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Personalized Molecular Medicine Clinical trial designs to establish biomarker based treatment decisions - Biometrical Considerations - Markus Loeffler Institute for Medical Informatics, Statistics and Epidemiology Center for Clinical Trials

  1. Personalized Molecular Medicine Clinical trial designs to establish biomarker based treatment decisions - Biometrical Considerations - Markus Loeffler Institute for Medical Informatics, Statistics and Epidemiology Center for Clinical Trials (ZKSL) Center for Bioinformatics (IZBI) University of Leipzig Markus.Loeffler@imise.uni-leipzig.de imise . AREVIR – Köln – 12.4.2013

  2. My Background • MD & physics • Computational models of regenerative tissues and cancers – eg CML treatment optimisation (Chemo/IFN  TKI only) • Biometrical support in the design and conduct of large scale clinical trials (cancer, heart, infections) – Interventional – Diagnostic and prognostic • Bioinformatics of high throughput analyses imise . AREVIR – Köln – 12.4.2013

  3. All men are equal (French Revolution) All men are different (Genetic Revolution) Both is true, depending on the point of view imise . AREVIR – Köln – 12.4.2013

  4. All men are equal (French Revolution) All men are different (Genetic Revolution) Both is true, depending on the point of view Biometrical point of view: Consider heterogeneity in the light of a model and resolve it only as far as needed and not more (Occam´s razor) imise . AREVIR – Köln – 12.4.2013

  5. What is PMM for me as biometrician ? A hypothesis and data driven formal model splitting a disease population into subpopulations using molecular diagnostic classifiers to design and allocate differential targeted treatments to improve efficacy and/or side effects compared with present standard treatments Note: - group based approach - cinical decision making - evidence based imise . AREVIR – Köln – 12.4.2013

  6. Clinical decision tree (model) disease + - Classifier 1 Classifier 2 Classifier 3 + - - + Th C Th D Th A Th B Note: Combinatorial problem  many trials needed to establish new standards imise . AREVIR – Köln – 12.4.2013

  7. Binary biomarker classifier - simple case - Ideal: Biomarker test is positive or negative with high reproducibility and reliability and only few cases remain unclear Binary by definition: - gene mutations: HER2, KRAS, IDH1 - surface markers (eg cd20 B-cell epitope ) imise . AREVIR – Köln – 12.4.2013

  8. Glioblastoma and IDH1-story German Glioma Network cohort imise . AREVIR – Köln – 12.4.2013

  9. Glioblastoma and IDH1 German Glioma Network cohort IDH1+ GBM: 5% AIII: 57% The view changing: Genetics more important than histology in diagnostics imise . AREVIR – Köln – 12.4.2013

  10. Biomarker classifier - complex cases- Continuous classifiers: eg gene expression signatures eg indices eg imaging signals (eg PET-SUV)  Make them binary by cut points imise . AREVIR – Köln – 12.4.2013

  11. Diffuse Large B-cell Lymphoma molecular Burkitt Lymphoma - classifier mBL non_mBL Core group 53 genes not selected for function Different disease imise . but same therapy AREVIR – Köln – 12.4.2013

  12. mBL classifier external validation on data from Dave et al. 2006 99 aggressive B-cell lymphomas were HGU113plus2.0 data were available mBL index Gene expression mBL index Dave et al. classifier imise . AREVIR – Köln – 12.4.2013

  13. Classifier construction Multistep process important: (1) exploratory (2) define the classifier on a training sample (3) test this classifier on internal and external validation samples and demonstrate that it offers added value to classifiers already available eg - comparing ROC-curves if diagnostic - adjusting COX-analyses if prognostic Mind: Statistical significance does not imply clinical relevance, It is the biology which matters !! imise . AREVIR – Köln – 12.4.2013

  14. Biomarker analyses in cohort studies Prospective clinical cohort studies play an important role for biomarker discovery with impact on diagnostis and prognosis Requires: Carefully phenotyped cases - - Careful follow up (if looking for prognosis) - High quality biomaterial High quality laboratories - Caveat: Cohorts outside RCTs can be biased and confounded not suited for determining which treatment is best !!  RCT imise . AREVIR – Köln – 12.4.2013

  15. Clinical trials to search best biomarker related treatments - one target case, dichotomous - imise . AREVIR – Köln – 12.4.2013

  16. Phase 3 biomarker trial designs - with binary test classifiers- Assumption: • Valid dichotomous biomarker • High sensitivity and specificity • Reproducibility shown • Practicability shown see Freidlin et al JNCI 2010, R Simon´s group imise . AREVIR – Köln – 12.4.2013

