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


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

  1. 1. Problems of the Design and Interpretation of Very Early Clinical Trials in Hemato-Oncology<br />Bruce D. Cheson, M.D.<br />Georgetown University Hospital<br />Lombardi Comprehensive Cancer Center<br />Chair, CALGB Lymphoma Committee<br />Washington, D.C., USA<br />
  2. 2. Earliest Published Clinical Trials<br />Daniel 1:11-20. Health of Hebrews fed a Kosher diet vs Babylonians fed on a state diet<br />Scurvy on the HMS Salisbury: <br /> cider vs vinegar vs lemons<br />
  3. 3. Phases of Clinical Trials<br />Phase I - determine toxicity<br />Phase II - determine activity<br />Phase III - evaluate efficacy<br />Phase IV - post-marketing<br />
  4. 4. The Problem<br />6500 trials open to accrual in the U.S. (clinicaltrials.gov)<br />3% of patients go on studies<br />Studies compete for same patients<br />63% of trials are ever completed<br />
  5. 5. Why our trials fail<br />Blame the design<br />Blame the physicians<br />Blame the patients<br />Blame the wealth of new agents<br />Rarely do we blame the science<br />
  6. 6. Problems in New Drug Development<br />Slow rate of accrual to clinical trials<br />Lack of good preclinical models<br />Lack of surrogate endpoints<br />False negatives – missing an active agent<br />False positives – waste resources on ineffective agent<br />
  7. 7. High False Positive Rate<br />Rely on a single study<br />Unpredictability of intermediate endpoints<br />Get excited by waterfall plots<br />Short follow-up<br />Response duration<br />Toxicity<br />Unplanned subset analyses<br />
  8. 8.
  9. 9. Will Rogers<br /> (1879-1935)<br />
  10. 10. The Will Rogers Effect<br />“When the Okies left Oklahoma and moved to California, they raised the average intelligence level in both states!”<br />
  11. 11. Or, in non-Cowboy Terms ~ <br />A positive effect will occur when both of the following conditions are met ~<br /><ul><li>The element being moved is below average for its current set. Removing it will, by definition, raise the average of the remaining elements
  12. 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</li></li></ul><li>Examples<br />Consider the sets R and S<br /><ul><li>R= (1, 2, 3, 4) – Mean = 2.5
  13. 13. S= (5, 6, 7, 8, 9) – Mean = 7
  14. 14. Moving 5 to R
  15. 15. Mean of R increases to 3
  16. 16. Mean of S increases to 7.5</li></li></ul><li>Relevance to Clinical Trials: Stage Migration<br />Improved detection of illness leads to the movement of people from the set of healthy to the set of unhealthy<br />Example: Increased sensitivity of PET scans in lymphoma<br /><ul><li>Moves “early” stage to advanced stage
  17. 17. Moves false-positive advanced stage to early stage
  18. 18. Improves the outcome of both cohorts</li></li></ul><li>CT from PET/CT - axial view<br />
  19. 19. Contrast enhanced CT - axial view<br />
  20. 20. PET - axial view<br />
  21. 21. PET-coronal<br />
  22. 22. Reducing the False Negative Rate<br />Recognize tumor diversity<br />Understand tumor biology<br />
  23. 23. Not All Patients or Their Diseases Are Created Equal<br />
  24. 24. Lymphoma Cancer Facts<br />Lymphomas represent the 5th most common form of cancer in the US<br />2011 projected >75,000 new cases in the US alone with ~ 25,000 deaths/yr<br />~ 60 morphologic/immunologic subtypes<br />Many more by molecular-genetic assessment<br />No two lymphomas are alike and treatments must be selected based on an individual’s tumor characteristics, by personalized medicine <br />
