Use of Prognostic & Predictive Genomic Biomarkers in Clinical Trial Design

1. Use of Prognostic & Predictive Genomic 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 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

5. Different Kinds of Biomarkers • Endpoint • Measured before, during and after treatment to monitor pace of disease and treatment effect • Pharmacodynamic • Does drug hit target • Intermediate • Does drug have anti-tumor effect • Conditional surrogate • Phase II • Futility analysis in phase III • Patient management • Surrogate for clinical outcome

6. 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 • Demonstration of concordance in treatment effects and evaluation of quantitative relationship between treatment effect on surrogate vs treatment effect on clinical outcome

7. Intermediate Endpoints in Phase I and II Trials • Biomarkers used as endpoints in phase I or phase II studies need not be validated surrogates of clinical outcome • The purposes of phase I and phase II trials are to determine whether to perform a phase III trial, and if so, with what dose, schedule, regimen and on what population of patients • Claims of treatment effectiveness should be based on phase III results

8. Different 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

9. 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 • Disease classification

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

11. 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 recommended routine testing for ER, PR and HER-2 in breast cancer

12. Most prognostic markers or prognostic models are not used because although they correlate with a clinical endpoint, they do not facilitate therapeutic decision making; • Most prognostic marker studies are based on a “convenience sample” of heterogeneous patients, often not limited by stage or treatment. • The studies are not planned or analyzed with clear focus on an intended use of the marker • Retrospective studies of prognostic markers should be planned and analyzed with specific focus on intended use of the marker • Prospective studies should address medical utility for a specific intended use of the biomarker • Treatment options and practice guidelines • Other prognostic factors

13. OncotypeDx as a Model for Development of a Therapeutically Relevant Gene Expression Signature • <10% of node negative ER+ breast cancer patients require or benefit from the cytotoxic chemotherapy that they receive • Identify patients with node negative ER+ breast cancer who have low risk of recurrence on tamoxifen alone

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

15. Key Features of OncotypeDx Development • Focus on important therapeutic decision context • Staged development and validation • Separation of data used for test development from data used for test validation • Development of robust analytically validated assay

16. Potential Uses of a Prognostic Biomarker • 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

17. Predictive Biomarkers

20. 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

21. 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

22. 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

23. 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

24. Architect – R Simon Developer – Emmes Corporation Contains wide range of analysis tools that I have selected Designed for use by biomedical scientists Imports data from all gene expression and copy-number platforms Automated import of data from NCBI Gene Express Omnibus Highly computationally efficient Extensive annotations for identified genes Integrated analysis of expression data, copy number data, pathway data and data other biological data BRB-ArrayTools

25. Classifiers Diagonal linear discriminant Compound covariate Bayesian compound covariate Support vector machine with inner product kernel K-nearest neighbor Nearest centroid Shrunken centroid (PAM) Random forrest Tree of binary classifiers for k-classes Survival risk-group Supervised pc’s With clinical covariates Cross-validated K-M curves Predict quantitative trait LARS, LASSO Feature selection options Univariate t/F statistic Hierarchical random variance model Restricted by fold effect Univariate classification power Recursive feature elimination Top-scoring pairs Validation methods Split-sample LOOCV Repeated k-fold CV .632+ bootstrap Permutational statistical significance Predictive Classifiers in BRB-ArrayTools

26. BRB-ArrayToolsJune 2009 • 10,000+ Registered users • 68 Countries • 1000+ Citations

27. Acknowledgements • NCI Biometric Research Branch • Kevin Dobbin • Alain Dupuy • Boris Freidlin • Wenyu Jiang • Aboubakar Maitournam • Michael Radmacher • Jyothi Subramarian • George Wright • Yingdong Zhao • BRB-ArrayTools Development Team • Soon Paik, NSABP • Daniel Hayes, U. Michigan