Development and Useof Predictive BiomarkersDr. Richard Simon

Potential Conflict of Interest

• None

Development and Use of Development and Use of Predictive BiomarkersPredictive Biomarkers

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

Biometric Research Branch Biometric Research Branch WebsiteWebsite

http://http://brb.nci.nih.govbrb.nci.nih.gov

Powerpoint presentationsPowerpoint presentations ReprintsReprints BRB-ArrayTools softwareBRB-ArrayTools software Web based Sample Size Planning Web based Sample Size Planning

Why are Metastatic Tumors Resistant?Why are Metastatic Tumors Resistant?

Poor intracellular drug access in bulky tumorsPoor intracellular drug access in bulky tumors Large tumors have low growth fractionsLarge tumors have low growth fractions Old tumors have undergone many generations Old tumors have undergone many generations

of replication, harbor many mutations and are of replication, harbor many mutations and are mutationally heterogeneousmutationally heterogeneous

Metastatic tumors have survived many Metastatic tumors have survived many selection pressures, activated detoxification selection pressures, activated detoxification pathways and de-activated control pathways pathways and de-activated control pathways like apoptosislike apoptosis

……

How Can We Treat More EffectivelyHow Can We Treat More Effectively

Treat earlyTreat early Treat intensivelyTreat intensively Treat with combinationsTreat with combinations Treat with drugs that target the key oncogenic Treat with drugs that target the key oncogenic

mutations that occurred early, are present in mutations that occurred early, are present in all cells of the tumor, drive the invasion of the all cells of the tumor, drive the invasion of the tumor and to which the tumor is addictedtumor and to which the tumor is addicted

Characterize the key oncogenic mutations in Characterize the key oncogenic mutations in individual tumors and select the right drugs individual tumors and select the right drugs for that tumor for that tumor

Prognostic & Predictive Prognostic & Predictive BiomarkersBiomarkers

Most cancer treatments currently benefit only a Most cancer treatments currently benefit only a minority of patients to whom they are minority of patients to whom they are administeredadministered

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

Help control medical costs Help control medical costs Improve the success rate of clinical drug Improve the success rate of clinical drug

developmentdevelopment

Personalized Oncology is Personalized Oncology is Here TodayHere Today

Estrogen receptor over-expression in Estrogen receptor over-expression in breast cancerbreast cancer Anti-estrogens, aromatase inhibitorsAnti-estrogens, aromatase inhibitors

HER2 amplification in breast cancerHER2 amplification in breast cancer Trastuzumab, LapatinibTrastuzumab, Lapatinib

OncotypeDx in breast cancerOncotypeDx in breast cancer Low score for ER+ node - = hormonal rxLow score for ER+ node - = hormonal rx

KRAS in colorectal cancerKRAS in colorectal cancer WT KRAS = cetuximab or panitumumabWT KRAS = cetuximab or panitumumab

EGFR mutation or amplification in NSCLCEGFR mutation or amplification in NSCLC EGFR inhibitorEGFR inhibitor

Different Kinds of Different Kinds of BiomarkersBiomarkers

Endpoint BiomarkersEndpoint Biomarkers A measurement made on a patient before, during A measurement made on a patient before, during

and after treatment to determine whether the and after treatment to determine whether the treatment is workingtreatment is working

Predictive biomarkersPredictive biomarkers Measured before treatment to identify who will Measured before treatment to identify who will

benefit from a particular treatmentbenefit from a particular treatment Prognostic biomarkersPrognostic biomarkers

Measured before treatment to indicate long-term Measured before treatment to indicate long-term outcome for patients untreated or receiving outcome for patients untreated or receiving standard treatmentstandard treatment

Endpoint BiomarkersEndpoint Biomarkers

Surrogate EndpointsSurrogate Endpoints It is very difficult to properly validate a It is very difficult to properly validate a

biomarker as a surrogate of clinical benefit for biomarker as a surrogate of clinical benefit for use as an alternative endpoint in phase III trialsuse as an alternative endpoint in phase III trials

