Correlative Studies in Clinical TrialsCorrelative Studies in Clinical Trials

Janet E. Dancey, MD

ObjectivesObjectives

• To define biomarker and correlative study• To describe the types of biomarkers• To understand the issues in designing

appropriate biomarker studies• To understand the roles of biomarkers in

phase I, II and III studies• To understand different biomarker trial

designs

What is a Biomarker?What is a Biomarker?

• Biomarker– A characteristic that is objectively measured and evaluated as an

indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention

Biomarkers Definitions working Group National Institutes of Health 2001

• Assay– A method for determining the presence or quantity of a component

• Test– A procedure that makes use of an assay for a particular purpose

Good biomarkers ≠ Good Assays ≠ Tests

What is a Correlative Study?What is a Correlative Study?

• A type of study that tests for a relationship between a condition and a potential causal factor of the condition

• Addresses the question:– What is the correlation between the X and Y?

• Cancer and obesity• QoL score and treatment• Response and Performance status• Prognosis and KRAS

plan2005.cancer.gov/glossary.html

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Types of Clinical BiomarkersTypes of Clinical Biomarkers

Pre-Pre-diagnosisdiagnosis

Pre-Pre-treatmenttreatment

Intra- Intra- treatmenttreatment

Post-Post-treatmenttreatment

DiagnosisDiagnosis

•RiskRisk•ScreeningScreening•Early Early detectiondetection

•PrognosticPrognostic•PredictivePredictive

•Early Early response response or futilityor futility

•Toxicity Toxicity monitoringmonitoring

•Early Early endpointendpoint

•Recurrence/Recurrence/progression progression monitoringmonitoring

•ConfirmationConfirmation•StagingStaging•SubtypingSubtyping

Biomarkers and Correlative StudiesBiomarkers and Correlative Studies

• Biomarker is what is measured• Correlative study is what is correlations are made

between the biomarker and something else• In recent years many biospecimen based biomarkers

have been incorporated into cancer therapeutic trials• Principles of design, measurement, data collection

and analysis are the same.– Eg hypothesis, trial design, measurement, collection,

analysis

Case Study – A Novel Targeted AgentCase Study – A Novel Targeted Agent You have been following the preclinical and clinical research.

It is your opinion that the development of a class of anticancer drugs could be facilitated by incorporating laboratory and imaging correlates early into drug development.

The sponsor wants investigators to develop a trial to rapidly translate exciting results seen in animal models into the clinic and that incorporates laboratory and imaging studies that would define the optimal dose and predict activity of this novel agent.

What do you do?What do you do?

Get the Right TeamGet the Right Team

• Scientist, • Clinician, • Pathologist, • Radiologist,• Trialist,• Statistician,

• Who is important?– CRAs– Treating nurses– Booking team– Pathology technicians – Scanning technicians

Who is critical? The patients!

Have the Right Core FacilitiesHave the Right Core Facilities

• Clinical trial intervention• Imaging• Pathology• Specimen management• Data management

Have the Right ToolsHave the Right Tools

• SOPs and training,• Protocol and consent forms,• Case report forms,• Reagents, technologies for analysis• Q/C• Etc, etc, etc

Know the limitations of Team, Cores and Tools!

Considerations for BiomarkersConsiderations for Biomarkers

• What is the question? (is it a good one?)• Biomarker(s)• Assay/technology• Specimen• Study/Trial Design• Study Execution – Trial and Biomarker (s)• Study Outcome – Trial and Biomarker(s)• Likely Impact• Economically and practically feasible

Biomarker, Assay, SpecimenBiomarker, Assay, SpecimenBiomarker is relevant to

the intervention outcome

Assay reliably measures biomarker in the specimen

Specimen is reasonable for measuring the biomarker

Integrating Biomarkers into Clinical Integrating Biomarkers into Clinical Research: BiomarkerResearch: Biomarker

• Why this/these biomarker(s)? – What is the question/purpose?

• (Is there one?)• (Is it an important one?)

– Determining potential “value-added”

• Rationale and supporting data– Laboratory experimentation– relevance to disease and/or therapy– Clinical evaluation– prevalence and significance in normal and cancer patients

Integrating Biomarkers into Clinical Integrating Biomarkers into Clinical Research: SamplesResearch: Samples

• Will the Samples Be Adequate?– Consider:

• Type, Number, Timing, handling, shipping, storage• Demands on patients/clinic/laboratory staff• Technical requirements of the proposed analysis• Likely impact/plan for handling missing/inadequate

specimens• Costs and resources

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Specimen CharacteristicsSpecimen Characteristics• Format

• Serum, FFPE or fresh/frozen tissue, etc.• Biopsy, surgical specimen, etc.

