Workshop 3: Cancer and the Immune System

(November 17,2014 - November 21,2014 )

Organizers


Avner Friedman
Department of Mathematics, The Ohio State University
Gregory Lesinski
Molecular Virology Immunology and Medical Genetics, The Ohio State University
Ami Radunskaya
Mathematics, Pomona College

Cancer immunology is the study of the interactions between the immune system and cancer cells. The aim is to discover innovative therapies using the immune system to retard the progression of the disease. The immune system aims to identify and destroy pathogenic microorganisms that invade our body. Cancer cells, however, are cells of our body, so the immune system may not recognize them as "enemy." In fact, cancer cells often even exploit the immune cells to help them proliferate. Tumor associated macrophages, for instance, are known to influence cancer cells by modulating immune functions, and accelerating angiogenesis, but not much is known on the cytokine signaling network that regulate this process. Recent years have seen the development of cancer immunotherapy, that is, the use of the immune system to attack malignant tumor cells. This can be achieved either by immunization of the patient by a vaccine, or by administering a therapeutic antibody as a drug which will recruit the immune system to recognize and destroy tumor cells. Recent years have also seen the development of mathematical models that aim to represent, at least at the conceptual level, the cancer-immune interactions, as well as models that represent immunotherapy processes. These models are formulated by systems of ODEs or PDEs. The present workshop will bring together cancer biologists and mathematical modelers to review the state of present knowledge and explore future directions. It will also provide an environment that will encourage communication and new contacts among the biologists and mathematicians. Formal lecture and informal discussions will articulate future directions where mathematical models can significantly enhance understanding of the complex relations between tumor cells and the immune cells, and suggest novel approaches to therapy.

Accepted Speakers

Baltazar Aguda
Founder & CEO, Disease Pathways, LLC
Xue-Feng Bai
Experimental Pathology, The Ohio State University
Helen Byrne
Centre for Collaborative Applied Mathematics, University of Oxford
Duan Chen
Department of Mathematics, University of North Carolina, Charlotte
Lisette de Pillis
Mathematics, Harvey Mudd College
Raluca Eftimie
Division of Mathematics, University of Dundee
Tim Eubank
Internal Medicine/Pulmonary Medicine, The Ohio State University
Thomas Hillen
Mathematical and Statistical Sciences, University of Alberta
Sarah Hook
School of Pharmacy, University of Otago
Karly Jacobsen
Mathematical Biosciences Institute, The Ohio State University
Jenny Jiang
Biomedical Engineering, The University of Texas at Austin
Yangjin Kim
Department of Mathematics, Konkuk University
Yang Kuang
Department of Mathematics and Statistics, Arizona State University
Urszula Ledzewicz
Department of Mathematics and Statistics, Southern Illinois University
Doron Levy
Department of Mathematics, University of Maryland
Kang-Ling Liao
Mathematical bioscience institute, The Ohio State University
Michael Lotze
Professor of Surgery and Bioengineering, University of Pittsburgh
Yoram Louzoun
Mathematics, bar ilan university
Shari Pilon-Thomas
Immunology Program, H. Lee Moffitt Cancer Center & Research Institute
Daniel Powell
Pathology & Laboratory Medicine, University of Pennsylvania
Mark Robertson Tessi
Integrated Mathematical Oncology, Moffitt Cancer Center
Shigui Ruan
Department of Mathematics, University of Miami
Theresa Whiteside
Pathology, Immunology and Otolaryngology, University of Pittsburgh School of Medicine
Chuan Xue
Department of Mathematics, The Ohio State University
Weiping Zou
Immunology and Biology, University of Michigan
Monday, November 17, 2014
Time Session
08:00 AM

Shuttle Pick Up to MBI

08:15 AM
08:45 AM

Breakfast

08:45 AM
09:00 AM

Introductions and Information - Marty Golubitsky

09:00 AM
09:45 AM
Avner Friedman - Cancer and the immune system

This is tutorial talk, in which I will introduce the main components of the immune system in the context of cancer. I will introduce the different phenotypes of macrophages, the four classes of CD4+ T cells, and the cytotoxic T cells (CTL) or CD8+ T cell. As will be explained, the tumor may be recognized by macrophages and dendritic cells, and these cells will then activate 'effective' T cells to kill cancer cells. However, the tumor can fight back against the immune system, and in fact it can even use the system to its own advantage, by "educating" macrophages so that they will actually enhance tumor growth by increasing VEGF production. Another factor that works in favor of a tumor are the T regulatory cells, enhanced by the tumor, which inhibit the activities of the effective T cells.

