This document discusses challenges in the design and interpretation of early phase clinical trials in hematology-oncology. It notes problems such as slow accrual, lack of good preclinical models and surrogate endpoints, and high false positive rates when relying on a single study with short follow-up. The document advocates considering alternative trial designs like adaptive randomization and enrichment to accelerate drug development while minimizing false results. It also stresses recognizing tumor heterogeneity and understanding biology to reduce false negatives and properly evaluate new agents.

1. Problems of the Design and Interpretation of Very Early Clinical Trials in Hemato-OncologyBruce D. Cheson, M.D.Georgetown University HospitalLombardi Comprehensive Cancer CenterChair, CALGB Lymphoma CommitteeWashington, D.C., USA

2. Earliest Published Clinical TrialsDaniel 1:11-20. Health of Hebrews fed a Kosher diet vs Babylonians fed on a state dietScurvy on the HMS Salisbury: cider vs vinegar vs lemons

3. Phases of Clinical TrialsPhase I - determine toxicityPhase II - determine activityPhase III - evaluate efficacyPhase IV - post-marketing

4. The Problem6500 trials open to accrual in the U.S. (clinicaltrials.gov)3% of patients go on studiesStudies compete for same patients63% of trials are ever completed

5. Why our trials failBlame the designBlame the physiciansBlame the patientsBlame the wealth of new agentsRarely do we blame the science

6. Problems in New Drug DevelopmentSlow rate of accrual to clinical trialsLack of good preclinical modelsLack of surrogate endpointsFalse negatives – missing an active agentFalse positives – waste resources on ineffective agent

7. High False Positive RateRely on a single studyUnpredictability of intermediate endpointsGet excited by waterfall plotsShort follow-upResponse durationToxicityUnplanned subset analyses

9. Will Rogers (1879-1935)

10. The Will Rogers Effect“When the Okies left Oklahoma and moved to California, they raised the average intelligence level in both states!”

11. Or, in non-Cowboy Terms ~ A positive effect will occur when both of the following conditions are met ~The element being moved is below average for its current set. Removing it will, by definition, raise the average of the remaining elements

12. The element being moved is above the current average of the set it is entering. Adding to it will, by definition, raise the averageExamplesConsider the sets R and SR= (1, 2, 3, 4) – Mean = 2.5

13. S= (5, 6, 7, 8, 9) – Mean = 7

14. Moving 5 to R

15. Mean of R increases to 3

16. Mean of S increases to 7.5Relevance to Clinical Trials: Stage MigrationImproved detection of illness leads to the movement of people from the set of healthy to the set of unhealthyExample: Increased sensitivity of PET scans in lymphomaMoves “early” stage to advanced stage

17. Moves false-positive advanced stage to early stage

18. Improves the outcome of both cohortsCT from PET/CT - axial view

19. Contrast enhanced CT - axial view

20. PET - axial view

21. PET-coronal

22. Reducing the False Negative RateRecognize tumor diversityUnderstand tumor biology

23. Not All Patients or Their Diseases Are Created Equal

24. Lymphoma Cancer FactsLymphomas represent the 5th most common form of cancer in the US2011 projected >75,000 new cases in the US alone with ~ 25,000 deaths/yr~ 60 morphologic/immunologic subtypesMany more by molecular-genetic assessmentNo two lymphomas are alike and treatments must be selected based on an individual’s tumor characteristics, by personalized medicine

25. Antoni van Leeuwenhoek (1632-1723)Invented the microscope around 166821

26. TreatmentAdviceContrast of Appearance vs.Gene Expression ProfilingMicroscopeDLBCLDLBCLHigh RiskLow RiskMicroarray22

27. Lenz et al., N. Engl. J. Med. 2008A study of the Lymphoma Leukemia Molecular Profiling Project (LLMPP)

28. Survival curves in diffuse large B-cell lymphoma treated with Bortezomib-R-CHOPRuan J et al. JCO 2011;29:690-697

29. Lenalidomide in DLBCLHernandez-Ilizaliturri et al, Cancer (e-pub, 2011)

30. Lenalidomide in DLBCL (n=40)Response RatesGCB - 9% (4% CR)Non-GCB – 53% (5% CR)P = .006Hernandez-Ilizaliturri et al, Cancer (e-pub, 2011)

31. Microvessel Density in DLBCLCardesa-Salzmann et al, Haematol 2011 (e-pub ahead of print)

32. LHCardesa-Salzmann et al, Haematol 2011 (e-pub ahead of print)

33. Clinical Factors:-FLIPI-F2Biological Factors:-Histology-Molecular-Cytogenetic-Protein-mRNAPredictors of Clinical Behavior in FLHost factors?-FCR SNPsMicroenvironment-Others