• Collection• Preservation• Storage conditions & time prior to assay

Integrating Biomarkers into Clinical Integrating Biomarkers into Clinical Research: AssayResearch: Assay

• Why this technology?• Analytical/Laboratory Validation (Laboratory performance)

– How well are you measuring the measurand?• Precision / Reproducibility• Method Comparison • LoB, LoD, LoQ• Linearity• Stability

– Clinical Laboratory Standards Institute (CLSI) http://www.nccls.org/– Is it fit for proposed purpose i.e. on the proposed samples/patients?

• Clinical Validation (“Clinical Qualification”)– Does the test have clinical utility?– Does it have added value over standard tests?

• FDA guidance : “Statistical Guidance on Reporting Results from Studies Evaluating Diagnostic Tests” issued in final form in March, 2007, http://www.fda.gov/cdrh/osb/guidance/1620.html

AssayAssay Methods

• Detailed protocol• Reagents or kits• Quantitation method (e.g., manual, image analysis)• Scoring & reporting

• QC procedures & reproducibility assessments• Marker evaluations blinded to patient characteristics

and clinical endpoints• Number and training of persons executing and

reading tests

Intratumoral heterogeneity of carbonic Intratumoral heterogeneity of carbonic anhydrase IX (CAIX) anhydrase IX (CAIX)

Effect of distributional heterogeneity on the analysis of tumor hypoxia based on carbonic anhydrase IX

VV Iakovlev, M Pintilie, A Morrison, et al Laboratory Investigation (2007) 87, 1206–1217

a) Immunoperoxidase staining for CAIX in a single tissue section. Analysis of the entire section gave a value of 10.8% CAIX labeling. The circles limit the analysis to 0.6 mm simulated tissue microarray (TMA) cores, and show a wide range in CAIX (for publication purpose only, the image was digitally enhanced to better visualize CAIX areas).

Copyright ©2005 American Association for Cancer Research

Baker, A. F. et al. Clin Cancer Res 2005;11:4338-4340

pSer473-Akt antibody in human gastroesophageal tumors and HT-29 colon cancer xenografts measured by

immunohistochemical staining.

A, patient tumor samples. 1 and 2 are two surgically resected specimens and 3 and 4 are two biopsy specimens. B, HT-29 human tumor xenografts excised from scid mice and kept at room temperature for the times shown. Each section also includes in the upper right-hand quadrant an on-slide control of HT-29 colon cancer cells stained for pSer473-Akt.

Integrating Biomarkers into Clinical Integrating Biomarkers into Clinical Research: SamplesResearch: Samples

• Will the samples be adequate?– Consider:

• Type, Number, Timing, handling, shipping, storage• Demands on patients/clinic/laboratory staff• Technical requirements of the proposed analysis• Likely impact/plan for handling missing/inadequate

specimens• Costs and resources

• Can the question still be answered?

ConsiderationsConsiderations• Flow of patients through study

• Reasons for patient/specimen dropout

• Numbers and events

• Number of test outcomes at each analysis stage and in each subgroup & missing values

Biomarkers in Clinical Research: UsesBiomarkers in Clinical Research: Uses

• Phase I – proof of target inhibition after reaching biologically active dose/concentration

• Phase II – predictive marker assessment after identifying promising level of activity; studies should be larger and randomized

• Phase III – prospective testing of biomarker and treatment

• Phase IV – prospective testing of biomarker and treatment

Markers and Endpoints for Trials of Markers and Endpoints for Trials of Molecularly Targeted Agents Molecularly Targeted Agents

Pharmacokinetics Blood and tissue levels

Biochemical Modulation of Target

Changes in protein function/phosphorylation

Biochemical Modulation of Target Pathway

Protein phosphorylation/function Expression array

Markers of Biological/Cellular Response

Proliferation, Cell cycle phase, Apoptosis, Angiogenesis, Metabolism

Clinical Response Objective Tumor ResponseProlonged Stable Disease

Clinical Benefit Survival, quality of life

Ph

ase 1

Ph

ase 2

Ph

ase 3

Phase 1 Trials: ConsiderationsPhase 1 Trials: Considerations

• Primary goal: To identify an appropriate dose/schedule for further evaluation

• Design principles:– Maximize safety– Minimize patients treated at biologically inactive doses– Optimize efficiency

• Study population:– Patients for whom no standard therapy

Small patient numbers

HeterogenousRefractoryTumours

Expect target modulation but not anti-tumour activity

Probability of Success in Assessing Probability of Success in Assessing Pharmacodynamic Effect in BiopsiesPharmacodynamic Effect in Biopsies

• It is certainly not 100%, and may be much lower• Analogy to chemical synthesis: yield < 100% at each step• Recognize potential problems and repair ASAP to maximize

probability of success.