Partial Surrogate Endpoints Partial Surrogate Endpoints Necessary but not sufficient for clinical benefitNecessary but not sufficient for clinical benefit Pharmacodynamic biomarkers can be useful in Pharmacodynamic biomarkers can be useful in

phase I/II studies as measures of treatment phase I/II studies as measures of treatment effecteffect

They need not be validated as surrogates for They need not be validated as surrogates for clinical benefitclinical benefit

Types of Validation for Types of Validation for Prognostic and Predictive Prognostic and Predictive

BiomarkersBiomarkers Analytical validationAnalytical validation

Measures accurately what it is supposed to Measures accurately what it is supposed to measuremeasure

Clinical validation/correlation Clinical validation/correlation Does the biomarker predict the clinical endpoint Does the biomarker predict the clinical endpoint

that it’s supposed to predict for independent datathat it’s supposed to predict for independent data Medical utilityMedical utility

Does use of the biomarker result in patient Does use of the biomarker result in patient benefitbenefit

Depends on medical context, other prognostic factors, Depends on medical context, other prognostic factors, therapeutic optionstherapeutic options

Prognostic and Predictive Prognostic and Predictive Biomarkers in OncologyBiomarkers in Oncology

Single gene or protein measurementSingle gene or protein measurement ER protein expressionER protein expression HER2 amplificationHER2 amplification KRAS mutationKRAS mutation

Scalar index or classifier that Scalar index or classifier that summarizes expression levels of summarizes expression levels of multiple genesmultiple genes

Prognostic Markers in Prognostic Markers in OncologyOncology

Most prognostic markers are not used Most prognostic markers are not used because they are not therapeutically because they are not therapeutically relevantrelevant Most studies do not address medical utilityMost studies do not address medical utility They use a convenience sample of patients They use a convenience sample of patients

for whom tissue is availablefor whom tissue is available Most prognostic marker studies are not Most prognostic marker studies are not

reliable because they are exploratory reliable because they are exploratory and not prospectively focused on a and not prospectively focused on a single markersingle marker

Prognostic Biomarkers Can Prognostic Biomarkers Can be Therapeutically Relevantbe Therapeutically Relevant

<10% of node negative ER+ breast <10% of node negative ER+ breast cancer patients require or benefit cancer patients require or benefit from the cytotoxic chemotherapy from the cytotoxic chemotherapy that they receivethat they receive

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 16

Time (yrs)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Relap

se-Free S

urvival

Low R isk (R S < 18) Intermediate R isk (R S 18 - 30) H igh R isk (RS 31)

p<0.0001

Paik et al, SABCS 2003

Predictive BiomarkersPredictive Biomarkers

In the past often studied as un-In the past often studied as un-focused post-hoc subset analyses of focused post-hoc subset analyses of RCTs.RCTs. Numerous subsets examinedNumerous subsets examined No pre-specified hypothesesNo pre-specified hypotheses No control of type I error from multiple No control of type I error from multiple

testing testing

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 likely genomic classifier of the patients likely to benefit from a new drugto benefit from a new drug

2.2. Establish analytical validity of the Establish analytical validity of the classifierclassifier

3.3. Use the completely specified classifier Use the completely specified classifier to design and analyze a focused to design and analyze a focused clinical trial to evaluate effectiveness clinical trial to evaluate effectiveness of the new treatment and how it of the new treatment and how it relates to the candidate biomarkerrelates to the candidate biomarker

Guiding PrincipleGuiding Principle

The data used to develop the classifier The data used to develop the classifier should be distinct from the data used to should be distinct from the data used to test hypotheses about treatment effect test hypotheses about treatment effect in subsets determined by the classifierin subsets determined by the classifier 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 be definitive claims are to be based should be definitive studies that test a treatment hypothesis in studies that test a treatment hypothesis in a patient population completely pre-a patient population completely pre-specified by the classifierspecified by the classifier

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

Enrichment Design

Enrichment DesignEnrichment Design

Primarily for settings where the Primarily for settings where the classifier is based on a single gene classifier is based on a single gene whose protein product is the target whose protein product is the target of the drug and the biological of the drug and the biological evidence that the new treatment in evidence that the new treatment in marker negative patients is marker negative patients is compellingcompelling eg Herceptin eg Herceptin