09:45 AM
10:30 AM
Ami Radunskaya
10:30 AM
11:00 AM

Break

11:00 AM
11:45 AM
Theresa Whiteside - Regulatory T cell networks in human cancer and immune therapies

Abstract not submitted.

11:45 AM
12:30 PM
Jenny Jiang
12:30 PM
02:00 PM

Lunch

02:00 PM
02:45 PM
Weiping Zou - T cell subsets in the tumor microenvironment

Abstract not submitted.

02:45 PM
03:30 PM
Michael Lotze
03:30 PM
04:15 PM

Break

04:15 PM
05:00 PM
Gregory Lesinski
05:00 PM
07:00 PM

Reception and Poster Session

07:15 PM

Shuttle Pick Up to Hotel

Tuesday, November 18, 2014
Time Session
08:00 AM

Shuttle Pick Up to MBI

08:15 AM
09:00 AM

Breakfast

09:00 AM
09:45 AM
Daniel Powell
09:45 AM
10:30 AM
Shari Pilon-Thomas - Adoptive cell therapy using tumor infiltrating lymphocytes (TIL)

Abstract not submitted.

10:30 AM
11:00 AM

Break

11:00 AM
11:45 AM
Tim Eubank - Regulation of angiogenesis by Tie2-expressing monocytes/macrophages in breast cancer

Abstract not submitted.

11:45 AM
02:00 PM

Lunch

02:00 PM
02:45 PM
Sarah Hook - Timing is everything: getting the right therapies to the right place at the right time

Abstract not submitted.

02:45 PM
03:30 PM
Xue-Feng Bai
03:30 PM
04:00 PM

Break

04:00 PM
05:30 PM

Panel Discussion (Lesinski and Lotze)

05:45 PM

Shuttle Pick Up to Hotel

Wednesday, November 19, 2014
Time Session
08:00 AM

Shuttle Pick Up to MBI

08:15 AM
09:00 AM

Breakfast

09:00 AM
09:30 AM
Scott Abrams - Mechanisms Underlying Altered Hematopoiesis in Solid Cancer Biology

Abstract not submitted.

09:30 AM
10:00 AM
Baltazar Aguda - Creating Cell Interaction Network Models Based on the Cancer Immunoediting Hypothesis

The cancer immunoediting hypothesis provides a conceptual framework that explains the immune system's dual role of inhibiting and promoting tumor growth. I will discuss the progress in the experimental validation of this hypothesis and propose cell interaction network models consistent with it.

10:00 AM
10:30 AM

Break

10:30 AM
11:00 AM
Helen Byrne
11:00 AM
11:30 AM
Lisette de Pillis - Modeling Cancer-Immune System Dynamics

We will present a variety of mathematical models of tumor-immune interactions that have resulted from interdisciplinary collaborations with practicing oncologists and experimentalists. We will discuss certain approaches to modeling cancer growth and immune system interactions, and treatment approaches that harness the power of the immune system to slow and sometimes stop cancer progression.

11:30 AM
02:00 PM

Lunch

02:00 PM
02:30 PM
Thomas Hillen - Individual cell model for the interactions between cancer cells and macrophages at the tumor invasion front.

The interactions between cancer cells and immune cells is one of the hallmarks of cancer. In this talk we present an individual based model, including the interactions between macrophages and cancer cells at the tumor invasion front. The model is based on the Glazier-Graner-Hogeweg

modeling approach (Cellular Potts Model). In this model the macrophages and cancer cells interact via adhesion modeled using the Differential Adhesion Hypothesis and a paracrine loop (Epidermal Growth Factor (EGF) and Colony Stimulating Factor (CSF)). We further present an extension to this model in which the macrophages increase the motility of close by cancer cells. This is a first try at incorporating the Epithelial Mesenchymal Transition (EMT) in this model.

02:30 PM
03:00 PM
Yoram Louzoun - Bistability induced by tumor-immune system interactions.

