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Statistical Issues in the Evaluation of Predictive Biomarkers Richard Simon, D.Sc. Chief, Biometric Research Branch National Cancer Institute

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Presentation on theme: "Statistical Issues in the Evaluation of Predictive Biomarkers Richard Simon, D.Sc. Chief, Biometric Research Branch National Cancer Institute"— Presentation transcript:

1 Statistical Issues in the Evaluation of Predictive Biomarkers Richard Simon, D.Sc. Chief, Biometric Research Branch National Cancer Institute

2 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

3 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

4 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

5 Cancers of a primary site are often a heterogeneous gouping 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

6 “Hypertension is not one single entity, neither is schizophrenia. It is likely that we will find 10 if we are lucky, or 50, if we are not very lucky, different disorders masquerading under the umbrella of hypertension. I don’t see how once we have that knowledge, we are not going to use it to genotype individuals and try to tailor therapies, because if they are that different, then they’re likely fundamentally … different problems…” –George Poste

7 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

8 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

9 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

10 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


12 The Roadmap 1.Develop a completely specified genomic classifier of the patients likely to benefit from a new drug 2.Establish analytical and pre-analytical validity of the classifier 3.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.

13 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

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

15 Using phase II data, develop predictor of response to new drug Develop Predictor of Response to New Drug Patient Predicted Responsive New Drug Control Patient Predicted Non-Responsive Off Study

16 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

17 If a drug is found safe and effective in a defined patient population, approval should not depend on finding the drug ineffective in some other population Consequently, if the drug is found safe and effective in biomarker classifier positive patients, approval of the drug should not be contingent on testing the drug in classifier negative patients

18 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

19 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

20 Trastuzumab Herceptin 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

21 Web Based Software for Comparing Sample Size Requirements

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

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 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

24 Validation of EGFR biomarkers for selection of EGFR-TK inhibitor therapy for previously treated NSCLC patients 2 nd line NSCLC with specimen FISH Testing FISH + (~ 30%) FISH − (~ 70%) Erlotinib Pemetrexed Erlotinib Pemetrexed Outcome 1° PFS 2° OS, ORR 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 957 patients 4 years accrual, 1196 patients 1-2 years minimum additional follow-up

25 Analysis Plan B (Limited confidence in test) Compare the new drug to the control overall for all patients ignoring the classifier. –If p overall  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 p subset  0.02 claim effectiveness for the classifier + patients.

26 This analysis strategy is designed to not penalize sponsors for having developed a classifier It provides sponsors with an incentive to develop genomic classifiers

27 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

28 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

29 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

30 Development of Genomic Classifiers Single gene or protein based on knowledge of therapeutic target Empirically determined based on evaluation of a set of candidate genes Empirically determined based on genome- wide correlating gene expression, copy number variation or genotype to patient outcome after treatment

31 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.


33 Conclusions Neither academic research, industry, NCI or FDA have adequately adapted to the fundamental discoveries of the heterogeneity of human cancers There is great potential for developing treatments that are highly effective for the right patients using prognostic and predictive biomarkers There is great potential for reducing the waste of economic resources from vast over-treatment of cancer patients Critical path objectives are more likely to be achieved thru development of predictive biomarkers than thru development of surrogate endpoint biomarkers

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