Developing a Predictive Biomarker as a Companion Diagnostic for a Molecularly Targeted Cancer Drug

1. Developing a Predictive Biomarker as a Companion Diagnostic for a Molecularly Targeted Cancer Drug Richard Simon, D.Sc. Chief, Biometric Research Branch National Cancer Institute http://brb.nci.nih.gov

2. BRB Websitehttp://brb.nci.nih.gov • Powerpoint presentations • Reprints • BRB-ArrayTools software • Web based Sample Size Planning

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 or are not likely 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. Kinds of Biomarkers • Predictive biomarkers • Measured before treatment to identify who is likely or unlikely to benefit from a particular treatment • Prognostic biomarkers • Measured before treatment to indicate long-term outcome for patients untreated or receiving standard treatment • Marker of disease aggressiveness or disease aggressiveness in context of standard treatment

5. Kinds of Biomarkers • Endpoint • Measured before, during and after treatment to monitor pace of disease and treatment effect • Pharmacodynamic (phase 0-1) • Does drug hit target • Intermediate response (phase 2) • Does drug have anti-tumor effect • Surrogate for clinical outcome (phase 3)

6. Prognostic and Predictive Biomarkers in Oncology • Single gene or protein measurement • Expression of drug target • Activation of pathway • Scalar index or classifier that summarizes expression levels of multiple genes

7. Types of Validation for Prognostic and Predictive Biomarkers • Analytical validation • Accuracy, reproducibility, robustness • Clinical validation • Is the biomarker correlated with a clinical endpoint • Clinical utility • Does use of the biomarker result in patient benefit • By informing treatment decisions • Is it actionable

8. Predictive Biomarkers • In the past often studied as exploratory post-hoc subset analyses of RCTs. • Numerous subsets examined • No pre-specified hypotheses • No control of type I error • Led to conventional wisdom • Only hypothesis generation • Only valid if overall treatment difference is significant

9. Clinical Trials Should Be Science Based • Cancers of a primary site may represent a heterogeneous group of diverse molecular diseases which vary fundamentally with regard to • the oncogenic mutations that cause them, • their responsiveness to specific drugs

10. How Can We Develop New Drugs in a Manner More Consistent With Modern Tumor Biology and ObtainReliable Information About What Regimens Work for What Kinds of Patients?

11. 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

12. 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

13. 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

14. TrastuzumabHerceptin • Pivotal phase III trial in metastatic breast cancer randomized 468 patients whose tumors over-expressed Her2 • results were highly statistically significant for survival • If eligibility had not been restricted to Her2 + patients, overall improvement in survival would have been only about 3.3% (assuming only the 25% Her2+ patients would have benefited) • 8000+ patients would have been required for an unrestricted trial

15. Applicability of Targeted 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 • e.g. PARP inhibitors • Biological rationale and phase II data provide basis for regulatory approval of the test

16. Evaluating the Efficiency of Targeted 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

17. 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

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

19. The targeted design was not used for development of anti-EGFR antibodies in colorectal cancer and would not have been successful • EGFR over-expression was viewed as the most relevant candidate predictive biomarker but EGFR expression level was not used as an eligibility criteria • Later, based on data independent of the pivotal randomized trials, it was observed that patients with KRAS mutations did not respond to EGFR antibodies • Tumor blocks were retrieved for patients on the randomized pivotal trials to evaluate the KRAS hypothesis

20. DevelopPredictor of Response to New Rx Predicted Responsive To New Rx Predicted Non-responsive to New Rx New RX Control New RX Control Stratification Design

21. Stratification Design • Use the test to structure a prospective specified primary 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

22. R Simon. Using genomics in clinical trial design, Clinical Cancer Research 14:5984-93, 2008 • R Simon. Designs and adaptive analysis plans for pivotal clinical trials of therapeutics and companion diagnostics, Expert Opinion in Medical Diagnostics 2:721-29, 2008

23. 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.

26. “Barring breakthroughs in patient selection methods or a windfall of imatinibs, the ‘all comers’ clinical development approach remains a valid – if frustrating and expensive – route to drug approval.” • A Kamb, S Wee, C Lengauer; Nature Rev Drug Discovery 6:115,2007

27. Gene Expression Signatures as Predictive Biomarkers • Developed de-novo based on phase II trials of a new regimen comparing expression profiles of responders to non-responders • Approximately 20 responders and 20+ non-responders required for clinical samples to detect 2-fold difference in gene expression with 90% power at significance level 0.001 • Dobbin & Simon, Biostatistics 6:27-38, 2005.