TrastuzumabTrastuzumabHerceptinHerceptin

Metastatic breast cancerMetastatic breast cancer 234 randomized patients per arm234 randomized patients per arm 90% power for 13.5% improvement in 1-90% power for 13.5% improvement in 1-

year survival over 67% baseline at 2-sided year survival over 67% baseline at 2-sided .05 level.05 level

If benefit were limited to the 25% assay + If benefit were limited to the 25% assay + patients, overall improvement in survival patients, overall improvement in survival would have been 3.375%would have been 3.375% 4025 patients/arm would have been required 4025 patients/arm would have been required

Evaluating the Efficiency of Evaluating the Efficiency of Enrichment DesignEnrichment Design

Simon R and Maitnourim A. Evaluating the efficiency of Simon R and Maitnourim A. Evaluating the efficiency of targeted designs for randomized clinical trials. Clinical targeted designs for randomized clinical trials. Clinical Cancer Research 10:6759-63, 2004.Cancer Research 10:6759-63, 2004.

Maitnourim A and Simon R. On the efficiency of Maitnourim A and Simon R. On the efficiency of targeted clinical trials. Statistics in Medicine 24:329-targeted clinical trials. Statistics in Medicine 24:329-339, 2005.339, 2005.

reprints and interactive sample size calculations at reprints and interactive sample size calculations at http://linus.nci.nih.gov/brbhttp://linus.nci.nih.gov/brb

Efficiency of Enrichment Efficiency of Enrichment DesignDesign

Depends on Depends on proportion of patients test positiveproportion of patients test positive effectiveness of new drug for test negative effectiveness of new drug for test negative

patientspatients When less than half of patients are test When less than half of patients are test

positive and the drug has little or no positive and the drug has little or no benefit for test negative patients, the benefit for test negative patients, the enrichment design requires dramatically enrichment design requires dramatically fewer randomized patientsfewer randomized patients

Stratification DesignStratification Design

Develop Predictor of Response to New Rx

Predicted Non-responsive to New Rx

Predicted ResponsiveTo New Rx

ControlNew RX Control

New RX

Does not use the test to restrict eligibility, but to Does not use the test to restrict eligibility, but to structure a prospective analysis planstructure a prospective analysis plan

Having a prospective analysis plan is essentialHaving a prospective analysis plan is essential “ “Stratifying” (balancing) the randomization is useful to Stratifying” (balancing) the randomization is useful to

ensure that all randomized patients have tissue ensure that all randomized patients have tissue available but is not a substitute for a prospective available but is not a substitute for a prospective analysis plananalysis plan

Size the study for adequate evaluation of T vs C Size the study for adequate evaluation of T vs C separately by marker statusseparately by marker status

The purpose of the study is to evaluate the new The purpose of the study is to evaluate the new treatment overall and for the pre-defined subsets; not treatment overall and for the pre-defined subsets; not to modify or refine the classifier to modify or refine the classifier

The purpose is not to demonstrate that repeating the The purpose is not to demonstrate that repeating the classifier development process on independent data classifier development process on independent data results in the same classifierresults in the same classifier

R Simon. Using genomics in clinical trial R Simon. Using genomics in clinical trial design, Clinical Cancer Research 14:5984-design, Clinical Cancer Research 14:5984-93, 200893, 2008

R Simon. Designs and adaptive analysis R Simon. Designs and adaptive analysis plans for pivotal clinical trials of plans for pivotal clinical trials of therapeutics and companion diagnostics, therapeutics and companion diagnostics, Expert Opinion in Medical Diagnostics Expert Opinion in Medical Diagnostics 2:721-29, 20082:721-29, 2008

Web Based Software for Web Based Software for Planning Clinical Trials of Planning Clinical Trials of

Treatments with a Treatments with a Candidate Predictive Candidate Predictive

BiomarkerBiomarker http://brb.nci.nih.gov http://brb.nci.nih.gov

Use of Archived Specimens in Evaluation of Use of Archived Specimens in Evaluation of Prognostic and Predictive BiomarkersPrognostic and Predictive Biomarkers

Richard M. Simon, Soonmyung Paik and Daniel F. HayesRichard M. Simon, Soonmyung Paik and Daniel F. Hayes

Claims of medical utility for prognostic and Claims of medical utility for prognostic and predictive biomarkers based on analysis of archived predictive biomarkers based on analysis of archived tissues can be considered to have either a high or tissues can be considered to have either a high or low level of evidence depending on several key low level of evidence depending on several key factors. factors.

