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LLA 2011 - B. Cheson - Problems of the design and interpretation of very early clinical trials in hemato-oncology Presentation Transcript

  • 1. Problems of the Design and Interpretation of Very Early Clinical Trials in Hemato-Oncology
    Bruce D. Cheson, M.D.
    Georgetown University Hospital
    Lombardi Comprehensive Cancer Center
    Chair, CALGB Lymphoma Committee
    Washington, D.C., USA
  • 2. Earliest Published Clinical Trials
    Daniel 1:11-20. Health of Hebrews fed a Kosher diet vs Babylonians fed on a state diet
    Scurvy on the HMS Salisbury:
    cider vs vinegar vs lemons
  • 3. Phases of Clinical Trials
    Phase I - determine toxicity
    Phase II - determine activity
    Phase III - evaluate efficacy
    Phase IV - post-marketing
  • 4. The Problem
    6500 trials open to accrual in the U.S. (clinicaltrials.gov)
    3% of patients go on studies
    Studies compete for same patients
    63% of trials are ever completed
  • 5. Why our trials fail
    Blame the design
    Blame the physicians
    Blame the patients
    Blame the wealth of new agents
    Rarely do we blame the science
  • 6. Problems in New Drug Development
    Slow rate of accrual to clinical trials
    Lack of good preclinical models
    Lack of surrogate endpoints
    False negatives – missing an active agent
    False positives – waste resources on ineffective agent
  • 7. High False Positive Rate
    Rely on a single study
    Unpredictability of intermediate endpoints
    Get excited by waterfall plots
    Short follow-up
    Response duration
    Toxicity
    Unplanned subset analyses
  • 8.
  • 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 average
  • Examples
    Consider the sets R and S
    • R= (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 7.5
  • Relevance to Clinical Trials: Stage Migration
    Improved detection of illness leads to the movement of people from the set of healthy to the set of unhealthy
    Example: Increased sensitivity of PET scans in lymphoma
    • Moves “early” stage to advanced stage
    • 17. Moves false-positive advanced stage to early stage
    • 18. Improves the outcome of both cohorts
  • CT from PET/CT - axial view
  • 19. Contrast enhanced CT - axial view
  • 20. PET - axial view
  • 21. PET-coronal
  • 22. Reducing the False Negative Rate
    Recognize tumor diversity
    Understand tumor biology
  • 23. Not All Patients or Their Diseases Are Created Equal
  • 24. Lymphoma Cancer Facts
    Lymphomas represent the 5th most common form of cancer in the US
    2011 projected >75,000 new cases in the US alone with ~ 25,000 deaths/yr
    ~ 60 morphologic/immunologic subtypes
    Many more by molecular-genetic assessment
    No 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 1668
    21
  • 26. TreatmentAdvice
    Contrast of Appearance vs.Gene Expression Profiling
    Microscope
    DLBCL
    DLBCL
    High Risk
    Low Risk
    Microarray
    22
  • 27. Lenz et al., N. Engl. J. Med. 2008
    A study of the Lymphoma Leukemia Molecular Profiling Project (LLMPP)
  • 28. Survival curves in diffuse large B-cell lymphoma treated with Bortezomib-R-CHOP
    Ruan J et al. JCO 2011;29:690-697
  • 29. Lenalidomide in DLBCL
    Hernandez-Ilizaliturri et al, Cancer (e-pub, 2011)
  • 30. Lenalidomide in DLBCL (n=40)
    Response Rates
    GCB - 9% (4% CR)
    Non-GCB – 53% (5% CR)
    P = .006
    Hernandez-Ilizaliturri et al, Cancer (e-pub, 2011)
  • 31. Microvessel Density in DLBCL
    Cardesa-Salzmann et al, Haematol 2011 (e-pub ahead of print)
  • 32. L
    H
    Cardesa-Salzmann et al, Haematol 2011 (e-pub ahead of print)
  • 33. Clinical Factors:
    -FLIPI
    -F2
    Biological Factors:
    -Histology
    -Molecular
    -Cytogenetic
    -Protein-mRNA
    Predictors of Clinical Behavior in FL
    Host factors?-FCR SNPs
    Microenvironment-Others
  • 34. Overall survival based on lymphoma associated macrophage (LAM) content
    Farinha, P. et al. Blood 2005;106:2169-2174
  • 35. .