Sample acquired?Acquired on time? Time documented?

Handled properly?

Shipped properly?Stored properly?

Assay works?

Bad karma?

P = 0.92 = 0.81, 0.93 = 0.72, 0.95 = 0.53 …

Target correct?

Biomarkers in Phase 1 Trials: UtilityBiomarkers in Phase 1 Trials: Utility

• Proof of mechanism (drug hits proposed target)

• When toxicity may be insufficient to determine active dose/schedule– Unlikely to occur at dose/exposure that affects the target– Due to off target effects and effects on target are uncertain– To target a specific degree of target inhibition to avoid significant

toxicity

• When pharmacokinetics may be insufficient to determine active dose/schedule– Assay lacking– Pharmacokinetics in plasma does not match effect in tissues

Phase 1 Trial Assessment of Target Effects - Phase 1 Trial Assessment of Target Effects - RequirementsRequirements

• Agent with acceptable preclinical activity, toxicology, pharmacology

• Known association of target effect and tumour activity• Well-characterized assay

• % change in target or target level associated with efficacy • Concentration/exposure required for target effect • Time course for effect on target, duration, recovery• Threshold of detection and CV of target measurements• Target effect on tumour vs other tissues (eg PBMC, Skin, Buccal)• Collection, processing, shipping, storage effects known/optimized

• Usual target values and variability in human tissue known• Patients’ tumours have relevant target• Commitment from investigators/patients

• mandatory requirement (just like PK)

Biomarker Studies in Phase 1 TrialsBiomarker Studies in Phase 1 Trials• MGMT activity after O6-benzylguanine

• Friedman H et al J Clin Oncol 16:3570-5, 1998; Spiro et al. Cancer Res 59:2402-10 1999; Dolan et al Clin Cancer Res 8:2519-23, 2002

• 20S proteosome inhibition after bortezomib• Lightcap E et al. Clin Chem 46:673-683, 2000; Adams J, Oncologist 1: 9-16,

2002;

• DCE-MRI after PTK787/valatanib• Galbraith S et al NMR in Biomed 15:132-142, 2002; Morgan, B. et al. J Clin

Oncol; 21:3955-3964 2003;

• S6K inhibition after everolimus• Tanaka C et al J Clin Oncol 26:1596-1602, 2008

• PARP Inhibition after ABT-888• Kinders RJ, et al. Clin Cancer Res. 2008 Nov 1;14(21):6877-85

Biomarkers in Phase II/III Biomarkers in Phase II/III Prognostic versus Predictive MarkersPrognostic versus Predictive Markers

• Prognostic markers provide information about the patient’s cancer outcome – e.g., cytogenetics in acute leukaemia; performance status

in lung cancer• Predictive markers provide information regarding the

probability of benefit or risk from a specific therapy • Markers may correlate with prognosis and treatment

outcome– ER, HER2 in breast carcinoma– KRAS in colorectal carcinoma– MGMT in glioblastoma

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Prognostic MarkerMeasurement associated with clinical outcome in absence of therapy or with application of standard therapy that all patients are likely to receive.

Examples: Oncotype DX, uPA/PAI-1 by ELISA in breast cancer Correlation with outcome not necessarily sufficient to impact clinical

decisions, may suggest targets for therapy

Good prognosis don’t need adjuv. Rx Useful prognostic information?

Hazard ratio = .18

Hazard ratio = .56

Prognostic Marker Study DesignPrognostic Marker Study Design

All patients Marker assay

Marker +

Marker -

Compare clinical outcomes

• Standard statistical methods such as log rank test or Cox PH regression assume that study subjects constitute a random sample.

• If sampling is stratified or based on outcome, prognostic effect estimates can be biased.

MGMT is PrognosticMGMT is Prognostic

• KM Estimates of Overall Survival by MGMT Methylation Status • Hegi et al NEJM 352:997-1003 2005

MGMT is PrognosticMGMT is Prognostic

• Hegi et al NEJM 352:997-1003 2005

Predictive MarkerPredictive Marker

Qualitative interaction• New drug better for M+ (h.r. = 0.44)• Control drug better for M (h.r. = 1.31)• Interaction = 0.44/1.31 = 0.33

Quantitative interaction• New drug better for M+ (h.r. = 0.44)• New drug better for M (h.r. = 0.76)• Interaction = 0.44/0.76 = 0.58

Measurement associated with response or lack of response to a particular therapy.