Studies using archived tissues, when conducted Studies using archived tissues, when conducted under ideal conditions and independently confirmed under ideal conditions and independently confirmed can provide the highest level of evidence. can provide the highest level of evidence.

Traditional analyses of prognostic or predictive Traditional analyses of prognostic or predictive factors, using non analytically validated assays on a factors, using non analytically validated assays on a convenience sample of tissues and conducted in an convenience sample of tissues and conducted in an exploratory and unfocused manner provide a very exploratory and unfocused manner provide a very low level of evidence for clinical utility. low level of evidence for clinical utility.

For Level I EvidenceFor Level I Evidence

Archived tissue adequate for a successful assay must be Archived tissue adequate for a successful assay must be available on a sufficiently large number of patients from a available on a sufficiently large number of patients from a phase III trial with a design that enables the appropriate phase III trial with a design that enables the appropriate analyses analyses Adequate statistical powerAdequate statistical power The patients included in the evaluation are clearly The patients included in the evaluation are clearly

representative of the patients in the trial. representative of the patients in the trial. The test should be analytically and pre-analytically The test should be analytically and pre-analytically

validated for use with archived tissue.validated for use with archived tissue. The analysis plan for the biomarker evaluation should be The analysis plan for the biomarker evaluation should be

completely specified in writing prior to the performance of completely specified in writing prior to the performance of the biomarker assays on archived tissue and should be the biomarker assays on archived tissue and should be focused on evaluation of a single completely defined focused on evaluation of a single completely defined classifier.classifier.

The results of the analysis should be validated using The results of the analysis should be validated using specimens from a similar, but separate, studyspecimens from a similar, but separate, study

Development of Development of Prognostic & Predictive Prognostic & Predictive Classifiers using Gene Classifiers using Gene

Expression ProfilesExpression Profiles

Major Flaws Found in 40 Major Flaws Found in 40 Studies Published in 2004Studies Published in 2004

Inadequate control of multiple comparisons in gene Inadequate control of multiple comparisons in gene findingfinding 9/23 studies had unclear or inadequate methods to deal 9/23 studies had unclear or inadequate methods to deal

with false positiveswith false positives 10,000 genes x .05 significance level = 500 false positives10,000 genes x .05 significance level = 500 false positives

Misleading report of prediction accuracyMisleading report of prediction accuracy 12/28 reports based on evaluating accuracy in training set 12/28 reports based on evaluating accuracy in training set

or using incomplete cross-validationor using incomplete cross-validation Misleading use of cluster analysis Misleading use of cluster analysis

13/28 studies invalidly claimed that expression clusters 13/28 studies invalidly claimed that expression clusters based on differentially expressed genes could help based on differentially expressed genes could help distinguish clinical outcomesdistinguish clinical outcomes

50% of studies contained one or more major flaws50% of studies contained one or more major flaws

Recent Literature Review of Recent Literature Review of Expression Profiling in Early Lung Expression Profiling in Early Lung

CancerCancerSimon & SubramanianSimon & Subramanian

Most studies relating gene expression profiles to Most studies relating gene expression profiles to outcome of cancer patients do not address outcome of cancer patients do not address medical utility medical utility The patients included are too heterogeneous with The patients included are too heterogeneous with

regard to stageregard to stage Failure to emphasize predictive accuracy over existing Failure to emphasize predictive accuracy over existing

prognostic factors rather than statistical significanceprognostic factors rather than statistical significance Most publications feature highly misleading Most publications feature highly misleading

claims based on failure to separate the data used claims based on failure to separate the data used for model development from the data used for for model development from the data used for model evaluationmodel evaluation Sample splittingSample splitting

Do not evaluate results in the training set!Do not evaluate results in the training set! Complete cross validationComplete cross validation

