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1 Clinical Validation of Prognostic Biomarkers of Risk and Predictive Biomarkers of Drug Efficacy or Safety Gene Pennello, Ph.D. Team Leader, Diagnostics.

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Presentation on theme: "1 Clinical Validation of Prognostic Biomarkers of Risk and Predictive Biomarkers of Drug Efficacy or Safety Gene Pennello, Ph.D. Team Leader, Diagnostics."— Presentation transcript:

1 1 Clinical Validation of Prognostic Biomarkers of Risk and Predictive Biomarkers of Drug Efficacy or Safety Gene Pennello, Ph.D. Team Leader, Diagnostics Devices Branch Division of Biostatistics Office of Surveillance and Biometrics Center for Devices and Radiological Health, FDA SAMSI Risk Perception Policy Practice WorkshopOctober 3, 2007

2 2 Outline FDA and Device Regulation Types of Biomarkers Validation of Diagnostics Predictive and Prognostic Biomarkers –Definitions, Endpoints –Study Designs for Predictive Biomarkers Prospective Designs – efficiency comparison Prospective-Retrospective Designs Summary

3 3 FDA CDER Drugs CDRH, Devices CBER, Biologics CVM, Veterinary CFSAN, Food NCTR

4 4 What are Medical Devices? An item for treating or diagnosing a health condition whose intended use is not achieved primarily by chemical or biological action within the body (Section 201(h) of the Federal Food Drug & Cosmetic (FD&C) Act). Definition by exclusion: Simply put, a medical device is any medical item for use in humans that is not a drug nor a biological product.

5 5 Example of Medical Devices Relatively Simple Devices tongue depressors thermometers latex gloves simple surgical instruments Ophthalmic devices intraocular lenses PRK lasers, Radiological devices MRI machines CT scanners digital mammography computer aided detection Cardiovascular Devices pacemakers defibrillators heart valves coronary stents artificial hearts Monitoring Devices glucometers bone densitometers Diagnostic Devices diagnostic test kits for HIV prostate-specific antigen (PSA) test human papillomavirus (HPV) test

6 6 Example of Medical Devices Emerging technologies multiplex genetic tests (e.g., for multiple mutations or microbes) Genomic and proteomic Dx tests Nanotechnological devices Microspheres for molecular treatment of cancer Robotics Theranostics (predictive biomarkers of response or adverse reaction to therapy). Artificial pancreas Dental, Ear, Nose, and Throat Devices hearing aids bronchoscopy system General, Surgical, and Restorative Devices breast implants artificial hips spinal fixation devices artificial skin

7 7 Example of Medical Devices Due to the wide variety in technology, complexity, and intended use, medical devices can present novel statistical design and analysis challenges.

8 8 Device Regulation Decision to approve a PMA application must rely upon valid scientific evidence to determine whether there is reasonable assurance that the device is safe and effective. Valid scientific evidence is evidence from well controlled studies, partially controlled studies and objective trials without matched controls, well documented case histories conducted by qualified experts that there is a reasonable assurance of safety and effectiveness... U.S. Code of Federal Regulations, Title 21 (Food and Drugs), U.S. Government Printing Office, Washington DC, 2001, Part 860.7 Web address http://www.access.gpo.gov/nara/cfr/waisidx_01/21cfr860_01.html (Accessed February, 2002) http://www.access.gpo.gov/nara/cfr/waisidx_01/21cfr860_01.html

9 9 Device Regulation Least Burdensome Provisions of FDA Modernization Act (1997) Secretary shall only request information that is necessary to making substantial equivalence determinations. Secretary shall consider, …, the least burdensome appropriate means of evaluating device effectiveness that would have a reasonable likelihood of resulting in approval. U.S. Code of Federal Regulations, Title 21 (Food and Drugs), U.S. Government Printing Office, Washington DC, 2001, Part 513(i)(1)(D) and 513(a)(3)(D)(ii). Web address http://www.access.gpo.gov/nara/cfr/waisidx_01/21cfr860_01.html http://www.access.gpo.gov/nara/cfr/waisidx_01/21cfr860_01.html

10 10 FDA Least Burdensome Guidance FDA Guidance: The Least Burdensome Provisions of the FDA Modernization Act of 1997: Concept and Principles (2002) Modern statistical methods may also play an important role in achieving a least burdensome path to market. For example, through the use of Baysian [sic] analyses, studies can be combined in order to help reduce the sample size needed for the experimental and/or control device.