Examples: ER/PgR for endocrine Rx benefit in breast cancer Statistical wisdom: Test for treatment by marker interaction

MGMT is PredictiveMGMT is Predictive

• KM Estimates of Overall Survival by MGMT Methylation Status and TreatmentHegi et al NEJM 352:997-1003 2005 Hegi et al NEJM 352:997-1003 2005

Trials Designs: Prospective and Retrospective Trials Designs: Prospective and Retrospective Evaluation of BiomarkersEvaluation of Biomarkers

Marker +Histology

Outcome

Marker -

Rx

Prospective Study

Rx Outcome

Histology Rx

Good Outcome

Poor Outcome

Marker +

Marker -

Marker +

Marker -

Retrospective Study

Biomarkers to Select Patients:Biomarkers to Select Patients:Prospective EvaluationProspective Evaluation

• Advantage– Fewest numbers of patients– Study design guaranteed to have sufficient power

to show treatment effect in marker group

• Disadvantage– Must know marker to select patients– Rapid turnaround to determine eligibility

Biomarkers to Select Patients:Biomarkers to Select Patients:Retrospective EvaluationRetrospective Evaluation

• Advantages– Maximize accrual– Need not know the right marker– Allows refinement of marker/assay while trial ongoing– Allows assessment in marker+/- groups

• Disadvantages– Risk of insufficient numbers within marker group(s)

• Prevalence of different marker defined subgroups– Collection of samples compromised

• Incomplete submission, suboptimal handling– Results may not be generalizable

• Bias sampling

Retrospective Analysis with Incomplete Retrospective Analysis with Incomplete Specimen CollectionSpecimen Collection

• RTOG study 86 -10. A randomized study of RT versus RT + TAB in prostate cancer patients.

• To assess its prognostic value, p53 expression was assessed on 129 of 456 patients entered.

• Patients with p53 assessed had similar distributions of treatment and prognostic factors as those who did not have p53 assessed.

• Statistically significant associations were found between the presence of abnormal p53 protein expression and poor outcome.

Data from Grignon et al. J Natl Cancer Inst. 1997;89:158–165.Pajak et al. Arch Pathol Lab Med. 2000;124:1011–1015

Retrospective Analysis with Incomplete Retrospective Analysis with Incomplete Specimen CollectionSpecimen Collection

• The survival of those patients who had a p53 determined on their tumour was statistically worse than those without a p53 determination (P = .03) regardless of the actual p53 assay result.

Data from Grignon et al. J Natl Cancer Inst. 1997;89:158–165.Figure from Pajak et al. Arch Pathol Lab Med. 2000;124:1011–1015

Biomarkers for Phase 2/3: Predictive markersBiomarkers for Phase 2/3: Predictive markers

• Goal: identification of patients likely to benefit (or elimination of those least likely to benefit)

• Considerations:– Drug activity– Treatment effect across patient subsets. – Prevalence of the subset(s) of patients with “sensitive”

disease or at risk for toxicity.– Assay performance i.e failure rate,

sensitivity/specificity/predictive value. – Samples requirements– Trial design to distinguish treatment and prognostic effects

Caveats for Assessing Treatment Effects in Caveats for Assessing Treatment Effects in Biomarker Defined GroupsBiomarker Defined Groups

• Biomarker may define a subgroup with a different prognosis from historical outcome data from trials done in an unselected group

• E.g. ER+ is both prognostic and predictive

– If the outcome with standard treatment is not well defined and/or the outcome of interest is PFS/OS consider a randomized controlled phase 2 design

• If a trial is designed to assess treatment effects in Marker+ and Marker- groups– False positives will dilute effect in marker+ group– False negatives will dilute the apparent differences in treatment effect

between marker defined groups.– Specimen loss or assay failure will increase the sample size– Trial may be 2-4x size of a conventional study

Phase 2 or 3 Trial – Histologically Defined and Phase 2 or 3 Trial – Histologically Defined and Biomarker Defined Patient PopulationsBiomarker Defined Patient Populations

• Trial is designed to assess treatment effects in Marker+ and Marker- groups– NB: need not be prospective stratification

• Marker assessment– Assay failure increases number of patients screened– False results will dilute differences in effects between marker groups

• If negative within marker groups, – Analyze between treatment groups– Analyze for new markers, cut-points etc.