  25. 25. Antoni van Leeuwenhoek (1632-1723)<br />Invented the microscope around 1668<br />21<br />
  26. 26. TreatmentAdvice<br />Contrast of Appearance vs.Gene Expression Profiling<br />Microscope<br />DLBCL<br />DLBCL<br />High Risk<br />Low Risk<br />Microarray<br />22<br />
  27. 27. Lenz et al., N. Engl. J. Med. 2008<br />A study of the Lymphoma Leukemia Molecular Profiling Project (LLMPP)<br />
  28. 28. Survival curves in diffuse large B-cell lymphoma treated with Bortezomib-R-CHOP<br />Ruan J et al. JCO 2011;29:690-697<br />
  29. 29. Lenalidomide in DLBCL<br />Hernandez-Ilizaliturri et al, Cancer (e-pub, 2011)<br />
  30. 30. Lenalidomide in DLBCL (n=40)<br />Response Rates<br />GCB - 9% (4% CR)<br />Non-GCB – 53% (5% CR)<br />P = .006<br />Hernandez-Ilizaliturri et al, Cancer (e-pub, 2011)<br />
  31. 31. Microvessel Density in DLBCL<br />Cardesa-Salzmann et al, Haematol 2011 (e-pub ahead of print)<br />
  32. 32. L<br />H<br />Cardesa-Salzmann et al, Haematol 2011 (e-pub ahead of print)<br />
  33. 33. Clinical Factors:<br />-FLIPI<br />-F2<br />Biological Factors:<br />-Histology<br />-Molecular<br />-Cytogenetic<br />-Protein-mRNA<br />Predictors of Clinical Behavior in FL<br />Host factors?-FCR SNPs<br />Microenvironment-Others<br />
  34. 34. Overall survival based on lymphoma associated macrophage (LAM) content<br />Farinha, P. et al. Blood 2005;106:2169-2174<br />
  35. 35. .<br />Weng W , Levy R JCO 2003;21:3940-3947<br />FcgRIII Polymorphisms and Response to Rituximab<br />
  36. 36. CORAL maintenance: PFS by gender in maintenance and observation arms<br />1.0<br />1.0<br />0.8<br />0.8<br />0.6<br />0.6<br />Survival probability<br />Survival probability<br />0.4<br />0.4<br />0.2<br />0.2<br />p = 0.0044<br />p = 0.5921<br />0.0<br />0.0<br />0<br />12<br />24<br />36<br />48<br />60<br />72<br />0<br />12<br />24<br />36<br />48<br />60<br />72<br />PFS (months)<br />PFS (months)<br />Female<br />Female<br />Male<br />Male<br />Rituximab<br />Observation<br />
  37. 37.
  38. 38. Probability of Survival<br />100<br />80<br />60<br />40<br />20<br />0<br />0<br />24<br />48<br />72<br />96<br />120<br />144<br />168<br />Months<br />13q deletion<br />Patients surviving (%)<br />12q trisomy<br />11q deletion<br />Normal<br />17p deletion<br />Döhner H, et al. N Engl J Med. 2000;343:1910-1916.<br />
  39. 39. Outcome With Alemtuzumab in CLL Cytogenetic Subgroups.<br />Stilgenbauer S et al. JCO 2009;27:3994-4001<br />
  40. 40. 100<br />100<br />80<br />80<br />Surviving (%)<br />60<br />60<br />Surviving (%)<br />P=0.0008<br />P=0.001<br />40<br />40<br />20<br />20<br />0<br />0<br />50<br />100<br />150<br />200<br />250<br />300<br />0<br />0<br />50<br />100<br />150<br />200<br />250<br />300<br />Months<br />Months<br />Survival of CLL Patients With Mutated vsUnmutated VH Genes<br />Stage-A CLL patients (n=62)<br />All patients (n=84)<br />Mutated<br />Mutated<br />Unmutated<br />Unmutated<br />Damle RN, et al. Blood. 1999;94:1840-1847.<br />Hamblin TJ, et al. Blood. 1999;94:1848-1854<br />
  41. 41. Genes That Best Discriminate Ig-Unmutated and Ig-mutated CLL<br />Ig-Unmut Ig-Mut<br />
  42. 42. Kaplan-Meier Estimates Based on Level of ZAP-70 Expression<br />Likelihood of survival<br />Risk of disease progression<br />100<br />90<br />80<br />70<br />60<br />50<br />40<br />30<br />20<br />10<br />0<br />100<br />90<br />80<br />70<br />60<br />50<br />40<br />30<br />20<br />10<br />0<br /><20% ZAP-70–positive cells<br />20% ZAP-70–positive cells<br />% Progression<br />% surviving<br /><20% ZAP-70–positive cells<br />20% ZAP-70–positive cells<br />P=0.009<br />P=0.01<br />0 2 4 6 8 10 12 14 16<br />0 4 8 12 16 20 24 28 32 36<br />Years after diagnosis<br />Years after diagnosis<br />Crespo M, et al. N Engl J Med. 2003;348:1764-1775.<br />