34. Overall survival based on lymphoma associated macrophage (LAM) contentFarinha, P. et al. Blood 2005;106:2169-2174

35. .Weng W , Levy R JCO 2003;21:3940-3947FcgRIII Polymorphisms and Response to Rituximab

36. CORAL maintenance: PFS by gender in maintenance and observation arms1.01.00.80.80.60.6Survival probabilitySurvival probability0.40.40.20.2p = 0.0044p = 0.59210.00.001224364860720122436486072PFS (months)PFS (months)FemaleFemaleMaleMaleRituximabObservation

38. Probability of Survival100806040200024487296120144168Months13q deletionPatients surviving (%)12q trisomy11q deletionNormal17p deletionDöhner H, et al. N Engl J Med. 2000;343:1910-1916.

39. Outcome With Alemtuzumab in CLL Cytogenetic Subgroups.Stilgenbauer S et al. JCO 2009;27:3994-4001

40. 1001008080Surviving (%)6060Surviving (%)P=0.0008P=0.0014040202000501001502002503000050100150200250300MonthsMonthsSurvival of CLL Patients With Mutated vsUnmutated VH GenesStage-A CLL patients (n=62)All patients (n=84)MutatedMutatedUnmutatedUnmutatedDamle RN, et al. Blood. 1999;94:1840-1847.Hamblin TJ, et al. Blood. 1999;94:1848-1854

41. Genes That Best Discriminate Ig-Unmutated and Ig-mutated CLLIg-Unmut Ig-Mut

42. Kaplan-Meier Estimates Based on Level of ZAP-70 ExpressionLikelihood of survivalRisk of disease progression10090807060504030201001009080706050403020100<20% ZAP-70–positive cells20% ZAP-70–positive cells% Progression% surviving<20% ZAP-70–positive cells20% ZAP-70–positive cellsP=0.009P=0.010 2 4 6 8 10 12 14 160 4 8 12 16 20 24 28 32 36Years after diagnosisYears after diagnosisCrespo M, et al. N Engl J Med. 2003;348:1764-1775.

43. The Nature of the Disease

44. Pathway vs. Network signalingPathway“Newtonian”Network“Chaotic”A. Friedman and N. Perrimon, Cell 128, January 26, 2007

45. ShcGrb2PI3-KSos-1RasAKTRafMEKK-1MEKmTORMKK-7ERKJNKWhich Target?

46. Sos-1PI3-KRasGrb2ShcRafMEKMEKK-1JNKMKK-7ERKAKTThe Target InteractomeWhere’s the target?Courtesy of I. Serebriiskii and E. Golemis, Fox Chase Cancer Center

47. Cheson’s Rule“The efficacy of a new drug is directly proportional to its negative impact on clinical research”

48. Victims of Our Own Successes: The DiseaseFront-line HLFront-line DLBCLFront-line FLFront-line CLL

49. Victims of Our Own Successes: The DrugNew drug B is very active in relapsed/refractory diseaseRapidly adopted as front-lineProblemsNo drug B naïve pts against which to compare drug CNo data on other drugs in B-relapsed ptsResults in relapse too good to improve uponResults in front-line too good to improve upon

50. ExamplesTK inhibitors in CMLB(R) in F/LG NHLB(R) in CLLBrentuximabvedotin in HL

51. SolutionsDrug B +/- drug C in relapsed-refractory ptsTakes a randomized trialLong time for answerDrug C in B-refractory ptsNo data with other agentsRisky

52. Do We Have Surrogate Endpoints?

53. 1.00.80.60.40.20.0012243648846072Time (months)MRD Response and Treatment-Free Survival After Alemtuzumab Treatment (N=91)MRD-negative CR/PR (n=18)P<0.0001Cumulative probabilityMRD-positive CR (n=14)MRD-positive PR (n=17)Nonresponders (n=42)Moreton P et al. J Clin Oncol. 2005;23:2971-2979.

54. PRIMA: Primary endpoint (PFS): 36 months’ follow-up after randomisation1.00.875%Rituximab maintenance0.6Event-free rate58%0.4ObservationStratified HR = 0.5595% CI: 0.44–0.68p < 0.00010.20.006121824303642485460Time (months)Patients at risk50547244542340430720784170–51346941536733424716170160–GA Salles et al. ASH 2010, Abstract 1788

55. Progression-free survival(from study registration)Conventional response assessment1.0CR/CRuPR0.8SD/PD0.6Probability of PFS0.40.2p < 0.00010.006121824303642485460Time (months)No. of subjectsEventCensoredMedian PFS (months)CR/CRu8935% (31)65% (58)52PR2744% (12)56% (15) NRSD/PD 888% (7)13% (1)7