28. Gene Expression Signatures as Predictive Biomarkers • Developed using human tumor cell lines • comparing expression profiles of human tumor cell lines responsive to the drug to those for cell lines not responsive • Signatures of pathway activation • Difficulty confirming publications claiming promising reports with established chemotherapeutic agents • Potti et al. Nature Med 12:1294,2006 • Lee et al. PNAS 104:13086,2007 • Coombes & Baggerly Nature Med 13:1276,2007; JCO 26:1186,2008

29. Using Cell Lines With Candidate Genes • Screen human tumor cell lines of a particular tumor type of interest to find some highly sensitive to the new drug • Sequence candidate genes of the sensitive cell lines to find mutations or amplifications correlated with drug sensitivity • e.g. MET inhibitors and MET amplification

30. Pusztai, et al. Clin Ca Res 13:6080, 2007

33. Two Stage Single Arm Phase 2 Design with K Candidate Binary MarkersR. Simon (unpublished) • Stage 1. • Treat N patients with the new drug • Perform test retrospectively on tissue from responders • Let Rk denote the number of responses in patients positive for test k • Identify the markers k for which Rkr*

34. Stage 2. • Test new patients prospectively. Exclude patients having negative test results for all markers k with Rkr* • Continue accrual until, • for each k with Rkr* • there are at least m patients in stage 2 positive for test k

35. K=10 candidate tests (ie genes) • N=100 • r*=2 • m=20 • Suppose • prevalence of each marker is 10% • response rate for positives for 9 markers is 10% and for 1 marker is 50% • In stage 1 we expect 10 patients positive for each marker, 1 response in marker + patients for each of the poor markers and 5 responses for patients + for the good marker. • In stage 2 we accrue 20 patients positive for the good marker and expect 10 responses • In stage 2 we don’t expect to treat any patients not positive for the good marker • Total accrual 100+20

36. K=10 candidate tests (ie genes) • N=50 • r*=1 • m=20 • Suppose • prevalence of each marker is 10% • response rate for positives for 9 markers is 10% and for 1 marker is 50% • In stage 1 we expect 5 patients positive for each marker, 0 responses for patients + for each of the poor markers and 2.5 responses for patients + for the good marker. • In stage 2 we accrue 20 patients positive for the good marker and expect 10 responses • In stage 2 we don’t expect to treat any patients not positive for the good marker • Total accrual 50+20

38. During Second Stage

40. Information about a predictive biomarker may develop following completion of the pivotal trials • It may be infeasible to conduct a new prospective trial for a previously approved drug • KRAS for anti-EGFR antibodies in colorectal cancer • HER2 for doxorubicin in breast cancer

41. In some cases the benefits of a prospective trial can be closely achieved by the carefully planned use of archived tissue from a previously conducted randomized clinical trial

42. Use of Archived Specimens in Evaluation of Prognostic and Predictive BiomarkersRichard M. Simon, Soonmyung Paik and Daniel F. HayesJNCI (In Press) • Claims of medical utility for prognostic and predictive biomarkers based on analysis of archived tissues can be considered to have either a high or low level of evidence depending on several key factors. • Studies using archived tissues, when conducted under ideal conditions and independently confirmed can provide the highest level of evidence. • Traditional analyses of prognostic or predictive factors, using non analytically validated assays on a convenience sample of tissues and conducted in an exploratory and unfocused manner provide a very low level of evidence for clinical utility.

43. Use of Archived Specimens in Evaluation of Prognostic and Predictive BiomarkersRichard M. Simon, Soonmyung Paik and Daniel F. Hayes JNCI (In Press) • For Level I Evidence: • (i) archived tissue adequate for a successful assay must be available on a sufficiently large number of patients from a phase III trial that the appropriate analyses have adequate statistical power and that the patients included in the evaluation are clearly representative of the patients in the trial. • (ii) The test should be analytically and pre-analytically validated for use with archived tissue. • (iii) The analysis plan for the biomarker evaluation should be completely specified in writing prior to the performance of the biomarker assays on archived tissue and should be focused on evaluation of a single completely defined classifier. • iv) the results from archived specimens should be validated using specimens from a similar, but separate, study.

44. Conclusions • New biotechnology and knowledge of tumor biology provide important opportunities to improve therapeutic decision making and new drug development • The established molecular heterogeneity of human diseases requires the use new approaches to the development and evaluation of therapeutics

45. Conclusions • Some of the conventional wisdom about statistical analysis of clinical trials is not applicable to trials dealing with co-development of drugs and diagnostic • e.g. subset analysis if the overall results are not significant or if an interaction test is not significant or if the randomization was not stratified by the subsetting variable

46. Conclusions • 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 patients? • The objective is to develop and validate key decision-making tools, not omics tests • The key is medical utility of the test, not the technology • The information doesn’t have to be perfect to be much better than what we currently have

47. Conclusions • Co-development of drugs and companion diagnostics increases the complexity of drug development • It does not make drug development simpler, cheaper and quicker • But it may make development more successful and it has great potential value for patients and for the economics of health care

48. Acknowledgements • NCI Biometric Research Branch • Kevin Dobbin • Boris Freidlin • Wenyu Jiang • Aboubakar Maitournam • Yingdong Zhao • Soon Paik, NSABP • Daniel Hayes, U. Michigan