Eshel Ben-Jacob Biochemistry & Cell Biology and CTBP, Rice University School of Physics & Astronomy, Tel Aviv University, Translating Cancer Data and Models to Clinical Practice Institute for Pure & Applied Mathematics, UCLA, Feb 10-14, 2014 Cancer Continues to Elude Us Dormancy and Relapse Metastasis Multiple Drug Resistance Are little understood and clinically insuperable An even Greater Challenge is Posed by the Cancer–Immunity Interplay These small membrane vesicles carry signals to distant parts of the body, where they can impact multiple dimensions of cellular life. Clotilde Théry TheScientist July 1, 2011 Keith Kasnot Zhang and William “Exosomes and Cancer: A Newly Described Pathway of Immune Suppression” Clinical Cancer Research 2011 Camussi et al. “Exosome/microvesicle-mediated epigenetic reprogramming of cells” J. Am. Cancer Research 2011 Exosome secretion Bobrie et al Traffic (2011) A Crash Course in Immunology Rethinking the Immune System Networked society of smart cells Dendritic cells (DC) play a key role in the society’s control and command Exosome-mediated immunity Rethinking Cancer Networked society of smart cells Exosome-mediated tumorigenesis Exosome-based Cancer-Immunity Cyberwar Coaching the Immune System Reflections on the Generic Modeling Approach The Realistic Trap vs. The Reminiscence Syndrome Simplifying the complexity by the art of generic modeling Ben-Jacob Nature 2002 Generic Modeling of the Exosome-mediated Interplay Rethinking the Cancer-Immunity Interplay Therapeutic Implications A Crash Course in Immunology The human body: 1015 bacteria, 1014 cells, 1012 immune cells, 1011 neurons The Dual Function of the Immune System Innate Immunity, Adaptive Immunity and Immune Memory The Complexity Innate Immunity: Natural Killer (NK) cells, Inflammation, Killer and Repair Macrophages Adaptive Immunity: Naïve T cells, Natural Killer T cells, Cytotoxic T cells, Helper T cells, Regulatory T cells, Memory T cells, B lymphocytes, Memory B cells Killer and Repair Macrophages Immature Dendritic Cells Mature Dendritic Cells Helper T cells Innate-DC-Adaptive Dendritic Cell Naïve T cells M1 (killer) Macrophage Dendritic Cell Networked Society of Smart Cells Immune Holography Immune development from Birth to Adulthood Madi et al. PNAS 2009, PLoS ONE 2011, Bransbburg-Zabary et al. Phys. Bio 2013 Hypothesis Dendritic Cells (DC) Play a key role in the society’s control and command Progenitors Mature DCs Bone Marrow (BM) Immature Dendritic cells DC and BM exosomes promote DC differentiation Blood circulation Tumor Stimulate the immune response Ben-Jacob mAbs (monoclonoal antibodies) 2014 Exosome-mediated immunity Exosomes from Antigen-presenting cells (APCs) Activation of NK cells Dendritic cell (DC) DC exosome DC maturation and differentiation Bone marrow exosome Progenitors Bone marrow Exosome-mediated immunity Activation Inhibition A Crash Course in Immunology Rethinking the Immune System Networked society of smart cells Dendritic cells (DC) play a key role in the society’s control and command Exosome-mediated immunity Rethinking Cancer Networked society of smart cells Exosome-mediated tumorigenesis Exosome-based Cancer-Immunity Cyberwar Coaching the Immune System Learning from bacteria about cancer Cancer as a Networked Society of Smart Cells Ben-Jacob, Coffey, Levine Opinion in Trends in Microbiology (2012) Kim et al Cell 2009 Self-seeding Circulating Tumor Cells (CTC) e.g. IL-6, IL-8 EBJ et al Tim 2012 Spying cells e.g. MMP1/ collagenase-1 Path generating Path finding Ben-Jacob et al. 2012 Signals from the Primary tumor Kaplan et al Nature 2005 Exosome-mediated tumorigenesis Wendler et al. J. Extracellular Vesicles July 2013 Azmi et al. Cancer Metastasis Rev. May 2013 Cancer Continues to Elude Us Tumor Can Evade and Deceive the Immune System Example: Tumor-Associated-Macrophages (TAMs) Bone marrow-derived leukocytes are solicited and directed by cancer to adopt unique phenotypes that can facilitate Tumor growth and survival. Rethinking the Cancer-Immunity Interplay A battle between two networked societies of smart cells Exosome-based Cyber-war Between Cancer and the Immune System Munich et al. OncoImmunology Oct 2012 Tumor exosomes IL-6 and Stat3 Yu et al. Journal of Immunology Dec 2007 Blocking DC differentiation FedExosomes: Engineering Therapeutic Exosomes that Truly Deliver Towards Dialysis of Tumor Exosomes Using Bacteria to Coach Dendritic Cells Exosome-based Cancer Vaccination? FedExosomes: Engineering Therapeutic Exosomes that Truly Deliver Marcus and Leonard, Parmaceuticals (2013) Towards Dialysis of Tumor Exosomes A Marleau et al. J. Translational Medicine 2012 B C Using Bacteria to Coach Dendritic Cells Ben-Jacob