56. Progression-free survival(from study registration) Post-treatment PET-CT based assessment1.0PET negativePET positive74%0.80.6Probability of PFS0.432%HR = 3.5 (95% CI 2.0-6.1)p < 0.00010.20.006121824303642485460Time (months)EventCensoredMedian PFS (months)No. of subjectsPET negative31% (28)69% (63)NR91PET positive33% (11)193367% (22)

57. Progression-free survival according to IPS group and PET results after two cycles of ABVDGallamini, A. et al. J Clin Oncol; 25:3746-3752 2007

58. BEACOPP treatment for 154 PET-2- positive advanced-stage HL patients PET2-negAll ptsPET2+Gallamini A. et al, Br J Haematol, 152:551, 2011

59. CALGB Risk-Adapted Studies in HL

60. Variables Influencing Interim PET ResultsDiseaseStageTherapyMethod of interpretation

61. Accelerate Completion of Clinical TrialsActivate fewer studiesConsider alternative funding sourcesIdentify surrogate endpointsNovel statistical designs

62. Is There Still a Role For Traditional Phase I Studies in Haematologic Malignancies?

63. Dose Escalation of Rituximab in CLL DosePatients with toxicity2(mg/m )No.No.Grade 500 3 0 - 650 3 11+ nausea 825 3 0 - 1000 3 21+ malaise, nausea 1500 4 1dyspnea, dose 2 225010 52+ fever, chills1+ nausea, fatigue$LTO’Brien et al, JCO 19:2165-70, 2001

64. Need to Consider Novel Study Designs

65. Randomized Discontinuation DesignPremise:Some drugs are more static than tumouricidal

66. Tumour growth may be slow

67. Single arm study cannot identify improved PFSEnrichment designSelect/randomize relatively homogenous patients

68. .Rosner G L et al. JCO 2002;20:4478-4484Randomized Discontuation Design

69. A Proposal for New Drug Development in CancerSmall Phase II TrialsMolecular EnrichmentMolecular EnrichmentRepeat

70. I-SPY2 TRIAL*ADAPTIVELYRANDOMIZE Experimental arm 1 Experimental arm 2Outcome:Complete Response Populationof patients Experimental arm 3 Experimental arm 4 Experimental arm 5 Standard therapy*Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging And moLecular Analysis 2

71. Sometimes We Are Misled By Rigid Response CriteriaCLLCheson, et al, Am J Hematol 29:72, 1988Cheson, et al, Blood 87:4990, 1996Hallek, Cheson, et al, Blood 111:5446, 2008AML Cheson, et al, JCO 8:813, 1990Cheson et al, JCO 21:4642, 2003MDSCheson, et al, Blood 96:3671, 2000Cheson, et a, Blood 108:419, 2006NHLCheson, et al, JCO 17:1244, 1999Cheson, et al, JCO 25:579, 2007MM – Don’t blame me!

72. Tumor Flare ReactionFlare reactionBaselinePost-treatmentOccasionalFever

73. Bone pain

74. ↑ WBC/ALCPI3K Delta Pathway Induces Proliferation Cell survival TraffickingCritical Signaling Pathways in B-Cell MalignanciesStromal cellT-cellSignalingstimulusIL-6BAFFCXCL13IL-6RBCRCXCR5BAFFRCD40Malignant B-cell membraneLYNgp130gp130JAKTRAF6JAKSYKaLYN/SYKbgPI3KDeltaSTATSTATBTKBTKPLCg2PLCg2AKTT308S473NF-kBpathwayGSK-3PKCmTORp70s6kelf4E

75. The Road to Individualized Therapy Starts With Science

76. CALGB Current/Planned Protocols in Untreated Follicular NHLLow-Intermediate FLIPI50402 - Rituximab + galiximab (completed)50404 - Rituximab + oblimersen (RIP)50701 - Rituximab + epratuzumab (completed)50803 - Rituximab + lenalidamide (near completion)50901 – Ofatumumab (500 mg vs 1000 mg)511XX – Rituximab/Lenalidomide +/- PCI32765

77. Correlative StudiesPET scansMolecular profiling, gene expressionTMAs collected on all patientsStudies of effector cell number/functionFOXP3, T-RegsMicroenvironmental factors, LAMsSNPsMicroRNAs

78. CALGB:50303 Phase III Randomized Study of R-CHOP v. DA-EPOCH-R with Microarray TreatmentcompletedARM A: R-CHOPC3C4C6C1C2C5StagePET/CTStagePET/CTRandomization C3C4C6C1C2C5Tumor BiopsyBlood SamplesARM B: DA-EPOCH-RTreatmentCompleted Gene Expression Profiling