Use of Prognostic & Predictive Biomarkers in Clinical Trial Design

1. Use of Prognostic & Predictive Biomarkers in Clinical Trial Design Richard Simon, D.Sc. Chief, Biometric Research Branch National Cancer Institute http://brb.nci.nih.gov

2. BRB Websitebrb.nci.nih.gov • Powerpoint presentations • Reprints • BRB-ArrayTools software • Data archive • Q/A message board • Web based Sample Size Planning • Clinical Trials • Optimal 2-stage phase II designs • Phase III designs using predictive biomarkers • Phase II/III designs • Development of gene expression based predictive classifiers

3. Prognostic & Predictive Biomarkers • Most cancer treatments benefit only a minority of patients to whom they are administered • Being able to predict which patients are likely or unlikely to benefit would • Save patients from unnecessary toxicity, and enhance their chance of receiving a drug that helps them • Control medical costs • Improve the success rate of clinical drug development

4. Predictive biomarkers • Measured before treatment to identify who will or will not benefit from a particular treatment • ER, HER2, KRAS • Prognostic biomarkers • Measured before treatment to indicate long-term outcome for patients untreated or receiving standard treatment • Only have medical utility if therapeutically relevant • Used to identify who does or does not require more intensive than standard treatment • OncotypeDx

5. Prognostic and Predictive Biomarkers in Oncology • Single gene or protein measurement • Scalar index or classifier that summarizes expression levels of multiple genes

6. Prognostic Factors in Oncology • Many prognostic factors are not used because they are not actionable • Most prognostic factor studies are not conducted with an intended use • They use a convenience sample of heterogeneous patients for whom tissue is available • Retrospective studies of prognostic markers should be planned and analyzed with specific focus on intended use of the marker • Design of prospective studies depends on context of use of the biomarker • Treatment options and practice guidelines • Other prognostic factors

7. Clinical Utility • Biomarker benefits patient by improving treatment decisions • Identify patients who have very good prognosis on standard treatment and do not require more intensive regimens • Identify patients who have poor prognosis on standard chemotherapy who are good candidates for experimental regimens

8. Prospective Evaluation of Prognostic Biomarker • Identify low stage patients for whom standard of care is chemotherapy • Find dataset of low stage patients who did not receive chemotherapy for whom archived tissue is available • Develop prognostic classifier of risk without chemotherapy of low stage patients • If the classifier identifies a group with a very low risk of recurrence in the absence of chemotherapy then: • Conduct RCT in which low stage patients who are low risk by biomarker classifier arerandomized to +- chemotherapy

9. If the predicted risk of recurrence is sufficiently low, then randomization may be omitted • The test of the biomarker is a test of whether the risk is as low as predicted • Absolute benefit of very low risk patients is by necessity very small • This is the approach of TAILORx

10. How Does This Approach Compare to the So Called Gold Standard of Randomizing Patients to Receive or Not Receive the Test?

11. Prospective Marker Strategy Design • Patients are randomized to either • have marker measured and treatment determined based on marker result and clinical features • don’t have marker measured and receive standard of care treatment based on clinical features alone

12. Randomize Patients to Test or No Test Rx Determined by Test Rx Determined By SOC

13. Marker Strategy Design • Inefficient • Many patients get the same treatment regardless of which arm they are randomized to • Uninformative • Since patients in the standard of care arm do not have the marker measured, it is not possible to compare outcome for patients whose treatment is changed based on the marker result

14. Apply Test to All Eligible Patients Using phase II data, develop predictor of response to new drug Test Deterimined Rx Different From SOC Test Determined Rx Same as SOC Off Study Use Test Determined Rx Use SOC

15. MINDACT randomizes breast cancer patients whose Mammaprint based Rx differs from SOC • Trial is sized to estimate risk of relapse of low risk Mammaprint patients randomized to no chemotherapy

16. Predictive Biomarkers

19. Cancers of a primary site are in many cases a molecularly heterogeneous group of diseases which vary enormously in their responsiveness to treatment, particularly molecularly targeted treatment • Can we develop new drugs in a manner more consistent with modern tumor biology and obtain reliable information about what regimens work for what kinds of tumors?