    Weng W , Levy R JCO 2003;21:3940-3947
    FcgRIII Polymorphisms and Response to Rituximab
  • 36. CORAL maintenance: PFS by gender in maintenance and observation arms
    1.0
    1.0
    0.8
    0.8
    0.6
    0.6
    Survival probability
    Survival probability
    0.4
    0.4
    0.2
    0.2
    p = 0.0044
    p = 0.5921
    0.0
    0.0
    0
    12
    24
    36
    48
    60
    72
    0
    12
    24
    36
    48
    60
    72
    PFS (months)
    PFS (months)
    Female
    Female
    Male
    Male
    Rituximab
    Observation
  • 37.
  • 38. Probability of Survival
    100
    80
    60
    40
    20
    0
    0
    24
    48
    72
    96
    120
    144
    168
    Months
    13q deletion
    Patients surviving (%)
    12q trisomy
    11q deletion
    Normal
    17p deletion
    Döhner 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. 100
    100
    80
    80
    Surviving (%)
    60
    60
    Surviving (%)
    P=0.0008
    P=0.001
    40
    40
    20
    20
    0
    0
    50
    100
    150
    200
    250
    300
    0
    0
    50
    100
    150
    200
    250
    300
    Months
    Months
    Survival of CLL Patients With Mutated vsUnmutated VH Genes
    Stage-A CLL patients (n=62)
    All patients (n=84)
    Mutated
    Mutated
    Unmutated
    Unmutated
    Damle 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 CLL
    Ig-Unmut Ig-Mut
  • 42. Kaplan-Meier Estimates Based on Level of ZAP-70 Expression
    Likelihood of survival
    Risk of disease progression
    100
    90
    80
    70
    60
    50
    40
    30
    20
    10
    0
    100
    90
    80
    70
    60
    50
    40
    30
    20
    10
    0
    <20% ZAP-70–positive cells
    20% ZAP-70–positive cells
    % Progression
    % surviving
    <20% ZAP-70–positive cells
    20% ZAP-70–positive cells
    P=0.009
    P=0.01
    0 2 4 6 8 10 12 14 16
    0 4 8 12 16 20 24 28 32 36
    Years after diagnosis
    Years after diagnosis
    Crespo M, et al. N Engl J Med. 2003;348:1764-1775.
  • 43. The Nature of the Disease
  • 44. Pathway vs. Network signaling
    Pathway
    “Newtonian”
    Network
    “Chaotic”
    A. Friedman and N. Perrimon, Cell 128, January 26, 2007
  • 45. Shc
    Grb2
    PI3-K
    Sos-1
    Ras
    AKT
    Raf
    MEKK-1
    MEK
    mTOR
    MKK-7
    ERK
    JNK
    Which Target?
  • 46. Sos-1
    PI3-K
    Ras
    Grb2
    Shc
    Raf
    MEK
    MEKK-1
    JNK
    MKK-7
    ERK
    AKT
    The Target Interactome
    Where’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 Disease
    Front-line HL
    Front-line DLBCL
    Front-line FL
    Front-line CLL
  • 49. Victims of Our Own Successes: The Drug
    New drug B is very active in relapsed/refractory disease
    Rapidly adopted as front-line
    Problems
    No drug B naïve pts against which to compare drug C
    No data on other drugs in B-relapsed pts
    Results in relapse too good to improve upon
    Results in front-line too good to improve upon
  • 50. Examples
    TK inhibitors in CML
    B(R) in F/LG NHL
    B(R) in CLL
    Brentuximabvedotin in HL
  • 51. Solutions
    Drug B +/- drug C in relapsed-refractory pts
    Takes a randomized trial
    Long time for answer
    Drug C in B-refractory pts
    No data with other agents
    Risky
  • 52. Do We Have Surrogate Endpoints?