11 11 Examples of Less Burdensome Non-U.S. data Surrogate endpoints (e.g., acute follow-up) Interim analysis, Adaptive design Bayesian methods (e.g., to reduce sample size) Propensity Scores for historical controls Sensitivity analysis for missing data. Note, could trade clinical for statistical burden FDA Draft Guidance for the Use of Bayesian Statistics in Medical Device (released May 23, 2006) www.fda.gov/cdrh/osb/guidance/1601.html FDA Draft Guidance for the Use of Bayesian Statistics in Medical Devicewww.fda.gov/cdrh/osb/guidance/1601.html

12 12 Least Burdensome Provision Least burdensome provision in FDAMA of 1997 is directed to both medical devices and diagnostics (including biomarkers).

13 13 Device Risk Classification Class I: Devices for which general controls provide reasonable assurance of the safety and effectiveness. Class II: General controls insufficient, Can establish special controls (performance standards [CLIA, ISO], FDA guidance. May require clinical data on a 510(k). Class III: General and special controls insufficient. Life-sustaining/supporting, substantial importance in preventing impairment of human health, potential unreasonable risk of illness or injury. Needs pre- market approval (PMA).

14 14 Post-Market Transformation Make postmarket data more widely available to Center staff and supplement search and reporting tools –"Investigate the use of data and text mining techniques to identify the "needles in the haystack" by identifying patterns in the incoming data that equate to public health signals. –Example is WebVDME Bayesian data-mining Design a pilot project to test the usefulness of quantitative decision-making methods for medical device regulation across the total product life cycle http://www.fda.gov/cdrh/postmarket/mdpi-report-1106.html

15 15 Types of Biomarkers Diagnostic Early detection (screening), enabling intervention at an earlier and potentially more curable stage than under usual clinical diagnostic conditions Monitoring of disease response during therapy, with potential for adjusting level of intervention (e.g. dose) on a dynamic and personal basis Risk assessment leading to preventive interventions for those at sufficient risk Prognosis, allowing for more aggressive therapy for patients with poorer prognosis Prediction of safety or efficacy (response) of a therapy, thereby providing guidance in choice of therapy

16 16 Types of Biomarkers Diagnostic Early Detection (screening) Monitoring Risk Assessment Prognostic Predictive of Safety or Efficacy The first three are considered together, where the focus is on identifying the disease or condition.

17 17 Types of Biomarkers Diagnostic Early Detection (screening) Monitoring Risk Assessment Prognostic Predictive of Safety or Efficacy The last three are attempting to predict the future.

18 18 Analytical Validation How well are you measuring the measurand? –Precision / Reproducibility –Method Comparison –LoB, LoD, LoQ –Linearity –Stability Clinical Laboratory Standards Institute (CLSI) (http://www.nccls.org/)http://www.nccls.org/

19 19 Clinical Validation (Qualification) –Does the test have clinical utility? –Does it have added value over standard tests (e.g, clinical covariates like age, tumor size, stage)? –May or may not require a clinical study EX. Roche Amplichip CDRH guidance document: Statistical Guidance on Reporting Results from Studies Evaluating Diagnostic Tests issued in final form in March, 2007, concerns reporting agreement when there is no perfect standard and also discrepancy resolution. http://www.fda.gov/cdrh/osb/guidance/1620.html

