk (R S 18 - 30) H igh R isk (RS 31)
p<0.0001
Paik et al, SABCS 2003
Key Features of OncotypeDx Key Features of OncotypeDx DevelopmentDevelopment
Focus on important therapeutic Focus on important therapeutic decision contextdecision context
Staged development and validationStaged development and validation Separation of data used for test Separation of data used for test
development from data used for test development from data used for test validationvalidation
Development of robust analytically Development of robust analytically validated assayvalidated assay
Potential Uses of a Potential Uses of a Prognostic BiomarkerPrognostic Biomarker
Identify patients who have very good Identify patients who have very good prognosis on standard treatment and prognosis on standard treatment and do not require more intensive do not require more intensive regimens regimens
Identify patients who have poor Identify patients who have poor prognosis on standard chemotherapy prognosis on standard chemotherapy who are good candidates for who are good candidates for experimental regimensexperimental regimens
Predictive BiomarkersPredictive Biomarkers
Predictive BiomarkersPredictive Biomarkers In the past often studied as In the past often studied as
exploratory post-hoc subset analyses exploratory post-hoc subset analyses of RCTs.of RCTs. Numerous subsets examinedNumerous subsets examined No pre-specified hypothesesNo pre-specified hypotheses No control of type I errorNo control of type I error
Led to conventional wisdomLed to conventional wisdom Only hypothesis generationOnly hypothesis generation Only valid if overall treatment difference Only valid if overall treatment difference
is significant is significant
Prospective Co-Prospective Co-Development of Drugs and Development of Drugs and
Companion DiagnosticsCompanion Diagnostics1.1. Develop a completely specified Develop a completely specified
genomic classifier of the patients genomic classifier of the patients likely to benefit from a new druglikely to benefit from a new drug
2.2. Establish analytical validity of the Establish analytical validity of the classifierclassifier
3.3. Use the completely specified Use the completely specified classifier in the primary analysis classifier in the primary analysis plan of a phase III trial of the new plan of a phase III trial of the new drugdrug
Guiding PrincipleGuiding Principle The data used to develop the classifier The data used to develop the classifier
should be distinct from the data used should be distinct from the data used to test hypotheses about treatment to test hypotheses about treatment effect in subsets determined by the effect in subsets determined by the classifierclassifier Developmental studies can be exploratoryDevelopmental studies can be exploratory Studies on which treatment effectiveness Studies on which treatment effectiveness
claims are to be based should not be claims are to be based should not be exploratoryexploratory
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
BRB-ArrayToolsBRB-ArrayTools Architect – R SimonArchitect – R Simon Developer – Emmes CorporationDeveloper – Emmes Corporation
Contains wide range of analysis tools that I have Contains wide range of analysis tools that I have selectedselected
Designed for use by biomedical scientistsDesigned for use by biomedical scientists Imports data from all gene expression and copy-Imports data from all gene expression and copy-
number platformsnumber platforms Automated import of data from NCBI Gene Express Automated import of data from NCBI Gene Express
OmnibusOmnibus Highly computationally efficientHighly computationally efficient Extensive annotations for identified genesExtensive annotations for identified genes Integrated analysis of expression data, copy number Integrated analysis of expression data, copy number
data, pathway data and data other biological datadata, pathway data and data other biological data
Predictive Classifiers in Predictive Classifiers in BRB-ArrayToolsBRB-ArrayTools
ClassifiersClassifiers Diagonal linear discriminantDiagonal linear discriminant Compound covariate Compound covariate Bayesian compound covariateBayesian compound covariate Support vector machine with Support vector machine with
inner product kernelinner product kernel K-nearest neighborK-nearest neighbor Nearest centroidNearest centroid Shrunken centroid (PAM)Shrunken centroid (PAM) Random forrestRandom forrest Tree of binary classifiers for Tree of binary classifiers for
k-classesk-classes Survival risk-groupSurvival risk-group
Supervised pc’sSupervised pc’s With clinical covariatesWith clinical covariates Cross-validated K-M curvesCross-validated K-M curves
Predict quantitative traitPredict quantitative trait LARS, LASSOLARS, LASSO
Feature selection optionsFeature selection options Univariate t/F statisticUnivariate t/F statistic Hierarchical random variance Hierarchical random variance
modelmodel Restricted by fold effectRestricted by fold effect Univariate classification Univariate classification
powerpower Recursive feature eliminationRecursive feature elimination Top-scoring pairsTop-scoring pairs
Validation methodsValidation methods Split-sampleSplit-sample LOOCVLOOCV Repeated k-fold CVRepeated k-fold CV .632+ bootstrap.632+ bootstrap
Permutational statistical Permutational statistical significancesignificance
BRB-ArrayToolsBRB-ArrayToolsJune 2009June 2009
10,000+ Registered users 10,000+ Registered users 68 Countries68 Countries 1000+ Citations1000+ Citations
AcknowledgementsAcknowledgements
NCI Biometric Research BranchNCI Biometric Research Branch Kevin DobbinKevin Dobbin Alain DupuyAlain Dupuy Boris FreidlinBoris Freidlin Wenyu JiangWenyu Jiang Aboubakar MaitournamAboubakar Maitournam Michael RadmacherMichael Radmacher Jyothi SubramarianJyothi Subramarian George WrightGeorge Wright Yingdong ZhaoYingdong Zhao
BRB-ArrayTools Development TeamBRB-ArrayTools Development Team Soon Paik, NSABPSoon Paik, NSABP Daniel Hayes, U. MichiganDaniel Hayes, U. Michigan
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