et al Trends in Microbiology 2012 Next: Engineering Exosome-secreting Bacteria Exosome-based cancer Vaccination? Escudier et al. Journal of Translational Medicine 2005 Tan et al International Jornal of Nanomedicine 2010 Reflections on the Generic Modeling Approach The Realistic Trap vs. The Reminiscence Syndrome Simplifying the complexity by the art of generic modeling Ben-Jacob Nature 2002 Generic Modeling of the Exosome-mediated Interplay Rethinking the Cancer-Immunity Interplay Therapeutic Implications Support at Rice Support at Rice Mingyang Lu, Rice Univ. Jose’ Onuchic, Rice Univ. Bin Huang, Rice Univ. Sam Hanash, MD Anderson Eshel Ben-Jacob, Rice And Tel Aviv Univ. Support at Tel Aviv: The Tauber Family Funds and the Maguey-Glass Chair Bobrie et al Traffic (2011) Our Generic Modeling Approach • Reduced model (to 3 components) • Population dynamics Cell-Cell Communication Network Steady States / Stability Associate with Stages of Cancer Cancer-immunity Landscape Cancer Tumorigenesis Transition Rate Problem Theraputic Strategies Treatment Simulations Cancer Biology Physics/mathematic Generic Modeling of the Exosome-based Cancer-Immunity Interplay C Cancer K D Killer Cells Dendritic Cells The CDK Model With Mingyang Lu, Bin Huang and Jose’ Onuchic, CTBP, and Sam Hanash, MD Anderson A Surprise Prediction It is hard to fight cancer Stable State The Existence of an Intermediate Cancer State Saddle point Saddle point Stable State The effect of immune recognition The meaning of steady-state solutions in light of tumorigenesis The Singular Effect of Exosomes The Effect of Time Delay Therapeutic implications Reassuring retrospect agreement The risk of over treatment The need for two stage therapy The Effect of DC Recognition of Cancer 1 r = 1.0 The effect of immune recognition [ (1- r) + r r = 0.1 r = 0.6 ] The Singular Effect of Exosomes The Absences of Intermediate State Removing the exosome-based communication kDK = 0.05 kDK = 0.15 Effecitve Cancer Cells (Cells/mL) 1200 The Effect of Time Delay 900 600 300 0 0 300 600 900 1200 Dendritic Cells (Cells/mL) 15 days 1200 Effecitve Cancer Cells (Cells/mL) Effecitve Cancer Cells (Cells/mL) 5 days 900 600 300 0 0 300 600 900 1200 Dendritic Cells (Cells/mL) 1200 900 600 300 0 0 300 600 900 1200 Dendritic Cells (Cells/mL) Signals 80 30 days radiation 60 40 20 0 0 20 40 60 80 Effecitve Cancer Cells (Cells/mL) Therapeutic Implications 100 1 Dendritic Cells (Cells/mL) 120 15 1 100 1500 Cancer cells Effecitve Cancer Cells (Cells/mL) Time (Days) 1200 Why? 900 600 40% reduction 300 0 0 20 40 60 Time (Days) 80 100 10 5 Reassuring retrospect agreement Simulations Days DC No fitting! Immune Defects in Breast Cancer Patients after Radiotherapy Standish et al 2008 J Soc Integr Oncol. Days Therapeutic Implications – The Need for Two Stage therapy Stage I Therapy: H2IT Inducing High to Intermediate Cancer State Transitions Stage II Therapy: I2LT Inducing Intermediate to Low Cancer State Transitions Therapeutic Implications: H2IT More efficient protocols – Alternating Therapy 10 days Radiation, 10 days DC therapy, ….. 120 Effecitve Cancer Cells (Cells/mL) Radiation 100 Signals 80 60 40 DC Therapy 20 0 0 50 100 1500 1200 Intermediate State 900 600 300 0 150 0 600 900 1200 1500 Dendritic Cells (Cells/mL) 1500 1200 Dendritic Cells (Cells/mL) Effecitve Cancer Cells (Cells/mL) Time (Days) 300 1200 900 600 300 0 1000 800 600 400 200 0 0 50 100 Time (Days) 150 0 50 100 Time (Days) 150 Surprise Prediction Effecitve Cancer Cells (Cells/mL) 120 100 Signals 80 60 40 20 0 0 50 100 1500 1200 900 600 300 0 150 0 600 900 1200 1500 Dendritic Cells (Cells/mL) 1200 Risk of Extra Treatment 1500 Dendritic Cells (Cells/mL) Effecitve Cancer Cells (Cells/mL) Time (Days) 300 1200 900 600 300 0 1000 800 600 400 200 0 0 50 100 Time (Days) 150 0 50 100 Time (Days) 150 H2IT by Optimal Path Therapy 4 days Radiation, 2 days DC therapy, ….. Effecitve Cancer Cells (Cells/mL) 120 100 Signals 80 60 40 20 0 0 50 100 150 1500 1200 900 600 300 0 200 0 600 900 1200 1500 Dendritic Cells (Cells/mL) 1500 1200 Dendritic Cells (Cells/mL) Effecitve Cancer Cells (Cells/mL) Time (Days) 300 1200 900 600 300 0 1000 800 600 400 200 0 0 50 100 Time (Days) 150 200 0 50 100 Time (Days) 150 200 Stage II Therapy: I2LT Inducing Intermediate to Low Cancer State Transitions Radiation Stage II Therapy: I2LT Inducing Intermediate to Low Cancer State Transitions Radiation DC Therapy Stage II Therapy: I2LT Inducing Intermediate to Low Cancer State Transitions DC Therapy New Hope Rethinking the Interplay Between Cancer and the Immune System Understanding the Role of Exosomes The Existence of Intermediate State Optimal Path Based Alternating Therapy Two stage Therapy The End