  43. 43. The Nature of the Disease<br />
  44. 44. Pathway vs. Network signaling<br />Pathway<br />“Newtonian”<br />Network<br />“Chaotic”<br />A. Friedman and N. Perrimon, Cell 128, January 26, 2007<br />
  45. 45. Shc<br />Grb2<br />PI3-K<br />Sos-1<br />Ras<br />AKT<br />Raf<br />MEKK-1<br />MEK<br />mTOR<br />MKK-7<br />ERK<br />JNK<br />Which Target?<br />
  46. 46. Sos-1<br />PI3-K<br />Ras<br />Grb2<br />Shc<br />Raf<br />MEK<br />MEKK-1<br />JNK<br />MKK-7<br />ERK<br />AKT<br />The Target Interactome<br />Where’s the target?<br />Courtesy of I. Serebriiskii and E. Golemis, Fox Chase Cancer Center<br />
  47. 47. Cheson’s Rule<br />“The efficacy of a new drug is directly proportional to its negative impact on clinical research”<br />
  48. 48. Victims of Our Own Successes: The Disease<br />Front-line HL<br />Front-line DLBCL<br />Front-line FL<br />Front-line CLL<br />
  49. 49. Victims of Our Own Successes: The Drug<br />New drug B is very active in relapsed/refractory disease<br />Rapidly adopted as front-line<br />Problems<br />No drug B naïve pts against which to compare drug C<br />No data on other drugs in B-relapsed pts<br />Results in relapse too good to improve upon<br />Results in front-line too good to improve upon<br />
  50. 50. Examples<br />TK inhibitors in CML<br />B(R) in F/LG NHL<br />B(R) in CLL<br />Brentuximabvedotin in HL<br />
  51. 51. Solutions<br />Drug B +/- drug C in relapsed-refractory pts<br />Takes a randomized trial<br />Long time for answer<br />Drug C in B-refractory pts<br />No data with other agents<br />Risky<br />
  52. 52. Do We Have Surrogate Endpoints?<br />
  53. 53. 1.0<br />0.8<br />0.6<br />0.4<br />0.2<br />0.0<br />0<br />12<br />24<br />36<br />48<br />84<br />60<br />72<br />Time (months)<br />MRD Response and Treatment-Free Survival After Alemtuzumab Treatment (N=91)<br />MRD-negative CR/PR (n=18)<br />P<0.0001<br />Cumulative probability<br />MRD-positive CR (n=14)<br />MRD-positive PR (n=17)<br />Nonresponders (n=42)<br />Moreton P et al. J Clin Oncol. 2005;23:2971-2979.<br />
  54. 54. PRIMA: Primary endpoint (PFS): 36 months’ follow-up after randomisation<br />1.0<br />0.8<br />75%<br />Rituximab maintenance<br />0.6<br />Event-free rate<br />58%<br />0.4<br />Observation<br />Stratified HR = 0.55<br />95% CI: 0.44–0.68<br />p < 0.0001<br />0.2<br />0.0<br />0<br />6<br />12<br />18<br />24<br />30<br />36<br />42<br />48<br />54<br />60<br />Time (months)<br />Patients at risk<br />505<br />472<br />445<br />423<br />404<br />307<br />207<br />84<br />17<br />0<br />–<br />513<br />469<br />415<br />367<br />334<br />247<br />161<br />70<br />16<br />0<br />–<br />GA Salles et al. ASH 2010, Abstract 1788 <br />
  55. 55. Progression-free survival(from study registration)Conventional response assessment<br />1.0<br />CR/CRu<br />PR<br />0.8<br />SD/PD<br />0.6<br />Probability of PFS<br />0.4<br />0.2<br />p < 0.0001<br />0.0<br />0<br />6<br />12<br />18<br />24<br />30<br />36<br />42<br />48<br />54<br />60<br />Time (months)<br />No. of subjects<br />Event<br />Censored<br />Median PFS (months)<br />CR/CRu<br />89<br />35% (31)<br />65% (58)<br />52<br />PR<br />27<br />44% (12)<br />56% (15)<br /> NR<br />SD/PD<br /> 8<br />88% (7)<br />13% (1)<br />7<br />