Many tumors target the specific immune system cells raised to protect the host against tumor. This targeting can take two main shapes. Either the tumor cells attract immune cells to the tumor, enhancing the tumor growth, or they prevent the immune cells from killing existing tumor cells. In both cases a positive feedback loop emerges between the tumor and immune system cell concentrations.

Such a feedback loop may explain the equilibrium obtained between the host immune system and tumors, where tumors stop growing, or grow very slowly, but are not destroyed by the immune response. While tumor size can increase by a factor of 10 within a day, in most cases, this huge division rate is not obtained and in reality the tumor is almost in equilibrium. This equilibrium can be explained by a bi-stable solution of the tumor-immune system dynamics.

We here study a generic set of feedback loops between the immune system cells and their targets in tumors and show that a bi-stable solution can emerge. This solution can occur only if the tumor induces death or inactivation of macrophages. In such a case, a simple negative effect of the pathogens on the macrophages will suffice to induce bistability. The initial conditions then becomes crucial for the solution, given that, according to it, the solution tends to one or the other fixed point, which correspond to a healthy or a sick state.

We show that double inhibition positive feedback loops (immune system kills tumor, which in turn kill immune system cells) behave differently than double activation feedback loops (Tumor produce cytokines that attract immune cells, which in turn produce cytokines which induce tumor growth).Double inhibition feedback loops induce bi-stability in most parameter space, while double activation feedback loops induce such a bistability in very limited regions of parameter space.

03:00 PM
03:30 PM

Break

03:30 PM
05:00 PM

Poster Chalk Talks (5-10 Minutes Each)

05:15 PM

Shuttle Pick Up to Hotel

Thursday, November 20, 2014
Time Session
08:00 AM

Shuttle Pick Up to MBI

08:15 AM
09:00 AM

Breakfast

09:00 AM
09:30 AM
Duan Chen - Involvement of tumor macrophage HIFs in chemotherapy effectiveness: Mathematical modeling of oxygen, pH, and glutathione.

The four variables, hypoxia, acidity, high glutathione (GSH) concentration and fast reducing rate (redox) are distinct and varied characteristics of solid tumors compared to normal tissue. These parameters are among the most significant factors underlying the metabolism and physiology of solid tumors, regardless of their type or origin. Low oxygen tension contributes to both inhibition of cancer cell proliferation and therapeutic resistance of tumors; low extracellular pH, the reverse of normal cells, mainly enhances tumor invasion; and dysregulated GSH and redox potential within cancer cells favor their proliferation. In fact, cancer cells under these microenvironmental conditions appreciably alter tumor response to cytotoxic anti- cancer treatments. Recent experiments measured the in vivo longitudinal data of these four parameters with tumor development and the corresponding presence and absence of tumor macrophage HIF-1α or HIF-2α in a mouse model of breast cancer. In the current paper, we present a mathematical model-based system of (ordinary and partial) differential equations to monitor tumor growth and susceptibility to standard chemotherapy with oxygen level, pH, and intracellular GSH concentration. We first show that our model simulations agree with the corresponding experiments, and then we use our model to suggest treatments of tumors by altering these four parameters in tumor microenvironment. For example, the model qualitatively predicts that GSH depletion can raise the level of reactive oxygen species (ROS) above a toxic threshold and result in inhibition of tumor growth.

09:30 AM
10:00 AM
Raluca Eftimie - Oncolytic viral therapies and the balance between the memory and effector immune responses

Abstract not submitted.

10:00 AM
10:30 AM

Break

10:30 AM
11:00 AM
Yang Kuang - A mathematical model for the immunotherapy of advanced prostate cancer

A mathematical model of advanced prostate cancer treatment is developed to examine the combined effects of androgen deprivation therapy and immunotherapy. Androgen deprivation therapy has been the primary form of treatment for advanced prostate cancer for the past 50 years. While initially successful, this therapy eventually results in a relapse after two to three years in the form of androgen-independent prostate cancer. Intermittent androgen deprivation therapy attempts to prevent relapse by cycling the patient on and off treatment. Over the past decade, dendritic cell vaccines have been used in clinical studies for the immunotherapy of prostate cancer with some success. Our model examines the efficacy of dendritic cell vaccines when used with continuous or intermittent androgen deprivation therapy schedules. Numerical simulations of the model suggest that immunotherapy can successfully stabilize the disease using both continuous and intermittent androgen deprivation.

