Moving from Correlative Science to Predictive Medicine

1. Moving from Correlative Science to Predictive Medicine Richard Simon, D.Sc. Chief, Biometric Research Branch National Cancer Institute http://linus.nci.nih.gov/brb

2. BRB Websitebrb.nci.nih.gov • Powerpoint presentations and audio files • Reprints & Technical Reports • BRB-ArrayTools software • BRB-ArrayTools Data Archive • 100+ published cancer gene expression datasets with clinical annotations • Sample Size Planning for Clinical Trials with Predictive Biomarkers

3. Kinds of Biomarkers • Surrogate endpoint • Pre & post rx, early measure of clinical outcome • Pharmacodynamic • Pre & post rx, measures an effect of rx on disease • Prognostic • Which patients need rx • Predictive • Which patients are likely to benefit from a specific rx • Product characterization For biological rx

4. Surrogate Endpoints • It is extremely difficult to properly validate a biomarker as a surrogate for clinical outcome. • It requires a series of randomized trials with both the biomarker and clinical outcome measured and demonstrating correlated differences • Even the concept of surrogate is dubious because often a large treatment effect on PFS corresponds to a small treatment effect on survival

5. Pharmacodynamic biomarkers used as endpoints in phase I or II studies need not be validated surrogates of clinical outcome • Unvalidated biomarkers can be used for early “futility analyses” in phase III trials

6. Prognostic Biomarkers • Most prognostic factors are not used because they are not therapeutically relevant • Most prognostic factor studies are poorly designed • They are not focused on a clear therapeutic decision context • They use a convenience sample of patients for whom tissue is available. Generally the patients are too heterogeneous to support therapeutically relevant conclusions • They address statistical significance rather than predictive accuracy relative to standard prognostic factors

7. Pusztai et al. The Oncologist 8:252-8, 2003 • 939 articles on “prognostic markers” or “prognostic factors” in breast cancer in past 20 years • ASCO guidelines only recommend routine testing for ER, PR and HER-2 in breast cancer • “With the exception of ER or progesterone receptor expression and HER-2 gene amplification, there are no clinically useful molecular predictors of response to any form of anticancer therapy.”

8. Prognostic Biomarkers Can be Therapeutically Relevant • 3-5% of node negative ER+ breast cancer patients require or benefit from systemic rx other than endocrine rx • Prognostic biomarker development should focus on specific therapeutic decision contexts

9. Key Features of OncotypeDx Development • Identification of important therapeutic decision context • Prognostic marker development was based on patients with node negative ER positive breast cancer receiving tamoxifen as only systemic treatment • Use of patients in NSABP clinical trials • Staged development and validation • Separation of data used for test development from data used for test validation • Development of robust assay with rigorous analytical validation • 21 gene RTPCR assay for FFPE tissue • Quality assurance by single reference laboratory operation

10. Predictive Classifiers • Most cancer treatments benefit only a minority of patients to whom they are administered • Particularly true for molecularly targeted drugs • Being able to predict which patients are likely to benefit would • save patients from unnecessary toxicity, and enhance their chance of receiving a drug that helps them • Help control medical costs • Improve the success rate of clinical drug development

11. Cancers of a primary site are often a heterogeneous grouping of diverse molecular diseases • The molecular diseases vary enormously in their responsiveness to a given treatment • It is feasible (but difficult) to develop prognostic markers that identify which patients need systemic treatment and which have tumors likely to respond to a given treatment • e.g. breast cancer and ER/PR, Her2

12. Conducting a phase III trial in the traditional way with tumors of a specified site/stage/pre-treatment category may • Result in a false negative trial • Unless a sufficiently large proportion of the patients have tumors driven by the targeted pathway • Require a very large number of randomized patients to detect the small average treatment effect

13. Positive results in traditionally designed broad eligibility phase III trials may result in subsequent treatment of many patients who do not benefit

15. Predictive Biomarkers • In the past often studied as un-focused post-hoc subset analyses of RCTs. • Numerous subsets examined • Same data used to define subsets for analysis and for comparing treatments within subsets • Multiple comparisons with no control of type I error • Led to conventional wisdom • Only for hypothesis generation • Only valid if overall treatment difference is significant

17. The Roadmap • Develop a completely specified genomic classifier of the patients likely to benefit from a new drug • Establish analytical and pre-analytical validity of the classifier • Use the completely specified classifier to design and analyze a new clinical trial to evaluate effectiveness of the new treatment with a pre-defined analysis plan that preserves the overall type-I error of the study.

18. Guiding Principle • The data used to develop the classifier must be distinct from the data used to test hypotheses about treatment effect in subsets determined by the classifier • Developmental studies are exploratory • Studies on which treatment effectiveness claims are to be based should be definitive studies that test a treatment hypothesis in a patient population completely pre-specified by the classifier

19. New Drug Developmental Strategy I • Restrict entry to the phase III trial based on the binary predictive classifier, i.e. targeted design

20. 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

21. Applicability of Design I • Primarily for settings where the classifier is based on a single gene whose protein product is the target of the drug • 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

22. Evaluating the Efficiency of Strategy (I) • 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

23. 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 • The targeted design may require fewer or more screened patients than the standard design

24. 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

25. Web Based Software for Comparing Sample Size Requirements • http://linus.nci.nih.gov/brb/

29. Develop Predictor of Response to New Rx Predicted Responsive To New Rx Predicted Non-responsive to New Rx New RX Control New RX Control Developmental Strategy (II)