New Challenges in Phase II New Challenges in Phase II Trial DesignTrial Design

Evaluating new drugs in molecularly Evaluating new drugs in molecularly heterogeneous diseasesheterogeneous diseases Treating a sufficient number of patients whose Treating a sufficient number of patients whose

tumors are thought to be good candidates for the tumors are thought to be good candidates for the drugdrug

Developing a predictive biomarker for identifying Developing a predictive biomarker for identifying target population and a robust test for use in the target population and a robust test for use in the phase III trialphase III trial

Development of effective combinationsDevelopment of effective combinations Reliable use of endpoints other than Reliable use of endpoints other than

objective responseobjective response

Selecting Patients for Selecting Patients for Phase II TrialPhase II Trial

If the phase II trial for a particular primary site If the phase II trial for a particular primary site is not enriched for patients thought responsive is not enriched for patients thought responsive to the drug, an initial stage of 10-15 patients to the drug, an initial stage of 10-15 patients may contain very few appropriate patientsmay contain very few appropriate patients

If drug target is thought known, accrual of If drug target is thought known, accrual of separate cohort of 25-30 patients whose separate cohort of 25-30 patients whose tumors are thought to be driven by the target tumors are thought to be driven by the target gives best chance to evaluate druggives best chance to evaluate drug

Small phase II trials are generally not Small phase II trials are generally not adequate for developing or even refining adequate for developing or even refining predictive biomarkerspredictive biomarkers

Phase II DesignsPhase II Designs

Single agentSingle agent In combination In combination with active with active

agentsagents

Response Response raterate

Simon Optimal 2-Simon Optimal 2-stage single arm stage single arm designdesign

Single arm Single arm comparison to comparison to historical controlhistorical control

Makuch-SimonMakuch-SimonThall-Simon Thall-Simon BayesianBayesian

Randomized designRandomized design

Time to Time to progressionprogression

Dixon-Simon single Dixon-Simon single arm comparison to arm comparison to historical controlhistorical controlRandomized Randomized designdesign

Randomized designRandomized design

Evaluating a New Drug in Evaluating a New Drug in Combination with Active AgentsCombination with Active Agents

For a new drug in combination with active agents, pFor a new drug in combination with active agents, p00 represents represents the response probability of the active agents without the new drug the response probability of the active agents without the new drug in the same type of patients being selected for the phase II study in the same type of patients being selected for the phase II study of the combination regimenof the combination regimen

The effectiveness of the single arm design is limited by the The effectiveness of the single arm design is limited by the availability of a large number of comparable patients who have availability of a large number of comparable patients who have been treated with the active agents alonebeen treated with the active agents alone

For combination regimens, unless pFor combination regimens, unless p00 is based on a large number of is based on a large number of patients, the methods of Makuch-Simon or Bayesian Thall-Simon patients, the methods of Makuch-Simon or Bayesian Thall-Simon designs should be used instead of the optimal two-stage design. designs should be used instead of the optimal two-stage design.

The Makuch-Simon and Thall-Simon designs require individual The Makuch-Simon and Thall-Simon designs require individual patient data for historical controls. This increases focus on patient data for historical controls. This increases focus on comparability and they take into account the actual number of comparability and they take into account the actual number of historical controls and the resulting uncertainty in phistorical controls and the resulting uncertainty in p00

Using Time to Progression Using Time to Progression or Stable Disease as or Stable Disease as

EndpointEndpoint Requires comparison to progression times for Requires comparison to progression times for

control patients not receiving drugcontrol patients not receiving drug Proportion of patients with “stable disease” Proportion of patients with “stable disease”

also requires a control group for evaluation also requires a control group for evaluation to be meaningfulto be meaningful

It is difficult to reliably evaluate time to It is difficult to reliably evaluate time to progression endpoint without a randomized progression endpoint without a randomized control groupcontrol group

With historical controls, specific controls With historical controls, specific controls should be used for whom comparability of should be used for whom comparability of prognosis and surveillance for progression prognosis and surveillance for progression can be establishedcan be established

Thall-Simon Bayesian Single Arm Thall-Simon Bayesian Single Arm Phase II Designs Using a Specific Phase II Designs Using a Specific Set of Historical Control PatientsSet of Historical Control Patients

Makuch, RW, and Simon, RM.: Sample size Makuch, RW, and Simon, RM.: Sample size considerations for non-randomized comparative considerations for non-randomized comparative studies. J. Chron. Dis. 33: 175-181, 1980.studies. J. Chron. Dis. 33: 175-181, 1980.