  • 53. 1.0
    0.8
    0.6
    0.4
    0.2
    0.0
    0
    12
    24
    36
    48
    84
    60
    72
    Time (months)
    MRD Response and Treatment-Free Survival After Alemtuzumab Treatment (N=91)
    MRD-negative CR/PR (n=18)
    P<0.0001
    Cumulative probability
    MRD-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 randomisation
    1.0
    0.8
    75%
    Rituximab maintenance
    0.6
    Event-free rate
    58%
    0.4
    Observation
    Stratified HR = 0.55
    95% CI: 0.44–0.68
    p < 0.0001
    0.2
    0.0
    0
    6
    12
    18
    24
    30
    36
    42
    48
    54
    60
    Time (months)
    Patients at risk
    505
    472
    445
    423
    404
    307
    207
    84
    17
    0

    513
    469
    415
    367
    334
    247
    161
    70
    16
    0

    GA Salles et al. ASH 2010, Abstract 1788
  • 55. Progression-free survival(from study registration)Conventional response assessment
    1.0
    CR/CRu
    PR
    0.8
    SD/PD
    0.6
    Probability of PFS
    0.4
    0.2
    p < 0.0001
    0.0
    0
    6
    12
    18
    24
    30
    36
    42
    48
    54
    60
    Time (months)
    No. of subjects
    Event
    Censored
    Median PFS (months)
    CR/CRu
    89
    35% (31)
    65% (58)
    52
    PR
    27
    44% (12)
    56% (15)
    NR
    SD/PD
    8
    88% (7)
    13% (1)
    7
  • 56. Progression-free survival(from study registration) Post-treatment PET-CT based assessment
    1.0
    PET negative
    PET positive
    74%
    0.8
    0.6
    Probability of PFS
    0.4
    32%
    HR = 3.5 (95% CI 2.0-6.1)
    p < 0.0001
    0.2
    0.0
    0
    6
    12
    18
    24
    30
    36
    42
    48
    54
    60
    Time (months)
    Event
    Censored
    Median PFS (months)
    No. of subjects
    PET negative
    31% (28)
    69% (63)
    NR
    91
    PET positive
    33% (11)
    19
    33
    67% (22)
  • 57. Progression-free survival according to IPS group and PET results after two cycles of ABVD
    Gallamini, A. et al. J Clin Oncol; 25:3746-3752 2007
  • 58. BEACOPP treatment for 154 PET-2- positive advanced-stage HL patients
    PET2-neg
    All pts
    PET2+
    Gallamini A. et al, Br J Haematol, 152:551, 2011
  • 59. CALGB Risk-Adapted Studies in HL
  • 60. Variables Influencing Interim PET Results
    Disease
    Stage
    Therapy
    Method of interpretation
  • 61. Accelerate Completion of Clinical Trials
    Activate fewer studies
    Consider alternative funding sources
    Identify surrogate endpoints
    Novel statistical designs
  • 62. Is There Still a Role For Traditional Phase I Studies in Haematologic Malignancies?
  • 63. Dose Escalation of Rituximab in CLL
    Dose
    Patients with toxicity
    2
    (mg/m )
    No.
    No.
    Grade
    500
    3
    0
    -
    650
    3
    1
    1+ nausea
    825
    3
    0
    -
    1000
    3
    2
    1+ malaise, nausea
    1500
    4
    1
    dyspnea, dose 2
    2250
    10
    5
    2+ fever, chills
    1+ nausea, fatigue
    $LT
    O’Brien et al, JCO 19:2165-70, 2001
  • 64. Need to Consider Novel Study Designs
  • 65. Randomized Discontinuation Design
    Premise:
    • Some drugs are more static than tumouricidal
    • 66. Tumour growth may be slow
    • 67. Single arm study cannot identify improved PFS
    Enrichment design
    Select/randomize relatively homogenous patients
  • 68. .
    Rosner G L et al. JCO 2002;20:4478-4484
    Randomized Discontuation Design
  • 69. A Proposal for New Drug Development in Cancer
    Small Phase II Trials
    Molecular
    Enrichment
    Molecular
    Enrichment
    Repeat
  • 70. I-SPY2 TRIAL*
    ADAPTIVELY
    RANDOMIZE
    Experimental arm 1
    Experimental arm 2
    Outcome:
    Complete
    Response
    Population
    of 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 Criteria
    CLL
    Cheson, et al, Am J Hematol 29:72, 1988
    Cheson, et al, Blood 87:4990, 1996
    Hallek, Cheson, et al, Blood 111:5446, 2008
    AML
    Cheson, et al, JCO 8:813, 1990
    Cheson et al, JCO 21:4642, 2003
    MDS
    Cheson, et al, Blood 96:3671, 2000
    Cheson, et a, Blood 108:419, 2006
    NHL
    Cheson, et al, JCO 17:1244, 1999
    Cheson, et al, JCO 25:579, 2007
    MM – Don’t blame me!
