Statistical Issues in Incorporating and Testing Biomarkers in Phase III Clinical Trials

1. Statistical Issues in Incorporating and Testing Biomarkers in Phase III Clinical Trials FDA/Industry Workshop; September 29, 2006 Daniel Sargent, PhD Sumithra Mandrekar, PhD Division of Biostatistics, Mayo Clinic L Collette, EORTC

2. 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?

3. Preliminary information Methods & feasibility of measurement of the marker in the target population Specificity to the cancer of interest Cut point for classification Prevalence of marker expression in the target population Properties as a prognostic marker (in absence of treatment or With non targeted std agent) Expected marker predictive effect Endpoint of interest

4. 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)

5. Trials in targeted populations • Gains in efficiency depend on marker prevalence and relative efficacy in marker + and marker - patients (Simon & Maitournam, CCR 2004)

6. Phase II/III Trials Designs 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

7. Targeted Trials Additional 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

8. 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

9. 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

10. Prognostic Marker Single 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 Prognostic No treatment or Standard, non targeted treatment Marker + Marker –

11. 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 Predictive No treatment or Standard Targeted Treatment Marker + Marker –

12. Validation Prognostic marker Series of patients with standard treatment Designs? Predictive Markers Randomized Clinical Trials

13. 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

14. Design I: upfront Stratification Treatment A Marker Level (-) Randomize Treatment B Register Test Marker Treatment A Marker Level (+) Randomize Power trial separately within marker groups Treatment B Sargent et al., JCO 2005

15. Approach I: Separate Tests Treatment A (Std) Statistical test With power R Marker - Treatment B (New) Test marker Treatment A (Std) Statistical test With power R Marker + Treatment B (New)

16. Approach II: Interaction Treatment A (Std) Statistical test With power R Marker - Treatment B (New) Test marker Treatment A (Std) R Marker + Treatment B (New)

17. Marker-based strategy design M - Marker- Based Strategy Statistical Test with Power Treatment A M + Treatment B Test marker R Non Marker Based Strategy Treatment A

18. Design II: Marker Based Strategy Marker Level (-) Treatment A Marker Based Strategy Marker Level (+) Treatment B Register Randomize Test Marker Treatment A Non Marker Based Strategy Randomize Treatment B Sargent et al., JCO 2005

19. Sample Size Interaction Design HR: 1.25 844 † 1220 † HR: 0.69 HR: 0.86 1705 † 2223† 2756†

20. Sample size: Strategy Design TS - Marker- Based Strategy IFL (20 mo) 16.5 mo TS + IO (14 mo) HR 0.91 4629 † R Non Marker Based Strategy IFL (15 mo) R 15 mo IO (15 mo)

21. 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

22. 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)

23. 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