HistologyStage

Initial Selection

Target Tested

Marker +

Marker -

Agent

Control

Agent

Control

Strata Randomize Outcome

Phase 3: Survival (Phase 2:ORR, TTE)

Control

Marker-guided Vs. Randomized DesignRandomize To Use Of Marker Versus No Marker Evaluation

All Patients

Marker DeterminedTreatment

Randomize Treatment

New Drug

New Drug

Control

Predictive Marker Study DesignPredictive Marker Study Design

M+

M−

• Provides direct measure of patient willingness to follow marker-assigned therapy

• Marker guided treatment may be attractive to patients or clinicians• Inefficient compared to completely randomized or randomized block

design

Predictive Marker Study DesignPredictive Marker Study Design

• The randomized designs offer the greatest flexibility to examine multiple markers.

• Require no a priori assumptions, and are more efficient than marker-guided designs.

• False positive, false negatives will make treatment outcomes in marker defined groups appear alike.

• Larger studies

Sample Size Considerations for Predictive Sample Size Considerations for Predictive Marker StudiesMarker Studies

• Testing for an interaction between marker status and treatment

• Power depends on

– Number of EVENTS

– AND their distribution into marker-by-treatment categories

– Magnitude of effect (e.g., ratio of hazard ratios or difference of survival differences)

– Significance level (e.g., usually 0.05)

• Test of interaction typically requires 2-4 times as many events as test for treatment main effect

Power ProblemsPower Problems• Embedding prognostic and predictive questions in

“large” treatment trials– Events sufficient for answering treatment question, may

be insufficient for prognostic or predictive questions in marker defined subgroups

– Specimen retrieval and assay failures exacerbate sample size problem

• If initial trial does not provide definitive answer, may not be able to prospectively test marker question

Overcoming Sample Size LimitationsOvercoming Sample Size Limitations

• Combine over multiple small studies– Patients and assays comparable?– Identify relevant studies?

• Publication bias• Description limited• REMARK reporting guidelines (McShane et al, 2005: BJC, EJC, JCO, JNCI,

NCPO) might help facilitate pooled analyses• Some large prospective marker trials will be needed

– MINDACT– TAILORx– N0723

• Reduce noise in marker measurements to lessen attenuation of marker effects– Assay improvements

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Statistical Analysis Methods

• Methods for calculating or comparing measures of diagnostic accuracy– Sensitivity/specificity– Likelihood ratios– Diagnostic odds ratio– Area under ROC curve

• Methods used to quantify uncertainty in accuracy measures (e.g., 95%)

• Methods for calculating test reproducibility, if any

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Statistical analysis methodsStatistical analysis methods

• Model building & assumptions• Variable selection • Missing data handling• Coding of marker values in analyses (e.g.,

continuous vs. categorized)• Internal or external validation

Barriers to successful inclusion of Barriers to successful inclusion of biomarker studiesbiomarker studies

• Often: add-on studies that aren’t necessary for achieving the (primary) objectives of the clinical trial– Clinical trials are hard to do well– If something is not a primary or secondary objective it is even harder to

do it well

• Clinical trials are not designed to answer the biomarker question• Biomarker studies may use assays that are not well established

– It is almost impossible to learn to do an assay while studying a novel treatment

• “normal” range, relevant cut-points, reproducibility– Too many variables

Biomarkers in Clinical Research: Biomarkers in Clinical Research: ImplicationsImplications

• Compelling BUT• Increased burden on patients/clinical staff• Significant coordination effort required • Significant increased cost over ‘traditional’ treatment trials

– Numbers of patients may be increased and cost/patient will increase

• Qualification, standardization and QA takes time, energy and money

• Have a good question, team of experts, assays, specimens, trial design and analysis

• Consider the feasibility, economics and likely impact

Think about how you will report the Think about how you will report the study before you startstudy before you start

• STARD: STAndards for Reporting of Diagnostic accuracy (Bossuyt et al.)• Published (2003): Clin Chem, Ann Intern Med, Radiol, BMJ,

AJCP, Clin BioChem, Clin Chem Lab Med• Re-published (2003-4): 8 journals

• REMARK: REporting recommendations for tumor MARKer prognostic studies (McShane et al.)• Published (2005): BJC, EJC, JCO, JNCI, NCPO• Re-published (2006): BCRT, Exp Oncol

Additional Designs and IssuesAdditional Designs and Issues

• Sargent D, et al. Clinical Trial Designs for Predictive Marker Validation in Cancer Treatment Trials. J Clin Oncol 23:2020-2027, 2005

• Simon R, Maitournam A. Evaluating the efficiency of targeted designs for randomized clinical trials. Clin Cancer Res; 10: 6759–6763, 2004