20. Evaluating a predictive biomarker for treatment T involves an RCT of T versus a control C. • Analysis of RCT determines whether the biomarker distinguishes the patients who benefit from T vs C from those who don’t • In this RCT, the biomarker should ideally be • completely specified in advance • focused on the single specific biomarker • the trial sized with sufficient marker + and marker – patients for adequately powered separate analysis of T vs C differences in each stratum. • Evaluating a predictive biomarker does not involve comparison of outcome of marker + vs marker – patient

22. Prospective Co-Development of Drugs and Companion Diagnostics • Develop a completely specified genomic classifier of the patients likely to benefit from a new drug • Establish analytical validity of the classifier • Use the completely specified classifier in the primary analysis plan of a phase III trial of the new drug

23. Guiding Principle • The data used to develop the classifier should be distinct from the data used to test hypotheses about treatment effect in subsets determined by the classifier • Developmental studies can be exploratory • Studies on which treatment effectiveness claims are to be based should not be exploratory

24. Develop Predictor of Response to New Drug Using phase II data, develop predictor of response to new drug Patient Predicted Responsive Patient Predicted Non-Responsive Off Study New Drug Control

25. Applicability of Targeted/Enrichment Design • Primarily for settings where the classifier is based on a single gene whose protein product is the target of the drug or the biology seems well understood • eg trastuzumab • With a strong biological basis for the classifier, it may be unacceptable to expose classifier negative patients to the new drug • Analytical validation, biological rationale and phase II data provide basis for regulatory approval of the test • Phase III study focused on test + patients to provide data for approving the drug

26. Principle • If a drug is found safe and effective in a defined (test +) patient population, approval should not depend on finding the drug ineffective in some other (test -) population

27. Evaluating the Efficiency of Enrichment Design • Simon R and Maitnourim A. Evaluating the efficiency of targeted designs for randomized clinical trials. Clinical Cancer Research 10:6759-63, 2004; Correction and supplement 12:3229, 2006 • Maitnourim A and Simon R. On the efficiency of targeted clinical trials. Statistics in Medicine 24:329-339, 2005. • reprints and interactive sample size calculations at http://linus.nci.nih.gov

28. Relative efficiency of targeted design depends on • proportion of patients test positive • effectiveness of new drug (compared to control) for test negative patients • When less than half of patients are test positive and the drug has little or no benefit for test negative patients, the targeted design requires dramatically fewer randomized patients

29. TrastuzumabHerceptin • Metastatic breast cancer • 234 randomized patients per arm • 90% power for 13.5% improvement in 1-year survival over 67% baseline at 2-sided .05 level • If benefit were limited to the 25% assay + patients, overall improvement in survival would have been 3.375% • 4025 patients/arm would have been required

31. Model for Two Treatments With Binary Response • Molecularly targeted treatment T • Control treatment C • 1- Proportion of patients that express target • pc control response probability • response probability for T patients who express target (R+) is (pc + 1) • Response probability for T patients who do not express target (R-) is (pc + 0)

34. Randomized Ratio(normal approximation) • RandRat = nuntargeted/ntargeted • 1= rx effect in marker + patients • 0= rx effect in marker - patients •  =proportion of marker - patients • If 0=0, RandRat = 1/ (1-) 2 • If 0= 1/2, RandRat = 1/(1- /2)2

35. Randomized Rationuntargeted/ntargeted

36. Screened Ratio • Nuntargeted = nuntargeted • Ntargeted =ntargeted/(1-) • ScreenRat = Nuntargeted/Ntargeted=(1- )RandRat

37. Screened Ratio

38. Decomposing Specificity of Treatment Effect from Accuracy of Test • RandRat = nuntargeted/ntargeted

39. Randomized Ratiosensitivity=specificity=0.9

40. Screened Ratio • Nuntargeted = nuntargeted

41. Screened Ratiosensitivity=specificity=0.9

42. Web Based Software for Designing RCT of Drug and Predictive Biomarker • http://brb.nci.nih.gov

46. It can be very difficult to develop an effective and analytically validated predictive biomarker prior to launch of the phase III trial • Even for anti-EGFR antibodies, a more effective biomarker turned out to be KRAS mutation, not EGFR expression • For small molecule kinase inhibitors the task is more difficult • In some settings it can be easier to use an analytically validated biomarker of poor outcome on the standard therapy

47. It can be very difficult to develop an effective and analytically validated predictive biomarker prior to launch of the phase III trial • Even for anti-EGFR antibodies, a more effective biomarker turned out to be KRAS mutation, not EGFR expression • For small molecule kinase inhibitors the task is more difficult • In some settings it can be easier to use an analytically validated biomarker of poor outcome on the standard therapy

48. Score function S for distinguishing patients with favorable outcome on standard rx vs those with unfavorable outcome • Developed on training set of pts receiving std rx • GF(s)=CDF of S in favorable pts • GU(s)=CDF of S in unfavorable pts • Computed on test set of pts receiving std rx

49. GU(s)=sensitivity of test for selecting pts with unfavorable outcome on std rx using threshold s • 1-GF(s)=specificity of test • Plot of GU(s) vs GF(s) = ROC curve

50. Latent classes • LC=F • LC=U • Pr[LC=F]= • PrS[Resp=F|LC=F]=p1 • PrS[Resp=F|LC=U]=p0 • PrE[Resp=F|LC=F]=p1 • PrS[Resp=F|LC=U]=p0+