Download Rethinking Cancer

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