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  1. Statistical Adaptation to Oncology Drug Development Evolution: From Cut/Burn/Poison to ImmunotherapyMingxiu Hu, PhD, ASA FellowSr. Vice President, Data Science and SystemsAdjunct Professor of Biostatistics, Yale University3rdOncoStat Symposium, April 26-27, 2019

  2. Topics • History of Cancer Treatments • Surgery/Radiation/Chemotherapy • History of Immunotherapy • Evolution of Oncology Drug Development • Clinical Trial Design • Analysis Methodology

  3. How does cancer develop? • Immune system • Recognize and kill 99.9999% of mutant cells • 0.00001% escape and lead to uncontrolled cell production–cancer • Cell reproduction error • Sunlight or toxins • Mutated by virus or genetically • Aging • Random error

  4. Surgery/Radiation/Chemotherapy/Targeted Therapy • Surgery (1700’s) and Radiation (1896) • Early stage and localized cancer • Not work with metastatic cancer (majority) • Chemotherapy/Targeted Therapies (1946) • Direct attack on cancer • Kill cancer and normal cells • Lots of side effects • Can come back quickly • Cure of cancer was only a dream • Efficacy is limited • A few month improvement in survival • Quality of life suffers

  5. Early Hints of Immunotherapy • Elizabeth Dashiell • 17 years old, Rockefeller’s lover • Cross-country train journey in the summer of 1890 • Returned in August with a swollen pinkie (half size of an olive) • Grow back a week after a perfect surgery • Diagnosed sarcoma after second surgery • Spread quickly and died on Jan 31, 1891

  6. Early Hints of Immunotherapy Infection made the tumor “hot” • Fred Stein (1885) • 31 years old, a house painter • Egg-sized mass on left cheek • 5 surgeries over 3 years • Tumor roared back after each surgery • Skin grafts left open wound and led to erysipelas infection (deadly infection in 19th century) • Red rashes, raging fever, chills, inflammation, … • Miraculously, his tumor melt away!

  7. Coley’s Toxin • Setbacks for immunotherapy: • 1952, Park Davis stopped production • 1963, FDA not acknowledge as a proven cancer therapy • 1965, American Cancer Society called it “Unproven Methods of Cancer Management” • Invented by Dr. William Coley in 1890’s • Recipe: Meat-water + salt + peptone + bacteria • Induce infection to recruit immune cells to kill cancer • No understanding of immune system, genes, mutations at that time • Inconsistency of efficacy reporting: • Coley’s daughter: 500 remissions out of 1000 patients • A 1960 report: 20 out of 93 • Serious side effect (including death)

  8. How does immune system work? • Cancer: • Sick body cell • Not infected but mutated • Innate Immune Response • Macrophages recognize and eat obviousinvaders (bacteria, viruses, sick cells, etc.) • Adaptive Immune Response • Against unfamiliar invaders • Foreign cells • B-cells mark invaders using its antibodies • Macrophages eat them • Infect body cells to produce viruses: • CD4 helper T-cell: Coordinate • CD8 killer T-cell: Kill

  9. Long road to modern immuno-oncology therapy • ~120 years from Coley’s Toxin (1890) to the first modern immunotherapy (Ipilimumab) 2011 • Long time to understand our immune system: • T-cells was discovered in 1968 • Different types of T-cells (CD4, CD8, CD28, etc.) distinguished later • Many misunderstandings about cancer and immune system: • T-cells cannot see cancer: • No symptoms and too similar to normal cells • Setbacks for immunotherapy for many years due to this misunderstanding • Even Ipilimumab was developed based on a misunderstanding • Immune Response: Recognition → Activation →Attack/Kill

  10. ImmunoncologyTherapy—Checkpoint Inhibitor • Ipilimumab, 2011 • Mistakenly thought CTLA-4 protein is an immune response gas pedal and Ipilimumab is an agonist that promotes immune response • Actually, CTLA-4 is a brake and Ipilimumab is an CTLA-4 inhibitor. Two wrongs make one right • Patent was granted based on the wrong understandings • Inhibit CTLA-4, unleash general activation of T-cell responses • Lots of side effect