  • 72. Tumor Flare Reaction
    Flare reaction
    Baseline
    Post-treatment
    Occasional
    • Fever
    • 73. Bone pain
    • 74. ↑ WBC/ALC
  • PI3K Delta Pathway Induces Proliferation Cell survival Trafficking
    Critical Signaling Pathways in B-Cell Malignancies
    Stromal cell
    T-cell
    Signaling
    stimulus
    IL-6
    BAFF
    CXCL13
    IL-6R
    BCR
    CXCR5
    BAFFR
    CD40
    Malignant B-cell membrane
    LYN
    gp130
    gp130
    JAK
    TRAF6
    JAK
    SYK
    a
    LYN/SYK
    b
    g
    PI3KDelta
    STAT
    STAT
    BTK
    BTK
    PLCg2
    PLCg2
    AKT
    T308
    S473
    NF-kB
    pathway
    GSK-3
    PKC
    mTOR
    p70s6k
    elf4E
  • 75. The Road to Individualized Therapy Starts With Science
  • 76. CALGB Current/Planned Protocols in Untreated Follicular NHL
    Low-Intermediate FLIPI
    50402 - 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 Studies
    PET scans
    Molecular profiling, gene expression
    TMAs collected on all patients
    Studies of effector cell number/function
    FOXP3, T-Regs
    Microenvironmental factors, LAMs
    SNPs
    MicroRNAs
  • 78. CALGB:50303 Phase III Randomized Study of
    R-CHOP v. DA-EPOCH-R with Microarray
    Treatment
    completed
    ARM A: R-CHOP
    C3
    C4
    C6
    C1
    C2
    C5
    Stage
    PET/CT
    Stage
    PET/CT
    Randomization
    C3
    C4
    C6
    C1
    C2
    C5
    Tumor Biopsy
    Blood Samples
    ARM B: DA-EPOCH-R
    Treatment
    Completed
    • Gene Expression Profiling
    • 79. GCB v ABC DLBCL Analysis
    • 80. Discovery
    • 81. Whole Genome Sequencing
    • 82. Germ line and Tumor
    • 83. Discovery
    6
    9
    15
    0
    18
    3
    21
    25
    12
    Time Line (weeks)
  • 84. Regulatory Problems
  • 85. Study Design: Relapsed/Refractory CLL
    Fludarabine/Cyclophosphamide
    Eligible
    Patients
    Fludarabine/Cyclophosphamide
    Oblimersen
    Dosing regimen:
    Oblimersen 3 mg/kg/d d1-7
    Fludarabine 25 mg/m2/d d5-7
    Cyclophosphamide 250 mg/m2/d d5-7
    O’Brien et al, JCO 27:5208, 2009
  • 86. Primary Endpoint
    Major Response
    25
    CR
    17%
    nPR
    20
    Percent
    Major
    Response
    15
    P=0.025
    9
    7%
    10
    2
    5
    8
    5
    0
    FC
    Oblimersen/
    FC
    O’Brien et al, JCO 27:5208, 2009
  • 87. Survival curves by treatment arm with 5 yrs follow-up
    ITT population
    CR or PR patients
    Patients with fludarabine-
    sensitive disease
    O'Brien S et al. JCO 2009;27:5208-5212
  • 88. FC +/- Oblimersen in R/R CLL: PFS
    O'Brien S et al. JCO 2007;25:1114-1120
  • 89. Reasons for FDA Rejection
    PFS better than OS in relapsed setting
    PFS secondary endpoint
    No PFS difference between arms
    Small benefit outweighed by adverse effects
    Could not identify pts likely to benefit
  • 90. Safety and Efficacy
    Value
    FDA
    EMEA
  • 91. Challenge to Trigger Paradigm Shift
    CURE FOR CANCER
    Paradigm Shift
    Mind Shift
    Patients & Advocates
    Pharma. & Biotech Co.
    Insurance & Guidelines
    $$$
    Science & Medical
    Government & Policy Makers
  • 92. Life Cycle of Drug Development
    Drug Discovery
    Rebirth
    Epiphany
    Clinical Trial
    Crisis (inactive, toxic)
    Safety +
    Efficacy
    Approval!!
    Pharmacoptosis
    (Programmed drug
    death)