30. Developmental Strategy (II) • Do not use the diagnostic to restrict eligibility, but to structure a prospective analysis plan • Having a prospective analysis plan is essential • “Stratifying” (balancing) the randomization is useful to ensure that all randomized patients have tissue available but is not a substitute for a prospective analysis plan • The purpose of the study is to evaluate the new treatment overall and for the pre-defined subsets; not to modify or refine the classifier • The purpose is not to demonstrate that repeating the classifier development process on independent data results in the same classifier

31. Validation of EGFR biomarkers for selection of EGFR-TK inhibitor therapy for previously treated NSCLC patients • PFS endpoint • 90% power to detect 50% PFS improvement in FISH+ • 90% power to detect 30% PFS improvement in FISH− • Evaluate EGFR IHC and mutations as predictive markers • Evaluate the role of RAS mutation as a negative predictive marker Outcome FISH + (~ 30%) Erlotinib 2nd line NSCLC with specimen 1° PFS 2° OS, ORR FISH Testing Pemetrexed 1-2 years minimum additional follow-up FISH − (~ 70%) Erlotinib Pemetrexed 4 years accrual, 1196 patients 957 patients

32. Analysis Plan B(Limited confidence in test) • Compare the new drug to the control overall for all patients ignoring the classifier. • If poverall 0.03 claim effectiveness for the eligible population as a whole • Otherwise perform a single subset analysis evaluating the new drug in the classifier + patients • If psubset 0.02 claim effectiveness for the classifier + patients.

33. This analysis strategy is designed to not penalize sponsors for having developed a classifier • It provides sponsors with an incentive to develop genomic classifiers • Incentives are appropriate because developing new drugs with companion diagnostics increases the complexity and cost of the drug development process

34. Analysis Plan C(adaptive) • Test for difference (interaction) between treatment effect in test positive patients and treatment effect in test negative patients • If interaction is significant at level int then compare treatments separately for test positive patients and test negative patients • Otherwise, compare treatments overall

35. Sample Size Planning for Analysis Plan C • 88 events in test + patients needed to detect 50% reduction in hazard at 5% two-sided significance level with 90% power • If 25% of patients are positive, when there are 88 events in positive patients there will be about 264 events in negative patients • 264 events provides 90% power for detecting 33% reduction in hazard at 5% two-sided significance level

36. Simulation Results for Analysis Plan C • Using int=0.10, the interaction test has power 93.7% when there is a 50% reduction in hazard in test positive patients and no treatment effect in test negative patients • A significant interaction and significant treatment effect in test positive patients is obtained in 88% of cases under the above conditions • If the treatment reduces hazard by 33% uniformly, the interaction test is negative and the overall test is significant in 87% of cases

37. Development of Genomic Classifiers • Single gene or protein based on knowledge of therapeutic target • Single gene or protein culled from set of candidate genes identified based on imperfect knowledge of therapeutic target • Empirically determined based on correlating gene expression to patient outcome after treatment • Pusztai, Anderson, Hess. Clin Cancer Res 2007;13:6080

38. Myth • Huge sample sizes are needed to develop effective predictive classifiers

39. Sample Size Planning References • K Dobbin, R Simon. Sample size determination in microarray experiments for class comparison and prognostic classification. Biostatistics 6:27-38, 2005 • K Dobbin, R Simon. Sample size planning for developing classifiers using high dimensional DNA microarray data. Biostatistics (In Press)

40. Sample size as a function of effect size (log-base 2 fold-changebetween classes divided by standard deviation). Two different tolerances shown, . Each class is equally represented in the population. 22000 genes on an array.

41. Development of Genomic Classifiers • During phase II development or • After failed phase III trial using archived specimens. • Adaptively during early portion of phase III trial.

43. Prognostic and Predictive Classifiers for Guiding Use of Approved Drugs

44. Developmental Studies vs Validation Studies • Validation studies use prognostic or predictive biomarkers or composite classifiers that have been completely defined in previous developmental studies

45. Types of Validation for Prognostic and Predictive Biomarkers • Analytical validation • Pre-analytical,analytical, post-analytical robustness • Clinical validation • Does the biomarker predict what it’s supposed to predict for independent data • Not whether independent studies produce the same predictive biomarkers • Clinical utility • Does use of the biomarker result in patient benefit

46. Clinical Utility • Benefits patient by improving treatment decisions • Depends on context of use of the biomarker • Treatment options and practice guidelines • Other prognostic factors

47. Establishing Clinical Utility of a Prognostic Biomarker Classifier • Identify patients for whom • practice standards imply cytotoxic chemotherapy • who have good prognosis without chemotherapy • Prospective trial using pre-defined classifier to identify good risk patients and withhold chemotherapy • TAILORx, MINDACT • Analysis of archived specimens from previous clinical trial in which patients did not receive chemotherapy • Pre-defined classifier • Prospective analysis plan developed before doing assay • Establish analytical and pre/post-analytical validity of assay • Large fraction of patients with adequate archived tissue

48. Establishing Clinical Utility of a Predictive Classifier of Benefit from Regimen T • Randomized trial of treatment with T versus control • Include both test + and test – patients and size trial to evaluate T vs control separately for the two groups of patients • Or include only test – patients if T is an established standard therapy • Prospective trial may not be feasible • “Prospective analysis” of archived specimens from previous trial

49. Myth of “Gold Standard” Design for Establishing Clinical Utility of a Predictive Classifier of Benefit from Regimen T • Randomize patients to whether or not to have classifier measured or to use standard of care • Standard of care group receive T and don’t have classifier measured • Patients randomized to have classifier measured • If test + (ie predicted to benefit from T) receive T • If test - receive control regimen C • Very inefficient • many patients get same treatment regardless of randomized arm • Since classifier is not measured in SOC arm, the trial must be huge to detect very small overall difference in outcome

50. Microarray Myths • That the greatest challenge is managing the mass of microarray data