  11. Immunoncology Therapy—Checkpoint Inhibitor • PD-1/PD-L1 Inhibitors: • PD-1 inhibitors: • Nivolumaband Prembrolizumab, 2014: • PD-L1 inhibitors: • Atezolizumab, 2016 • PD-1/PD-L1 shot down T-cell attacks after immune response activation • PD-1/PD-L1 inhibition is more specific and less side effect

  12. Immuno-oncology Therapy—CAR-T Cell Therapy • CAR-T Cell Therapy: • Collect a patient’s own T-cells • Modify in vitro by adding CAR gene • Better recognize and attack cancer • Inject back to the patient • So far mainly for liquid tumors (tests ongoing for solid tumors)

  13. Future Direction of Immunotherapies INFLAMED (“hot” tumor) CD8+ T Cells infiltrated But not functional IMMUNE EXCLEDED CD8+ T Cells accumulated but not efficiently infiltrated IMMUNE DESSET CD8+ T Cells absent from tumor and its periphery Respond favorably to checkpoint Convert to inflamed phenotype with inhibition combinations

  14. Future Direction of Immunotherapies Hot Tumor vs Cold Tumor Only hot tumors respond well to checkpoint inhibitors TMB=Tumor Mutation Burden

  15. Future Direction of Immunotherapies Future Immunotherapy will be highly personalized: TMB, PDL1, MSI, MSS, etc IR=Immune Recognition; TIL=Tumor Infiltrating Lymphocytes; MSI=MicroSatellite Instability

  16. Special Statistical Issues with Oncology Immunotherapy Development Focus on two key issues: • Delayed Effect--Non-proportional hazard • Underestimate treatment effect at interim analysis • How to estimate treatment effect • Personalization • Identify sub-populations to distinguish a new treatment • Complex designs

  17. Delayed Effect Examples—Nivolumab in Melanoma and Renal Cell Carcinoma (RCC) PFS in Melanoma OS in Melanoma PFS in RCC OS in RCC

  18. Delayed Effect—More Powerful Test Statistics • Family of Weighted Log-rank Tests: • Log-rank test (Mantel 1966): • Equal weights for all events • Most powerful with PH model • Fleming-Harrington test (1982): • More weights for later events • More powerful with delayed effects

  19. Delayed Effect—More Powerful Test Statistics • test (Miao, Hua, Xue, and Hu, 2019): • LR—Log-rank test; FH—Fleming-Harrington test • Good asymptotic property and distribution • Under PH model, close to LR test and more powerful than FH test • Under delayed effect, close to FH test and more powerful than LR test • A good practical choice when delayed effect is not clear • Whether there is a delayed effect • How long is the delay

  20. Delayed Effect—Power Comparison

  21. Delayed Effect—Sample Size Comparison

  22. Delayed Effect—Estimation of Treatment Effect • Non-proportional hazard • Single piece of HR does not capture real effect • A Practical Approach (see our paper for details): • Estimating separation time • Estimating HR before separation time • Estimating HR after separation time

  23. Delayed Effect—Design Consideration • Group Sequential Designs: • Don’t use futility or use lower futility hurdle • Add more interim analyses (longer PFS and OS) • Build in delayed effect to determine “optimal” timing if information available • Sample/even size Adaptive Design: • Build in delayed effect in determining conditional power, if information available • Delay interim decision • Eliminate futility zone and unfavorable zone to be conservative

  24. Complexity of Trial Designs • Design Factors: • Multiple treatment regimens (different combinations) • Multiple primary endpoints (ORR, PFS, OS) • Multiple tumor types (basket trial) • Multiple interim analyses • Interim adaptations (enrichment, sample size adaptation) • Identify gene signature and confirming treatment effect

  25. Challenges in Identifying Effective Subgroups: Our big data problem is different • Outcome is patients’ life and cannot be lightly tested • Life experiment media experiment • Data collection involves a long, expensive, and strictly regulated process • Most variables are not predictive and predictive ones can only explain a small amount of variability • Statistical methods have to be efficient, reliable, and reproducible, which is lacking • Most gene signature findings are not reproducible