11:00 AM
11:30 AM
Yangjin Kim - How can failure in immune response (Th17, Neutrophil, Tregs) lead to the tumor invasion in lung cancer development?: A mathematical model

Abstract not submitted.

11:30 AM
02:00 PM

Lunch

02:00 PM
02:30 PM
Urszula Ledzewicz - Dynamics and optimal control of tumor-immune interactions under metronomic chemotherapy

Abstract not submitted.

02:30 PM
03:00 PM
Doron Levy
03:00 PM
03:30 PM

Break

03:30 PM
04:00 PM
Shigui Ruan
04:15 PM

Shuttle Pick Up to Hotel

05:30 PM
06:00 PM

Cash Bar - Crowne Plaza

06:00 PM
07:30 PM

Banquet at Crowne Plaza

Friday, November 21, 2014
Time Session
08:00 AM

Shuttle Pick Up to MBI

08:15 AM
09:00 AM

Breakfast

09:00 AM
09:30 AM
Mark Robertson Tessi - The impact of T-cell homeostasis on solid and liquid cancers

Abstract not submitted.

09:30 AM
10:00 AM
Chuan Xue - A mathematical model for pancreatic cancer growth and treatments

Abstract not submitted.

10:00 AM
10:30 AM

Break

10:30 AM
11:00 AM
Kang-Ling Liao - The effect of CD200-CD200R on tumor proliferation

CD200 is a cell membrane protein that interacts with CD200 receptor (CD200R) of myeloid lineage cells. During tumor initiation and progression, CD200-positive tumor cells can interact with M1 and M2 macrophages through CD200-CD200R-compex to silence macrophages. However, this mechanism has been shown to have apparently two contradictory experimental results in tumor growth: inhibition and promotion. In this talk, I will introduce a system of partial differential equations that we constructed to explain why these two opposite experimental results can both take place depending on the "affinity" of M1 and M2 macrophages to form the complex CD200-CD200R with tumor.

11:00 AM
11:30 AM
Karly Jacobsen - Modeling the effects of macrophage content and CCN1 on glioma virotherapy

Abstract not submitted.