  56. 56. Progression-free survival(from study registration) Post-treatment PET-CT based assessment<br />1.0<br />PET negative<br />PET positive<br />74%<br />0.8<br />0.6<br />Probability of PFS<br />0.4<br />32%<br />HR = 3.5 (95% CI 2.0-6.1)<br />p < 0.0001<br />0.2<br />0.0<br />0<br />6<br />12<br />18<br />24<br />30<br />36<br />42<br />48<br />54<br />60<br />Time (months)<br />Event<br />Censored<br />Median PFS (months)<br />No. of subjects<br />PET negative<br />31% (28)<br />69% (63)<br />NR<br />91<br />PET positive<br />33% (11)<br />19<br />33<br />67% (22)<br />
  57. 57. Progression-free survival according to IPS group and PET results after two cycles of ABVD<br />Gallamini, A. et al. J Clin Oncol; 25:3746-3752 2007<br />
  58. 58. BEACOPP treatment for 154 PET-2- positive advanced-stage HL patients <br />PET2-neg<br />All pts<br />PET2+<br />Gallamini A. et al, Br J Haematol, 152:551, 2011<br />
  59. 59. CALGB Risk-Adapted Studies in HL<br />
  60. 60. Variables Influencing Interim PET Results<br />Disease<br />Stage<br />Therapy<br />Method of interpretation<br />
  61. 61. Accelerate Completion of Clinical Trials<br />Activate fewer studies<br />Consider alternative funding sources<br />Identify surrogate endpoints<br />Novel statistical designs<br />
  62. 62. Is There Still a Role For Traditional Phase I Studies in Haematologic Malignancies?<br />
  63. 63. Dose Escalation of Rituximab in CLL<br /> Dose<br />Patients with toxicity<br />2<br />(mg/m )<br />No.<br />No.<br />Grade<br /> 500<br /> 3<br /> 0<br /> -<br /> 650<br /> 3<br /> 1<br />1+ nausea<br /> 825<br /> 3<br /> 0<br /> -<br /> 1000<br /> 3<br /> 2<br />1+ malaise, nausea<br /> 1500<br /> 4<br /> 1<br />dyspnea, dose 2<br /> 2250<br />10<br /> 5<br />2+ fever, chills<br />1+ nausea, fatigue<br />$LT<br />O’Brien et al, JCO 19:2165-70, 2001<br />
  64. 64. Need to Consider Novel Study Designs<br />
  65. 65. Randomized Discontinuation Design<br />Premise:<br /><ul><li>Some drugs are more static than tumouricidal
  66. 66. Tumour growth may be slow
  67. 67. Single arm study cannot identify improved PFS</li></ul>Enrichment design<br />Select/randomize relatively homogenous patients<br />
  68. 68. .<br />Rosner G L et al. JCO 2002;20:4478-4484<br />Randomized Discontuation Design<br />
  69. 69. A Proposal for New Drug Development in Cancer<br />Small Phase II Trials<br />Molecular <br />Enrichment<br />Molecular <br />Enrichment<br />Repeat<br />
  70. 70. I-SPY2 TRIAL*<br />ADAPTIVELY<br />RANDOMIZE<br /> Experimental arm 1<br /> Experimental arm 2<br />Outcome:<br />Complete <br />Response <br />Population<br />of patients<br /> Experimental arm 3<br /> Experimental arm 4<br /> Experimental arm 5<br /> Standard therapy<br />*Investigation of Serial Studies to Predict Your Therapeutic Response with <br />Imaging And moLecular Analysis 2<br />
  71. 71. Sometimes We Are Misled By Rigid Response Criteria<br />CLL<br />Cheson, et al, Am J Hematol 29:72, 1988<br />Cheson, et al, Blood 87:4990, 1996<br />Hallek, Cheson, et al, Blood 111:5446, 2008<br />AML <br />Cheson, et al, JCO 8:813, 1990<br />Cheson et al, JCO 21:4642, 2003<br />MDS<br />Cheson, et al, Blood 96:3671, 2000<br />Cheson, et a, Blood 108:419, 2006<br />NHL<br />Cheson, et al, JCO 17:1244, 1999<br />Cheson, et al, JCO 25:579, 2007<br />MM – Don’t blame me!<br />
  72. 72. Tumor Flare Reaction<br />Flare reaction<br />Baseline<br />Post-treatment<br />Occasional<br /><ul><li>Fever
  73. 73. Bone pain
