Challenges in Moving Towards Predictive Oncology

1. Challenges in Moving Towards Predictive Oncology Richard Simon, D.Sc. Chief, Biometric Research Branch National Cancer Institute http://linus.nci.nih.gov

2. BRB Websitehttp://linus.nci.nih.gov/brb • Powerpoint presentations and audio files • Reprints & Technical Reports • BRB-ArrayTools software • BRB-ArrayTools Data Archive • Sample Size Planning for Targeted Clinical Trials

3. Many 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 efficiency of clinical drug development

5. “Biomarkers” • Surrogate endpoints • A measurement made before and after treatment to determine whether the treatment is working • Prognostic markers • A measurement made before treatment to indicate likely outcome untreated or on standard treatment • Predictive classifiers • A measurement made before treatment to select good patient candidates for the treatment

6. Most prognostic factors are not used because they are not therapeutically relevant • Most prognostic factor studies use a convenience sample of patients for whom tissue is available. Often the patients are too heterogeneous to support therapeutically relevant conclusions • Prognostic factors in focused population can be therapeutically useful • Oncotype DX • Classifiers that predict benefit from specific treatments can be more useful that broad prognostic factors

8. The Roadmap • Develop a completely specified genomic classifier of the patients likely to benefit from a new drug • Establish reproducibility of measurement 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.

9. 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

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

11. 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

12. 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 Herceptin • With substantial biological basis for the classifier, it may be unacceptable ethically to expose classifier negative patients to the new drug • Without strong biological rationale, FDA may have difficulty approving the test

14. Efficiency relative to trial of unselected patients depends on proportion of patients test positive, and effectiveness of 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

15. 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

20. GefitinibIressa • Two negative untargeted randomized trials first line advanced NSCLC • 2130 patients • 10% have EGFR mutations • If only mutation + patients benefit by 20% increase of 1-year survival, then 12,806 patients/arm are needed • For trial targeted to patients with mutations, 138 are needed

21. 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)

22. 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

23. 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 not except that stratification ensures that all randomized patients will have tissue available • 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

24. Analysis Plan A • Compare the new drug to the control for classifier positive patients • If p+>0.05 make no claim of effectiveness • If p+ 0.05 claim effectiveness for the classifier positive patients and • Compare new drug to control for classifier negative patients using 0.05 threshold of significance

25. Sample size for Analysis Plan A • 88 events in classifier + patients needed to detect 50% reduction in hazard at 5% two-sided significance level with 90% power • If 25% of patients are positive, then 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

26. Study-wise false positivity rate is limited to 5% with analysis plan A • It is not necessary or appropriate to require that the treatment vs control difference be significant overall before doing the analysis within subsets

27. Analysis Plan B • Test for 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

28. Sample Size Planning for Analysis Plan B • 88 events in classifier + patients needed to detect 50% reduction in hazard at 5% two-sided significance level with 90% power • If 25% of patients are positive, then 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

29. Simulation Results for Analysis Plan B • 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

30. The Roadmap • Develop a completely specified genomic classifier of the patients likely to benefit from a new drug • Establish reproducibility of measurement 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.

31. 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 • And not closely regulated by FDA • 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

32. Use of Archived Samples • From a non-targeted “negative” clinical trial to develop a binary classifier of a subset thought to benefit from treatment • Test that subset hypothesis in a separate clinical trial • Prospective targeted type I trial • Using archived specimens from a second previously conducted clinical trial

33. Development of Genomic Classifiers • Before phase III trial initiated • After phase III trial using archived specimens • Use classifier in subsequent phase III trial

34. Development of Genomic Classifiers • Single gene or protein based on knowledge of therapeutic target • Empirically determined based on evaluation of a set of candidate classifiers • e.g. EGFR assays • Empirically determined based on genome-wide correlating gene expression or genotype to patient outcome after treatment

35. Developing Composite Genomic Classifiers • Composite classifiers incorporate the contributions of multiple single-gene features • The single gene feature are usually selected based on their value for distinguishing patients likely to respond to the new rx

36. DNA Microarray Technology • Powerful tool for understanding mechanisms and enabling predictive medicine • Challenges ability of biomedical scientists to use effectively to produce biological knowledge or clinical utility • Challenges statisticians with new problems for which existing analysis paradigms are often inapplicable • Excessive hype and skepticism

40. Statistically state-of-the-art integrated software for DNA microarray expression and copy number data analysis Architecture and statistical content by R Simon User interface for use and education of biomedical scientists Extensive built-in gene annotation and linkage to genomic websites Publicly available for non-commercial use Active user list-serve and message board BRB-ArrayTools http://linus.nci.nih.gov/brb

41. BRB-ArrayToolsMay 2007 • 7188 Registered users • 1962 Distinct institutions • 68 Countries • 365 Citations • Registered users • 3951 in US • 565 at NIH • 275 at NCI • 2014 US EDU • 754 US Govt (non NIH) • 3237 Foreign

42. France 270 Canada 269 UK 244 Germany 239 Italy 216 Taiwan 196 Netherlands 177 Korea 168 Japan 153 China 150 Spain 146 Australia 130 India 107 Belgium 83 New Zeland 61 Sweden 50 Singapore 46 Brazil 48 Israel 41 Denmark 40 Countries With Most BRB ArrayTools Registered Users

43. Developmental vs Validation Studies • Developmental studies develop predictive classifiers and provide internal estimates of predictive accuracy • Validation studies should establish medical utility of a classifier previously developed

44. Limitations to Developmental Studies • Sample handling and assay conduct are performed under controlled conditions that do not incorporate real world sources of variability • Small study size limits precision of estimates of predictive accuracy • Predictive accuracy may not reflect clinical utility

45. Validation Studies • Should establish that the classifier is reproducibly measurable and has clinical utility relative to practice standards

46. Types of Clinical Utility • Identify patients whose prognosis is good without cytotoxic chemotherapy • Identify patients who are likely or unlikely to benefit from a specific therapy • Multiple therapeutic options available

47. Oncotype-Dx • Predictive index of distant disease-free survival for node negative ER + patients receiving local therapy plus tamoxifen • Fully specified classifier developed using frozen specimens from NSABP B20 patients • Applied prospectively to frozen specimens from NSABP B14 patients who received Tamoxifen for 5 years • Good risk patients had very good relapse-free survival

48. p<0.0001 338 pts 149 pts 181 pts B-14 Results—Relapse-Free Survival Paik et al, SABCS 2003