11:30 AM
12:00 PM

Discussion

12:15 PM

Shuttle Pick Up to Hotel

Name Email Affiliation
Aguda, Baltazar bdaguda@gmail.com Founder & CEO, Disease Pathways, LLC
Bai, Xue-Feng Xue-feng.Bai@osumc.edu Experimental Pathology, The Ohio State University
Bates, Dan bates@math.colostate.edu Department of Mathematics, Colorado State University
Buttenschoen, Andreas andreas.buttenschoen@ualberta.ca Department of Mathematical and Statistical Sciences, University of Alberta
Byrne, Helen byrneh@maths.ox.ac.uk Centre for Collaborative Applied Mathematics, University of Oxford
Cebulla, Colleen colleen.cebulla@osumc.edu Ophthalmology and Visual Science, The Ohio State University
Chacon, Jessica jchacon@mail.med.upenn.edu Ovarian Cancer Research Center,
Chen, Duan dchen10@uncc.edu Department of Mathematics, University of North Carolina, Charlotte
Davis, Courtney courtney.davis2@pepperdine.edu Natural Science Division, Pepperdine University
de Pillis, Lisette depillis@hmc.edu Mathematics, Harvey Mudd College
Eftimie, Raluca reftimie@maths.dundee.ac.uk Division of Mathematics, University of Dundee
Eubank, Tim tim.eubank@osumc.edu Internal Medicine/Pulmonary Medicine, The Ohio State University
Everett, Rebecca rarodger@asu.edu Applied Mathematics, Arizona State University
Friedman, Avner afriedman@math.ohio-state.edu Department of Mathematics, The Ohio State University
Gevertz, Jana gevertz@tcnj.edu Mathematics and Statistics, The College of New Jersey
Gulzar, Faisal mfaisalgul33@gmail.com Department of Pharmacology, Faculty of Pharmacy, University of Sargodha
Hillen, Thomas thillen@ualberta.ca Mathematical and Statistical Sciences, University of Alberta
Hook, Sarah sarah.hook@otago.ac.nz School of Pharmacy, University of Otago
Jacobsen, Karly jacobsen.50@mbi.osu.edu Mathematical Biosciences Institute, The Ohio State University
Jiang, Jenny Jenny.n.jiang@gmail.com Biomedical Engineering, The University of Texas at Austin
Kang, Hyunji gagul0618@gmail.com Mathematics, Konkuk University
Kianercy, Ardeshir akianer1@jhmi.edu Urology, Johns Hopkins Hospital
Kim, Yangjin ahyouhappy@konkuk.ac.kr Department of Mathematics, Konkuk University
Kim, Peter pkim@maths.usyd.edu.au School of Mathematics and Statistics, University of Sydney
Kong, Liang lkong9@uis.edu Mathematical Science, University of Illinois at Springfield
Kraj, Piotr peter_kraj@knology.net Dept. Biological Sciences, Old Dominion University
Kuang, Yang kuang@asu.edu Department of Mathematics and Statistics, Arizona State University
Lavi, Orit laviorit@gmail.com Laboratory of Cell Biology, National Cancer Institute, NIH
Ledzewicz, Urszula uledzew@siue.edu Department of Mathematics and Statistics, Southern Illinois University
Lee, Hyun Geun leeh@korea.ac.kr Department of Mathematics, Institute of Mathematical Sciences, Ewha W. University
Lee, Wanho wlee@nims.re.kr Mathematical Biology Team, National Institute for Mathematical Sciences
Lesinski, Gregory Gregory.Lesinski@osumc.edu Molecular Virology Immunology and Medical Genetics, The Ohio State University
Levy, Doron dlevy@math.umd.edu Department of Mathematics, University of Maryland
Liao, Kang-Ling liao.92@mbi.osu.edu Mathematical bioscience institute, The Ohio State University
Lim, Sookkyung sookkyung.lim@uc.edu Department of Mathematical Sciences, University of Cincinnati
Lotze, Michael lotzemt@upmc.edu Professor of Surgery and Bioengineering, University of Pittsburgh
Louzoun, Yoram louzouy@math.biu.ac.il Mathematics, bar ilan university
Mahdipour Shirayeh, Ali ali.mahdipour@gmail.com Applied Mathematics, University of Waterloo, University of Waterloo
Miskov-Zivanov, Natasa nmiskov@andrew.cmu.edu Department of Computer Science, Carnegie Mellon University
Moore, Helen dr.helen.moore@gmail.com Clinical Pharmacology and Pharmacometrics, Bristol-Myers Squibb
Pilon-Thomas, Shari Shari.Pilon-Thomas@moffitt.org Immunology Program, H. Lee Moffitt Cancer Center & Research Institute
Powell, Daniel poda@mail.med.upenn.edu Pathology & Laboratory Medicine, University of Pennsylvania
Radunskaya, Ami aradunskaya@pomona.edu Mathematics, Pomona College
Rambani, Komal komal.rambani@osumc.edu IBGP, OSU
Rezaei Yousefi, Mohammadmahdi rezaeiyousefi.1@osu.edu Electrical and Computer Engineering, The Ohio State University
Robertson Tessi, Mark Mark.Robertsontessi@moffitt.org Integrated Mathematical Oncology, Moffitt Cancer Center
Ruan, Shigui ruan@math.miami.edu Department of Mathematics, University of Miami
Rutter, Erica Erica.Rutter@asu.edu School of Mathematical & Statistical Science, Arizona State University
Sarapata, Elizabeth esarapata@g.hmc.edu Mathematics, Claremont Graduate University
Stepien, Tracy tstepien@asu.edu School of Mathematical and Statistical Sciences, Arizona State University
Urbanska, Katarzyna katu@mail.med.upenn.edu
Wares, Joanna jwares@richmond.edu Math and Computer Science, University of Richmond
Whiteside, Theresa whitesidetl@upmc.edu Pathology, Immunology and Otolaryngology, University of Pittsburgh School of Medicine
Xue, Chuan cxue@math.osu.edu Department of Mathematics, The Ohio State University
Zou, Weiping wzou@umich.edu Immunology and Biology, University of Michigan
Creating Cell Interaction Network Models Based on the Cancer Immunoediting Hypothesis

The cancer immunoediting hypothesis provides a conceptual framework that explains the immune system's dual role of inhibiting and promoting tumor growth. I will discuss the progress in the experimental validation of this hypothesis and propose cell interaction network models consistent with it.