20 20 Roche AmpliChip CYP450 Test (CDRH de novo 510(k) K042259) Genotypes two cytochrome P450 genes (29 polymorphisms in CYP2D6 gene, 2 in CYP2C19) to provide the predictive phenotype of the metabolic rate for a class of therapeutics metabolized primarily by CYP2D6 or CYP2C19 gene products. The phenotypes are (1) Poor metabolizers: (3) Extensive metabolizers: (2) Intermediate metabolizers: (4) Ultrarapid metabolizers: Cytochrome P450s are a large multi-gene family of enzymes found in the liver, and are linked to the metabolism of approximately 70- 80% of all drugs. Among them, the polymorphic CYP2D6 and CYP2C19 genes are responsible for approximately 25% of all CYP450-mediated drug metabolism. A polymorphism in these enzymes can lead to an excessive or prolonged therapeutic effect or drug-related toxicity after a typical dose by failing to clear a drug from the blood or by changing the pattern of metabolism to produce toxic metabolites. http://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfPMN/pmn.cfm

21 21 Adding Value to Standard Clinical Predictors 1)Head to Head: Marker superior to clinical predictors at predicting outcome. 2)Incremental Improvement: Combination superior to clinical predictors alone. 3)Marker Predictive within Clinical Strata: e.g., HR(+, –) significant within age, tumor grade, tumor size groups.

22 22 Multivariate Index Assays An IVDMIA is a device that: –Combines the values of multiple variables using an interpretation function to yield a single, patient- specific result (e.g., a classification, score, index, etc.), that is intended for use in the diagnosis of disease or other conditions, or in the cure, mitigation, treatment or prevention of disease, and –Provides a result whose derivation is non-transparent and cannot be independently derived or verified by the end user. MIA result could be a binary (dichotomous) (such as yes or no), categorical (such as disease type), ordinal (such as low, medium, high) or a continuous scale. –Source: FDA MIA Draft Guidance http://www.fda.gov/cdrh/oivd/guidance/1610.html

23 23 Typical Endpoints for Prognostic or Predictive Biomarkers 1.Time to Event 2.Event by Time t TreatmentMedian Survival Time A6 months B12 months Hazard Ratio0.5 TreatmentRNot RResponse Rate A30 0.50 (30/60) B10500.13 (10/60)

24 24 Relative Risk vs. Diagnostic Accuracy Relative Risk looks good, but Dx accuracy not great limited clinical utility? ENo E +30 60 –105060 4080120 Marker Event by Time t Relative Risk 3.0 = (30/60)/(10/60) Se 0.75 (30/40) Sp 0.63 (50/80) PPV 0.50 (30/60) NPV 0.83 (50/60) Example taken from Emir, Wieand, Su, Cha, Analysis of repeated markers used to predict progression of cancer Statist. Med., 17, 2563- 78, 1998.

25 25 Hazard Ratio vs. Diagnostic Accuracy NCCTG Mayo Clinic Study. CA15-3 ratio as diagnostic for progression of breast cancer (as determined by physical exam). Example taken from Emir, Wieand, Su, Cha, Analysis of repeated markers used to predict progression of cancer Statist. Med., 17, 2563- 78, 1998. Hazard Ratio 2.3 (p = 0.0002) Se 0.30 (0.17,0.43) Sp 0.82 (0.74,0.89) PPV 0.27 (0.21,0.33)

26 26 Diagnostic Performance Sensitivity Specificity (TP rate):(TN rate):FP rate: fraction offraction offraction of respondersnon-responders non-responders who test +who test –who test + Test is useful if TP rate > FP rate, i.e., sensitivity + specificity > 1. EX. Useless test:sensitivity 0.80, specificity 0.20

27 27 Diagnostic Performance PositiveNegative predictivepredictive value (PPV):value (NPV): 1 – NPV: fraction offraction offraction of test +s whotest –s whotest –s who responddont respondrespond Test is useful if PPV + NPV > 1 EX. Useless test:PPV 0.60, NPV 0.40

28 28 d A ROC curve is a plot of sensitivity (true positive rate) vs. 1-specificity (false positive rate) over all possible cutoff points for the test. The test is informative if the area under the curve is greater than 0.5.

29 29 Prognostic Biomarker (Strong Defn) Prognostic factor. Informs about an outcome independent of specific treatment (ability of tumor to proliferate, invade, and/or spread). Prognostic biomarker is associated with likelihood of an outcome (e.g., survival, response, recurrence) such that magnitude of association is independent of treatment. On some scale, treatment and biomarker effects are additive, that is, do not interact.