  17. (I) Biomarker stratified trial design Note: may need different sample sizes in each stratum imise . AREVIR – Köln – 12.4.2013

  18. Marker Validation for Erlotinib in Lung Cancer imise . AREVIR – Köln – 12.4.2013

  19. NSCLC and metagene classifier imise . AREVIR – Köln – 12.4.2013

  20. Biomarker stratified design Advantages: - Can provide full information on treatment benefits in each subgroup and also regarding interactions Can provide information whether biomarkers are overall - prognostic and predictive for the special treatments - Can often be implemented post hoc in RCTs that have been conducted provided biobanking is available But: - requires more cases than a simple RCT - up to 4-fold if interaction is estimated Works only if few biomarkers are investigated - Can only be done if same treatment in both strata can be justified - imise . AREVIR – Köln – 12.4.2013

  21. (II) Biomarker enrichment design imise . AREVIR – Köln – 12.4.2013

  22. DLBCL-Ritux DL uxim imab ab 6 x CHOP-like CD20 + DLBCL 18-60 years Random. IPI 0,1 Stages II-IV, I with bulk 6 x CHOP-like + Rituximab CD20 neg: N = 800, T-cell Lym 10% difference in 1.EP Off Study

  23. Tim Time t to o Tr Treatment Fail Failure 1.0 .9 79.9% R-CHEMO .8 Probability .7 p = 0.000 000 007 .6 60.8% CHEMO .5 .4 .3 .2 .1 0.0 0 5 10 15 20 25 30 35 40 45 50 Months median observation time: 22 months Pfreundschuh et al Lancet Oncol 2006

  24. Biomarker enrichment designs Advantage: - Can only resolve treatment question in one subgroup Disadvantage: - No answer for other subgroup - Cannot provide information about overall prognostic or predictive relevance of biomarker CAVE: Severe problems if the classifier is not correctly classifiying  overtreatment, undertreatment, wrong treatment imise . AREVIR – Köln – 12.4.2013

  25. (III) Biomarker Strategy Design imise . AREVIR – Köln – 12.4.2013

  26. Biomarker strategy design NSCLC-example imise . AREVIR – Köln – 12.4.2013

  27. Biomarker Strategy Design standard vs fully individualized treatments Process of Fully individualized treatment treatment engineering Biomarker Assessment & Random Standard treatment eg: Dentritic cell based vaccination therapy HIV targeted drug combinations CAVE: Regulatory problems imise . AREVIR – Köln – 12.4.2013

  28. Design standard vs fully individualized treatments Computational Individualized treatment treatment design Assessment R Standard treatment eg: HIV targeted drug combinations CAVE: Regulatory problems imise . AREVIR – Köln – 12.4.2013

  29. Design two different individualized treatments Computational Individualized treatment (Comp) treatment design Assessment R Traditional Individualized treatment (trad) treatment design eg: HIV targeted drug combinations CAVE: Regulatory problems imise . AREVIR – Köln – 12.4.2013

  30. Biomarker Strategy Design Compares a complex treatment strategy as a whole vs a standard treatment Advantage: Provides information on the overall strategy, but not on subgroups can cope with many treament options Disadvantage: Interpretation problems which parts of the strategy are successful or harmful imise . AREVIR – Köln – 12.4.2013

  31. Remark Splitting the diseases into smaller subpopulations we often see stronger benefits with targeted treatment hence we need to do many trials but sample sizes and power is not necessarily a major concern imise . AREVIR – Köln – 12.4.2013

  32. Special designs imise . AREVIR – Köln – 12.4.2013

  33. Which of two classifiers is superior ? example Mindac trial Consensus Low risk: moderate Th BRCA Moderate Th Two biomarkers Discordant: R (1) Immun histo (2) Gene profile Intensive Th Consensus High risk: intensive Th imise . AREVIR – Köln – 12.4.2013

  34. Multimarker-multitarget designs Vach et al 2006 2 Biomarkers: a and b Disregards a & b cases which can coexist Therapy: A- Targeted a & b cases not fully exploited B- Targeted S- Standard imise . AREVIR – Köln – 12.4.2013

  35. Multimarker designs Vach et al 2006 Best design - balanced - many comparisons - value of each single marker/Th Full benefit if many independent targeted therapies are considered imise . AREVIR – Köln – 12.4.2013

  36. Phase 2 and 3 trial sequence Traditional trial sequence: 1. Phase 2 – one armed experimental trial 2. Phase 3 – RCT with control vs experimental Problems: - Unknown selections in phase 2 Phase 2 information not used in phase 3 - - Regulatory delays  Integrated randomized phase 2 & 3 designs helpful imise . AREVIR – Köln – 12.4.2013

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