Involvement of tumor macrophage HIFs in chemotherapy effectiveness: Mathematical modeling of oxygen, pH, and glutathione.

The four variables, hypoxia, acidity, high glutathione (GSH) concentration and fast reducing rate (redox) are distinct and varied characteristics of solid tumors compared to normal tissue. These parameters are among the most significant factors underlying the metabolism and physiology of solid tumors, regardless of their type or origin. Low oxygen tension contributes to both inhibition of cancer cell proliferation and therapeutic resistance of tumors; low extracellular pH, the reverse of normal cells, mainly enhances tumor invasion; and dysregulated GSH and redox potential within cancer cells favor their proliferation. In fact, cancer cells under these microenvironmental conditions appreciably alter tumor response to cytotoxic anti- cancer treatments. Recent experiments measured the in vivo longitudinal data of these four parameters with tumor development and the corresponding presence and absence of tumor macrophage HIF-1α or HIF-2α in a mouse model of breast cancer. In the current paper, we present a mathematical model-based system of (ordinary and partial) differential equations to monitor tumor growth and susceptibility to standard chemotherapy with oxygen level, pH, and intracellular GSH concentration. We first show that our model simulations agree with the corresponding experiments, and then we use our model to suggest treatments of tumors by altering these four parameters in tumor microenvironment. For example, the model qualitatively predicts that GSH depletion can raise the level of reactive oxygen species (ROS) above a toxic threshold and result in inhibition of tumor growth.

Modeling Cancer-Immune System Dynamics

We will present a variety of mathematical models of tumor-immune interactions that have resulted from interdisciplinary collaborations with practicing oncologists and experimentalists. We will discuss certain approaches to modeling cancer growth and immune system interactions, and treatment approaches that harness the power of the immune system to slow and sometimes stop cancer progression.

Oncolytic viral therapies and the balance between the memory and effector immune responses

Abstract not submitted.

Regulation of angiogenesis by Tie2-expressing monocytes/macrophages in breast cancer

Abstract not submitted.

Cancer and the immune system

This is tutorial talk, in which I will introduce the main components of the immune system in the context of cancer. I will introduce the different phenotypes of macrophages, the four classes of CD4+ T cells, and the cytotoxic T cells (CTL) or CD8+ T cell. As will be explained, the tumor may be recognized by macrophages and dendritic cells, and these cells will then activate 'effective' T cells to kill cancer cells. However, the tumor can fight back against the immune system, and in fact it can even use the system to its own advantage, by "educating" macrophages so that they will actually enhance tumor growth by increasing VEGF production. Another factor that works in favor of a tumor are the T regulatory cells, enhanced by the tumor, which inhibit the activities of the effective T cells.

Individual cell model for the interactions between cancer cells and macrophages at the tumor invasion front.

The interactions between cancer cells and immune cells is one of the hallmarks of cancer. In this talk we present an individual based model, including the interactions between macrophages and cancer cells at the tumor invasion front. The model is based on the Glazier-Graner-Hogeweg

modeling approach (Cellular Potts Model). In this model the macrophages and cancer cells interact via adhesion modeled using the Differential Adhesion Hypothesis and a paracrine loop (Epidermal Growth Factor (EGF) and Colony Stimulating Factor (CSF)). We further present an extension to this model in which the macrophages increase the motility of close by cancer cells. This is a first try at incorporating the Epithelial Mesenchymal Transition (EMT) in this model.

Timing is everything: getting the right therapies to the right place at the right time

Abstract not submitted.

Modeling the effects of macrophage content and CCN1 on glioma virotherapy

Abstract not submitted.

How can failure in immune response (Th17, Neutrophil, Tregs) lead to the tumor invasion in lung cancer development?: A mathematical model

Abstract not submitted.