30 30 HR(A,B)=0.67

31 31

32 32 Prognostic Biomarker (Weak Defn) Prognostic factor. Informs about an outcome independent of specific treatment (ability of tumor to proliferate, invade, and/or spread). Prognostic biomarker is associated with likelihood of an outcome (e.g., survival, response, recurrence) in a population that is untreated or on a standard (non-targeted) treatment. If population is clearly defined, than can use to choose more or less aggressive therapy, but not specific therapies, per se.

33 33 HR(A,B)=0.67

34 34 Prognostic Biomarker Her2-neu for node-negative women with breast cancer – prognostic for recurrence Breast cancer prognostic test based on microarray gene expression of RNAs extracted from breast tumor tissue to assess a patients risk for distant metastasis for women less than 61 with Stage I or II disease with tumor size less than or equal 5.0 cm and who are lymph node negative. (Ref.: Buyse et al. JNCI 98, 1183-1192)

35 35 Agendia Mammaprint Gene Signature for Time to Distant Metastasis (N=302) 5-year: Low risk group: 0.95 (0.91-0.99) High risk group: 0.78 (0.72-0.84) 10-year: Low risk group: 0.90 (0.85-0.96) High risk group:0.71 (0.65-0.78) Buyse et al JNCI (2006), 98, 1183-1192

36 36 Proportion alive at 10 years ClinicalGene NProportion* Signature Low RiskLow Risk520.88 (0.74 to 0.95) Sp Low RiskHigh Risk280.69 (0.45 to 0.84) 1–Se High RiskLow Risk590.89 (0.77 to 0.95) Sp High RiskHigh Risk1630.69 (0.61 to 0.76) 1–Se * Buyse et al JNCI 2006

37 37 Predictive Biomarker Predictive factor. Implies relative sensitivity or resistance to specific treatments or agents. Predictive biomarker predicts differential effect of treatment on outcome. Treatment and biomarker interact. Predictive biomarker can be useful for selecting specific therapy.

38 38 HR(A,B)=0.5 HR(A,B)=1.0

39 39 Predictive Biomarker of Efficacy Marker: HER2/neu Treatment: Trastuzumab (Herceptin) Objective response rate: Herceptin+ChemoChemo FISH+95/176 (54%) 51/168 (30%) FISH-19/50 (38%) 22/57 (39%) Arch. Pathol. Lab Med Jan 2007 (ASCO/CAP Guidelines)

40 40 Predictive Biomarkers for Safety Predict risk of an adverse event dependent on the biomarker Example –UGT1A1, cleared by FDA, to predict the risk of neutropenia in patients taking irinotecan for colorectal cancer

41 41 Prospective Study Designs for Predictive Markers Untargeted Design (Reference) Validate Treatment, Marker Simultaneously Marker by Treatment Design Targeted Design (Marker + Subset Only) Marker Strategy Design Historical Control

42 42 Untargeted Design (Reference) Test if drug works in entire population. Mixture of marker + and – drug effects. Can store samples if test is not ready.

43 43 Marker by Treatment (Interaction) Design A Randomized Block Design Can test for biomarker by treatment interaction (predictive biomarker) Test needs to be available before trial ensues.

44 44 Marker by Treatment Design Questions Test Drug Overall and within Marker + Subset –0.04, 0.01 tests suggested to control Type I error rate at 0.05 (Simon), but subset could drive overall result. –Frequentist multiplicity penalty may preclude subset testing as good business strategy. –Statement about drug, not biomarker Test Marker Overall and within Drug Subset –Statement about marker, not drug. Test for Treatment by Marker Interaction –Simultaneously validates drug and marker.

45 45 Targeted Design Test if drug works in subset. Cannot test if marker discriminates. Only PPV available.

46 46 Efficiency of Designs Efficiency gain depends on marker prevalence, relative efficacy, and difference tested. Relative Efficiency Marker Prevalence Relative Efficacy * Targeted Design Interaction Design 25%0%16x8x 50%0%4x2x 75%0%1.8x0.9x * Marker – to Marker + Patients Simon & Maitournam, CCR 2004 Marker by Treatment Design: Test for Interaction approx. efficiency enriching with half +s, half –s.

