Statistical Issues in the Evaluation of Predictive

BiomarkersRichard Simon, D.Sc.

Chief, Biometric Research BranchNational Cancer Institute

http://brb.nci.nih.gov

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

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

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

• 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

• “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

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

• 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

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

• 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

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.

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

New Drug Developmental Strategy I

• Restrict entry to the phase III trial based on the binary predictive classifier, i.e. targeted design

Using phase II data, develop predictor of response to new drugDevelop Predictor of Response to New Drug

Patient Predicted Responsive

New Drug Control

Patient Predicted Non-Responsive

Off Study

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

• 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

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

• 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

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

Web Based Software for Comparing Sample Size

Requirements

• http://linus.nci.nih.gov/brb/

Developmental Strategy (II)

Develop Predictor of Response to New Rx

Predicted Non-responsive to New Rx

Predicted ResponsiveTo New Rx

ControlNew RX Control

New RX

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

Validation of EGFR biomarkers for selection of EGFR-TK inhibitor therapy for previously

treated NSCLC patients

2nd line NSCLC

with specimen

FISHTesting

FISH +(~ 30%)

FISH −(~ 70%)

Erlotinib

Pemetrexed

Erlotinib

Pemetrexed

Outcome1° PFS2° 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 patients4 years accrual, 1196 patients

1-2 years minimum additional follow-up

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.

• This analysis strategy is designed to not penalize sponsors for having developed a classifier

• It provides sponsors with an incentive to develop genomic classifiers

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

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

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

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

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.

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