Moving from Correlative Studies to Predictive Medicine

1. Moving from Correlative Studies 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 Targeted Clinical Trials

3. Objectives of Phase I Trials • Develop dose/schedule • Determine whether the drug inhibits the targeted pathway

4. Dose/Schedule • Ideal is to have a drug and target so specific for cancer cells that the drug can be delivered repeatedly at doses that completely shut down the de-regulated pathway without toxicity to normal cells • Because most current targets are not specific to cancer cells, most targeted drugs are toxic

5. Dose/Schedule • Few examples of drugs whose effectiveness at inhibiting target decreases with dose after maximum • Titrating dose for maximum inhibition of target is difficult due to assay variability and need for tumor biopsies • Titrating dose to plasma concentration at which target is inhibited in pre-clinical systems is more feasible

6. Dose/Schedule • Determining dose just below MTD which can be delivered repeatedly is often the most appropriate and practical approach • Accrue an additional cohort of patients at that selected dose to determine whether the target is inhibited

7. Objectives of Phase II Trials of Targeted Agents • Determine whether there is a population of patients for whom the drug demonstrates sufficient anti-tumor activity to warrant a phase III trial • Optimize the regimen in which the drug will be used in the phase III trial • Optimize the target population for the phase III trial

8. Traditional Phase II Trials • Estimate the proportion of tumors that shrink by 50% or more when the drug is administered either singly or in combination to patients with advanced stage tumors of a specific primary site

9. Using Time to Progression as Endpoint in Phase II Trials • Requires comparison to distribution of progression times for patients not receiving drug • Proportion of patients without progression at a specified time also requires comparison for evaluation • Historical control vs randomized comparison • Phase 2.5 trial design

10. Phase 2.5 Trial Design • Simon R et al. Clinical trial designs for the early clinical development of therapeutic cancer vaccines. Journal of Clinical Oncology 19:1848-54, 2001 • Korn EL et al. Clinical trial designs for cytostatic agents: Are new approaches needed? Journal of Clinical Oncology 19:265-272, 2001

11. Phase 2.5 Trial Design • Randomization to chemotherapy alone or with new drug • Endpoint is progression free survival regardless of whether it is a validated surrogate of survival • One-sided significance level can exceed .05 for analysis and sample size planning

12. Total Sample SizeRandomized Phase 2.52 years accrual, 1.5 years followup

13. Randomized Discontinuation Design (RDD) • The RDD starts all patients on the drug • Patients with early progression go off study • Patients with objective response continue on the drug • Other patients are randomized to continue the drug or stop administration and be observed • PFS from time of randomization is the endpoint

14. Randomized Discontinuation Design (RDD) • The RDD can facilitate observing an effect of the drug on PFS compared to a standard randomized phase 2 design • when many patients have tumors that are insensitive to the drug and rapidly progressive • The RDD generally requires a large total sample size • The RDD is not a phase III trial because it does not establish the clinical utility of administering the drug to the patient compared to not administering it

19. 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

20. 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 candidate biomarker and clinical outcome measured

21. Cardiac Arrhythmia Supression Trial • Ventricular premature beats was proposed as a surrogate for survival • Antiarrythmic drugs supressed ventricular premature beats but killed patients at approximately 2.5 times that of placebo

22. Biomarkers for use as endpoints in phase I or II studies need not be validated as surrogates for clinical outcome • Phase I and II studies should not change practice • Unvalidated biomarkers can be used for early “futility analyses” in phase III trials

23. 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.”

24. Prognostic Markers • Most prognostic factors are not used because they are not therapeutically relevant • Most prognostic factor studies are poorly designed and not focused on a clear objective; they use a convenience sample of patients for whom tissue is available. Generally the patients are too heterogeneous to support therapeutically relevant conclusions

25. 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 context

26. 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

27. 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 • No control of type I error • Led to conventional wisdom • Only hypothesis generation • Only valid if overall treatment difference is significant

28. 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

29. The standard approach to designing phase III clinical trials is based on three assumptions • Qualitative treatment by subset interactions are unlikely • “Costs” of over-treatment are less than “costs” of under-treatment • It is not feasible to reliably evaluate treatments for subsets

30. Qualitative treatment by subset interactions are unlikely • Biology has shown that this is often false • “Costs” of over-treatment are less than “costs” of under-treatment • With today’s drugs this is economically unsustainable • It is not feasible to reliably evaluate treatments for subsets • With molecularly targeted treatment, and prospectively defined candidate subsets, this is feasible

31. Standard Clinical Trial Approaches • Have led to widespread over-treatment of patients with drugs to which few benefit • Possible failure to appreciate the effectiveness of some drugs in biologically restricted target populations

32. This is not a plea for acceptance of the typical unreliable post-hoc data dredging approach to subset analysis • Subset analysis does not have to be about post-hoc comparing treatments in numerous subsets with no control of overall type I error

34. 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.

35. 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

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

37. 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

38. 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

39. 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

40. 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

41. No treatment Benefit for Assay - Patientsnstd / ntargeted

42. Treatment Benefit for Assay – Pts Half that of Assay + Pts nstd / ntargeted

43. Comparison of Targeted to Untargeted DesignSimon R,Development and Validation of Biomarker Classifiers for Treatment Selection, JSPI

44. 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

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

50. 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)