Statistical Issues in Incorporating and Testing

Biomarkers in Phase III Clinical Trials

FDA/Industry Workshop; September 29, 2006

Daniel Sargent, PhDSumithra Mandrekar, PhD

Division of Biostatistics, Mayo ClinicL Collette, EORTC

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What are we testing

• A (novel) therapeutic whose efficacy is predicted by a marker?

• A marker proposed to predict the efficacy of an (existing) therapeutic?

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Preliminary information

Methods & feasibility ofmeasurement of the marker

in the target populationSpecificity to the cancer of interest

Cut point for classificationPrevalence of marker expression

in the target populationProperties as a prognostic marker

(in absence of treatment orWith non targeted std agent)

Expected marker predictive effect

Endpoint of interest

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Phase II/III Trials

Patient Selection for targeted therapies

• Test the recommended dose on patients who are most likely to respond based on their molecular expression levels

• May result in a large savings of patients (Simon & Maitournam, CCR 2004)

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Trials in targeted populations

• Gains in efficiency depend on marker prevalence and relative efficacy in marker + and marker - patients

Prevalence Relative Efficacy

Efficiency Gain

25% 0% 16x

25% 50% 2.5x50% 0% 4x50% 50% 1.8x75% 0% 1.8x75% 50% 1.3x

(Simon & Maitournam, CCR 2004)

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Phase II/III TrialsDesigns for Targeted Trials

May use standard approaches. Possible Issues• Could lead to negative trials when the

agent could have possible “clinical benefit”, since precise mechanism of action is unknown

• Could miss efficacy in other patients• Inability to test association of the biologic

endpoints with clinical outcomes in a Phase II setting

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Targeted TrialsAdditional considerations• Not always obvious as to who is likely to

respond - often identified only after testing on all patients

• Slower accrual, and need to screen all patients anyway

• Need real time method for assessing patients who are / are not likely to respond

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Example: C-225 in colon cancer

• Early trials mandated EGRF expression • (Saltz, JCO 2004, Cunningham, NEJM 2004)

• Response rate did not correlate with expression level (Cunningham, NEJM 2004)

• Faint: RR 21%• Weak or Moderate: RR 25%• Strong: RR 23%

• Case series demonstrates no correlation between expression and response

• (Chung, JCO 2005)

• Currently indicated only in patients with EGFR expressing tumors, but most current studies do not require EGFR expression

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Design of Tumor Marker Studies

• Current staging and risk-stratification methods incompletely predict prognosis or treatment efficacy

• New therapeutic options emerging• Optimizing and individualizing therapy is

becoming increasingly desirable• Very few potential biological markers are

developed to the point of allowing reliable use in clinical practice

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Prognostic MarkerSingle trait or signature of traits that separates different populations with respect to the risk of an outcome of interest in absence of treatment or despite non targeted “standard” treatment

PrognosticNo treatment or

Standard, non targeted treatment

Marker +

Marker –

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Predictive Marker

Single trait or signature of traits that separates different populations with respect to the outcome of interest in response to a particular (targeted) treatment

PredictiveNo treatment or Standard

Marker +

Marker –

Targeted Treatment

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Prognostic marker Series of patients with standard treatment

Predictive Markers Randomized Clinical Trials

Validation

Designs?

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Randomized Trials• Trials to assess clinical usefulness of

predictive markers – i.e., does use of the marker result in a clinical benefit of a therapy

• Upfront stratification for the marker status before randomization

• Randomize and use a marker-based strategy to compare outcome between marker-based arm with non-marker based arm Sargent et al, JCO 2005

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Register Test Marker

Marker Level (-)

Randomize

Treatment A

Marker Level (+)

Treatment B

Sargent et al., JCO 2005

Design I: upfront Stratification

Randomize

Treatment A

Treatment B

Power trial separately withinmarker groups

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Approach I: Separate Tests

Marker -

Marker +

R

R

Test marker

Treatment A (Std)

Treatment B (New)

Treatment A (Std)

Treatment B (New)

Statistical testWith power

Statistical testWith power

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Approach II: Interaction

Marker -

Marker +

R

R

Test marker

Treatment A (Std)

Treatment B (New)

Treatment A (Std)

Treatment B (New)

Statistical testWith power

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Marker-based strategy design

M -

M +

RTest marker

Treatment A

Treatment B

Marker-Based

Strategy

Non MarkerBased

Strategy

Treatment A

StatisticalTest with

Power

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Register

Marker Based Strategy

Non Marker Based Strategy

Randomize

Treatment A

Treatment B

Marker Level (-)

Treatment A

Marker Level (+) Treatment B

Test Marker

Sargent et al., JCO 2005

Design II: Marker Based Strategy

Randomize

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Median OS Irinotecan/Oxaliplatin

(IO)

Irinotecan/5-FU/L

TS low(50%) 16 months

20 months

TS high(50%) 14 months 12

months

HR: 1.25

Sample Size Interaction Design

HR: 0.86

844 †

1705 †

2223†2756†

HR: 0.691220 †

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Sample size: Strategy Design

TS -

TS +

IFL (20 mo)

IO (14 mo)

Marker-Based

Strategy

Non MarkerBased

Strategy

IFL (15 mo)

IO (15 mo)R 15 mo

16.5 mo

HR0.91R4629

†

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Discussion

• Sample Size • Typically large, especially if the

marker effect size is modest• Depends on many factors such as

• The marker prevalence in the target population

• The baseline risk in the unselected population receiving standard treatment

• The expected treatment difference in all marker groups

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Conclusions• The Marker Based Strategy design is

preferable whenever more than one treatment are involved or when the treatment choice is based on a panel of markers

• That design generally requires more patients than the Interaction design

• The marker is also prognostic • Dilution (marker + patients receive the targeted

therapy in the randomized non marker based group)

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Conclusions

• In the case of a single marker and two treatments, Interaction Design preferable

• Separate Tests versus Interaction ?• Depends on strength of evidence needed for the

marker effect and sample size• Whenever the interaction HR is larger than any of the

treatment HRs (generally qualitative interaction) the interaction approach demands less patients

• A partial Separate Tests approach may be useful whenever no treatment difference is expected in one of the marker groups