47 47 Efficiency of Designs Efficiency gain depends on marker prevalence, relative efficacy, and difference tested. Relative Efficiency Marker Prevalence Relative Efficacy * Targeted Design Interaction Design 25% 5.2x1.5x 50%25%2.6x0.7x 75%25%1.5x0.4x * Marker – to Marker + Patients Simon & Maitournam, CCR 2004 Marker by Treatment Design: Test for Interaction approx. efficiency enriching with half +s, half –s.

48 48 Efficiency of Designs Efficiency gain depends on marker prevalence, relative efficacy, and difference tested. Relative Efficiency Marker Prevalence Relative Efficacy * Targeted Design Interaction Design 25%50%2.5x0.3x 50% 1.8x0.2x 75%50%1.3x0.1x * Marker – to Marker + Patients Simon & Maitournam, CCR 2004 Marker by Treatment Design: Test for Interaction approx. efficiency when enriching with half +s, half –s.

49 49 Improving Efficiency of Interaction Design Enrich with Test Positives if Pr(+) is low Find scale such that marker and treatment effects are additive Adaptive Randomization Bayesian subset analysis If reader variability (e.g., IHC), then use multiple readers. Prior Information

50 50 Possibilities for Increasing Efficiency of Interaction Design Enrich with Test Positives if Pr(+) is low –Estimates of Sensitivity and Specificity are biased because they depend on Pr(+). –Use inverse probability weighting (Horvitz, Thompson, 1952) or Bayes Theorem (Begg, Greenes, 1983) to obtain unbiased estimates.

51 51 A Marker-Based Strategy Pro:More ethical, perhaps. More patients given experimental drug. Test utility based on PPV E, NPV E. Con: Cannot assess test-treatment interaction.

52 52 Marker-Based Strategy RNot R Eab P00 Test + Response RNot R Ecd Pef Test – E NaïveE Unbd Se a / (a+c)a / (a+2c) Sp d / (d+b)2d / (2d+b) PPV a / (a+b)same NPV d / (c+d)same

53 53 A Marker-Based Strategy RNot R E20 40 P000 20 40 Test + Response RNot R E23157180 P24156180 46314360 Test – E NaïveE Unbd Se 20/43 (0.47) 20/66 (0.30) Sp 157/177 (0.89) 314/334 (0.94) PPV 20/40 (0.50) Same NPV 157/180 (0.88) Same

54 54 Possibilities for Increasing Efficiency of Interaction Design Transformation –Find a transformation (Box-Cox?) of outcome that makes treatment and effects additive. –Can then pool marker effect estimates within treatments A and B. –Can also pool drug effect estimates within marker + and marker – s.

55 55 Possibilities for Increasing Efficiency of Interaction Design Adaptive Randomization –Adapt randomization ratio to treatment A and B within biomarker subsets to maximize (a) power, or (b) fraction of patients on better treatment –If response rate < 0.5 for both treatments, then (a) and (b) are compatible, otherwise in tension. –Pr(+) disturbed, so need to adjust Se, Sp

56 56 Possibilities for Increasing Efficiency of Interaction Design Bayesian subset analysis (cf. Dixon, Simon) –Subsets modeled as exchangeable via random effects. –Subset estimate borrows strength from complement subset, increasing precision of estimate. –However, interaction estimate more conservative relative to usual non-Bayesian analysis.

57 57 Bayesian Subset Analysis Power is enhanced to show drug works in marker + subset (blue). Power is enhanced to show marker works (discriminates) in patients taking drug (red)

58 58 Possibilities for Increasing Efficiency of Interaction Design Use Multiple Readers –EGFR IHC test (Dako) and Cetuximab and Panitumumab (Amgen) for Colorectal Cancer. % of cells stained and maximum staining intensity subject to reader variability –Use multiple readers, account for random reader effects. Multiple Reader, Multiple Case Designs (MRMC) are used for digital mammography systems and computed aided detection (CAD) systems Analysis can be difficult.