  74. 74. ↑ WBC/ALC</li></li></ul><li>PI3K Delta Pathway Induces Proliferation Cell survival Trafficking<br />Critical Signaling Pathways in B-Cell Malignancies<br />Stromal cell<br />T-cell<br />Signaling<br />stimulus<br />IL-6<br />BAFF<br />CXCL13<br />IL-6R<br />BCR<br />CXCR5<br />BAFFR<br />CD40<br />Malignant B-cell membrane<br />LYN<br />gp130<br />gp130<br />JAK<br />TRAF6<br />JAK<br />SYK<br />a<br />LYN/SYK<br />b<br />g<br />PI3KDelta<br />STAT<br />STAT<br />BTK<br />BTK<br />PLCg2<br />PLCg2<br />AKT<br />T308<br />S473<br />NF-kB<br />pathway<br />GSK-3<br />PKC<br />mTOR<br />p70s6k<br />elf4E<br />
  75. 75. The Road to Individualized Therapy Starts With Science<br />
  76. 76. CALGB Current/Planned Protocols in Untreated Follicular NHL<br />Low-Intermediate FLIPI<br />50402 - Rituximab + galiximab (completed)<br />50404 - Rituximab + oblimersen (RIP)<br />50701 - Rituximab + epratuzumab (completed)<br />50803 - Rituximab + lenalidamide (near completion)<br />50901 – Ofatumumab (500 mg vs 1000 mg)<br />511XX – Rituximab/Lenalidomide +/- PCI32765<br />
  77. 77. Correlative Studies<br />PET scans<br />Molecular profiling, gene expression<br />TMAs collected on all patients<br />Studies of effector cell number/function<br />FOXP3, T-Regs<br />Microenvironmental factors, LAMs<br />SNPs<br />MicroRNAs<br />
  78. 78. CALGB:50303 Phase III Randomized Study of <br />R-CHOP v. DA-EPOCH-R with Microarray <br />Treatment<br />completed<br />ARM A: R-CHOP<br />C3<br />C4<br />C6<br />C1<br />C2<br />C5<br />Stage<br />PET/CT<br />Stage<br />PET/CT<br />Randomization <br />C3<br />C4<br />C6<br />C1<br />C2<br />C5<br />Tumor Biopsy<br />Blood Samples<br />ARM B: DA-EPOCH-R<br />Treatment<br />Completed <br /><ul><li> Gene Expression Profiling
  79. 79. GCB v ABC DLBCL Analysis
  80. 80. Discovery
  81. 81. Whole Genome Sequencing
  82. 82. Germ line and Tumor
  83. 83. Discovery </li></ul>6<br />9<br />15<br />0<br />18<br />3<br />21<br />25<br />12<br />Time Line (weeks)<br />
  84. 84. Regulatory Problems<br />
  85. 85. Study Design: Relapsed/Refractory CLL<br />Fludarabine/Cyclophosphamide<br />Eligible<br />Patients<br />Fludarabine/Cyclophosphamide<br />Oblimersen<br />Dosing regimen:<br />Oblimersen 3 mg/kg/d d1-7<br />Fludarabine 25 mg/m2/d d5-7<br />Cyclophosphamide 250 mg/m2/d d5-7<br />O’Brien et al, JCO 27:5208, 2009<br />
  86. 86. Primary Endpoint<br />Major Response<br />25<br />CR<br />17%<br />nPR<br />20<br />Percent<br />Major<br />Response<br />15<br />P=0.025<br />9<br />7%<br />10<br />2<br />5<br />8<br />5<br />0<br />FC<br />Oblimersen/<br />FC<br />O’Brien et al, JCO 27:5208, 2009<br />
  87. 87. Survival curves by treatment arm with 5 yrs follow-up<br />ITT population<br />CR or PR patients<br />Patients with fludarabine-<br />sensitive disease<br />O'Brien S et al. JCO 2009;27:5208-5212<br />
  88. 88. FC +/- Oblimersen in R/R CLL: PFS<br />O'Brien S et al. JCO 2007;25:1114-1120<br />
  89. 89. Reasons for FDA Rejection<br />PFS better than OS in relapsed setting<br />PFS secondary endpoint<br />No PFS difference between arms<br />Small benefit outweighed by adverse effects<br />Could not identify pts likely to benefit<br />
  90. 90. Safety and Efficacy<br />Value<br />FDA<br />EMEA<br />
  91. 91. Challenge to Trigger Paradigm Shift <br />CURE FOR CANCER<br />Paradigm Shift<br />Mind Shift<br />Patients & Advocates<br />Pharma. & Biotech Co.<br />Insurance & Guidelines <br />$$$<br />Science & Medical <br />Government & Policy Makers<br />
  92. 92. Life Cycle of Drug Development<br />Drug Discovery<br />Rebirth<br />Epiphany<br />Clinical Trial<br />Crisis (inactive, toxic)<br />Safety +<br />Efficacy<br />Approval!!<br />Pharmacoptosis<br />(Programmed drug<br />death)<br />