Dixon, DO, and Simon, R. Sample size considerations Dixon, DO, and Simon, R. Sample size considerations for studies comparing survival curves using historical for studies comparing survival curves using historical controls. J. Clin. Epidemiology 41: 1209-1214, 1988.controls. J. Clin. Epidemiology 41: 1209-1214, 1988.

Thall, P F and Simon R. A Bayesian approach to Thall, P F and Simon R. A Bayesian approach to establishing sample size and monitoring establishing sample size and monitoring criteria for criteria for phase II clinical trials. Controlled Clinical Trials phase II clinical trials. Controlled Clinical Trials 15:463-481, 1994.15:463-481, 1994.

Thall PF, Simon R, Estey E: A new statistical strategy Thall PF, Simon R, Estey E: A new statistical strategy for monitoring safety and efficacy in single-arm clinical for monitoring safety and efficacy in single-arm clinical trials. Journal of Clinical Oncology 14:296-303, 1996.trials. Journal of Clinical Oncology 14:296-303, 1996.

Randomized Phase II Screening Randomized Phase II Screening DesignsDesigns

Simon, Ellenberg, Wittes Simon, Ellenberg, Wittes Cancer Treatment Reports 69:1375,1985Cancer Treatment Reports 69:1375,1985

For evaluating multiple new drugs, regimens For evaluating multiple new drugs, regimens or combinations to select most promisingor combinations to select most promising Arm with greatest observed response rate is Arm with greatest observed response rate is

selected regardless of how small the difference isselected regardless of how small the difference is Not for comparing a new drug/regimen to controlNot for comparing a new drug/regimen to control

Randomization ensures uniform patient Randomization ensures uniform patient selection and evaluationselection and evaluation

Can be used with time to progression endpointCan be used with time to progression endpoint

Phase 2.5 Trial DesignPhase 2.5 Trial Design

Randomization to new regimen vs controlRandomization to new regimen vs control E.g. C+X vs CE.g. C+X vs C

Endpoint is progression free survival Endpoint is progression free survival regardless of whether it is an accepted phase regardless of whether it is an accepted phase III endpointIII endpoint

Threshold of significance can exceed .05 for Threshold of significance can exceed .05 for sample size planningsample size planning

Simon R et al. Clinical trial designs for the early clinical Simon R et al. Clinical trial designs for the early clinical development of therapeutic cancer vaccines. Journal of Clinical development of therapeutic cancer vaccines. Journal of Clinical Oncology 19:1848-54, 2001Oncology 19:1848-54, 2001

Korn EL et al. Clinical trial designs for cytostatic agents: Are new Korn EL et al. Clinical trial designs for cytostatic agents: Are new approaches needed? Journal of Clinical Oncology 19:265-272, approaches needed? Journal of Clinical Oncology 19:265-272, 20012001

Total Sample SizeTotal Sample SizeRandomized Phase 2.5Randomized Phase 2.5

2 years accrual, 1.5 years followup2 years accrual, 1.5 years followup

ImprovemeImprovement in nt in

median median PFSPFS

Hazard Hazard RatioRatio

=.05=.05 =.10=.10 =.20=.20

4 4 → 6 → 6 monthsmonths

1.51.5 216216 168168 116116

6 6 → 9 → 9 monthsmonths

1.51.5 228228 176176 120120

4 4 → 8 → 8 monthsmonths

22 7676 6060 4040

66→12 →12 monthsmonths

22 8484 6464 4444

AcknowledgementsAcknowledgements

NCI Biometric Research BranchNCI Biometric Research Branch Boris FreidlinBoris Freidlin Yingdong ZhaoYingdong Zhao Alain DupuyAlain Dupuy Wenyu JiangWenyu Jiang Aboubakar MaitournamAboubakar Maitournam Jyothi SubramanianJyothi Subramanian

Soonmyung Paik, NSABPSoonmyung Paik, NSABP Daniel Hayes, U. MichiganDaniel Hayes, U. Michigan

Questions?