59 59 Possibilities for Increasing Efficiency of Interaction Design Prior Information (Bayesian analysis) –Borrow strength from previous study regarded as exchangeable with current study.

60 60 Register Marker Based Strategy Non Marker Based Strategy Treatment A Marker Level (-) Treatment A Marker Level (+) Treatment B Test Marker Sargent et al., JCO 2005 Marker Based Strategy Design Randomize

61 61 Register Marker Based Strategy Non Marker Based Strategy Randomize Treatment A Treatment B Marker Level (-) Treatment A Marker Level (+) Treatment B Test Marker Sargent et al., JCO 2005 Marker Based Strategy Design Randomize

62 62 Marker Based Strategy Design Lacks power: Differential effect comparison diluted because some patients in non-marker-based strategy arm get marker-based treatment (could eliminate these to increase power). Might be best suited if have > 2 treatments or > 2 markers –EX. Irinotecan regiment (dose, timing, frequency) determined by UGT1A1 genotype (6/6, 6/7, or 7/7) in colorectal cancer patients.

63 63 Marker Based Strategy If no gold standard, then can be only way to assess effectiveness of a test. EX. Detection tumor of origin in cancers of unknown primary. –No gold standard: IHC, imaging, may fail to identify TOO. –Randomize patients to be managed with new test + standard, or with standard alone –Compare arms on survival

64 64 Targeted Design w. Historical Control Drug already on market, but has poor response rate. If response rate in marker + study is significantly greater than historical rate, then marker discriminates. Limitations –Lacks power because effect diluted. –Need to calibrate historical rate to marker + study (adjust for covariates).

65 65 Prospective-Retrospective Designs Prospectively apply marker to stored samples (in retrospect). Can test overall, w. subset, or for interaction. Missing samples could introduce bias. RCT samples. Randomization ensures case and control samples have similar characteristics. Case-control samples. Avoid selection bias by matching on sample processing date, processing sites, etc., and not excluding censored times. Reserve samples only for analytically validated markers that are biologically plausible.

66 66 The Challenge of Multiplicity Multiplicity of classifiers Microarrays and proteomics Many predictive models could be built with so many inputs. The challenge is to confirm any such model with an independent data set. A caveat: the independent test data set cannot be continually reused. Great discipline is required in this regard.

67 67 Cross-Validation Pitfall Simon, Radmacher, Dobbin, McShane (2003), Pitfalls in the Use of DNA Microarray Data for Diagnostic and Prognostic Classification, JNCI, 95 (1)

68 68 Summary Remarks How to assess a test or biomarker is well- known, but not as well-known in therapeutic circles. Need to assess whether the biomarker adds anything to what we already know. The number of possibly good biomarker candidates is enormous but great care is needed in restricting the search.

69 69 Summary Remarks Need to encourage least burdensome approaches to validating biomarkers without compromising level of evidence Essential to confirm marker in independent dataset Studies to demonstrate informativeness of a biomarker can be quite difficult to design, conduct and analyze.

70 70 Acknowledgements CDRH Division of Biostatistics (DBS) –Greg Campbell, Division Director –Diagnostic Devices Branch (DDB) Lakshmi Vishnuvajjala, Branch Chief Estelle Russek-Cohen, Team Leader Gene Pennello, Team Leader Bipasa BiswasKyungsook Kim, Harry BusharSamir Lababidi Arkendra DeKristen Meier Shanti Gomatam Kyunghee Song Thomas GwiseRong Tang

71 71 More References Sargent et al (2005). Clinical trial designs for predictive marker validation in cancer treatment trials. J Clin Oncol 23:2020-2027. Pennello & Vishnuvajjala (2005). Statistical design and analysis issues with pharmacogenomic drug-diagnostic co-development, In American Stat. Assoc. 2005 Proc. of the Biopharm. Section, Joint Statistical Meetings, Minneapolis, MN, August, 2005; American Stat. Assoc.: Alexandria, VA. FDA Drug-Diagnostic Co-Development Concept Paper. April 2005. http://www.fda.gov/cder/genomics/pharmacoconceptfn.pdf http://www.fda.gov/cder/genomics/pharmacoconceptfn.pdf

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