A mathematical model for the immunotherapy of advanced prostate cancer

A mathematical model of advanced prostate cancer treatment is developed to examine the combined effects of androgen deprivation therapy and immunotherapy. Androgen deprivation therapy has been the primary form of treatment for advanced prostate cancer for the past 50 years. While initially successful, this therapy eventually results in a relapse after two to three years in the form of androgen-independent prostate cancer. Intermittent androgen deprivation therapy attempts to prevent relapse by cycling the patient on and off treatment. Over the past decade, dendritic cell vaccines have been used in clinical studies for the immunotherapy of prostate cancer with some success. Our model examines the efficacy of dendritic cell vaccines when used with continuous or intermittent androgen deprivation therapy schedules. Numerical simulations of the model suggest that immunotherapy can successfully stabilize the disease using both continuous and intermittent androgen deprivation.

Dynamics and optimal control of tumor-immune interactions under metronomic chemotherapy

Abstract not submitted.

The effect of CD200-CD200R on tumor proliferation

CD200 is a cell membrane protein that interacts with CD200 receptor (CD200R) of myeloid lineage cells. During tumor initiation and progression, CD200-positive tumor cells can interact with M1 and M2 macrophages through CD200-CD200R-compex to silence macrophages. However, this mechanism has been shown to have apparently two contradictory experimental results in tumor growth: inhibition and promotion. In this talk, I will introduce a system of partial differential equations that we constructed to explain why these two opposite experimental results can both take place depending on the "affinity" of M1 and M2 macrophages to form the complex CD200-CD200R with tumor.

Bistability induced by tumor-immune system interactions.

Many tumors target the specific immune system cells raised to protect the host against tumor. This targeting can take two main shapes. Either the tumor cells attract immune cells to the tumor, enhancing the tumor growth, or they prevent the immune cells from killing existing tumor cells. In both cases a positive feedback loop emerges between the tumor and immune system cell concentrations.

Such a feedback loop may explain the equilibrium obtained between the host immune system and tumors, where tumors stop growing, or grow very slowly, but are not destroyed by the immune response. While tumor size can increase by a factor of 10 within a day, in most cases, this huge division rate is not obtained and in reality the tumor is almost in equilibrium. This equilibrium can be explained by a bi-stable solution of the tumor-immune system dynamics.

We here study a generic set of feedback loops between the immune system cells and their targets in tumors and show that a bi-stable solution can emerge. This solution can occur only if the tumor induces death or inactivation of macrophages. In such a case, a simple negative effect of the pathogens on the macrophages will suffice to induce bistability. The initial conditions then becomes crucial for the solution, given that, according to it, the solution tends to one or the other fixed point, which correspond to a healthy or a sick state.

We show that double inhibition positive feedback loops (immune system kills tumor, which in turn kill immune system cells) behave differently than double activation feedback loops (Tumor produce cytokines that attract immune cells, which in turn produce cytokines which induce tumor growth).Double inhibition feedback loops induce bi-stability in most parameter space, while double activation feedback loops induce such a bistability in very limited regions of parameter space.

Adoptive cell therapy using tumor infiltrating lymphocytes (TIL)

Abstract not submitted.

The impact of T-cell homeostasis on solid and liquid cancers

Abstract not submitted.

Regulatory T cell networks in human cancer and immune therapies

Abstract not submitted.

A mathematical model for pancreatic cancer growth and treatments

Abstract not submitted.

T cell subsets in the tumor microenvironment

Abstract not submitted.

Posters

A Differential Equation Model for a Viral Infection with an Immune Response

Viral infection stimulates the cellular immune response resulting in production of CD4 T cells, cytotoxic T lymphocytes (CTL), and antibodies and other immune cells. CTL attack and kill cells that are infected by viruses. Antibodies are capable of identifying, neutralizing and opsonizing viruses, whereas CD4 T cells stimulate the proliferation of CTL, NK cells.

We include the cellular immune response in a new model for viral infection of a host which has applications to Human Immunodeficiency Virus and hepatitis C infection. The model consists of four differential equations for healthy target cells attacked by a virus, infected target cells, cellular immune response and free virus. We show the model has three equilibria corresponding to the disease-free stage, the acute stage of infection and the chronic stage of infection. Stability results are summarized and numerical simulations illustrate the model dynamics.

Biological implications for control of the infection via the immune response are discussed.