We extend our model of the antitumor immune response initiated by laser-immunotherapy treatment to more closely examine key steps in the immune response 1) tumor antigen acquisition by antigen-presenting dendritic cells (DCs) and 2) cytotoxic T cell (CTL) priming by lymphatic DCs. Specifically we explore the formation of DC-CTL complexes that lead to CTL priming. We find that the bias in the dissociation rate of the complex influences the outcome of treatment. In particular, a bias towards priming favors a rapid activated CTL response and the clearance of tumors.
Regulatory T cells (Tregs) have tremendous influence on treatment outcomes in patients receiving immunotherapy for cancerous tumors. We present a mathematical model incorporating the primary cellular and molecular components of antitumor laser immunotherapy. We explicitly model developmental classes of dendritic cells (DCs), cytotoxic T cells (CTLs), primary and metastatic tumor cells, and tumor antigen. Regulatory T cells have been shown to kill antigen presenting cells, to influence dendritic cell maturation and migration, to kill activated killer CTLs in the tumor microenvironment, and to influence CTL proliferation. Since Tregs affect explicitly modeled cells, but we do not explicitly model dynamics of Treg themselves, we use model parameters to analyze effects of Treg immunosuppressive activity. We will outline a systematic method for assigning clinical outcomes to model simulations and use this condition to associate simulated patient treatment outcome with Treg activity.
Laser immunotherapy (LIT) is a cancer treatment with promising results in animal models and some `no other option' patients. The therapy elicits an immune response that targets the primary tumor and, perhaps more importantly, otherwise untreatable metastases. We develop and analyze a mathematical model that includes key elements of the host immune response set in motion by LIT. We use Latin Hypercube Sampling (LHS) to EFFICIENTLY sample model parameters from a high-dimensional parameter space and get a broad sense of model dynamics. Depending on a variety of tumor, therapy, and immune parameters, a variety of patient outcomes ranging from tumor clearance to patient death are possible.
We use a mathematical model to describe and predict the population dynamics of tumor cells, immune cells, and other immune components in a host undergoing laser immunotherapy treatment against metastatic cancer. We incorporate key elements of the treatment into the model: a function describing the laser-induced primary tumor cell death and parameters capturing the role and strength of the primary immunoadjuvant, glycated chitosan. We focus on identifying conditions that ensure a successful treatment. In particular, we study the patient response (i.e., anti-tumor immune dynamics and treatment outcome) in two different but related mathematical models as we vary quantitative features of the immune system (supply, proliferation, death, and interaction rates). We compare immune dynamics of a `baseline' immune model against an `augmented' model (with additional cell types and antibodies) and in both, we find that using strong immunoadjuvants, like glycated chitosan, that enhance dendritic cell activity yields more promising patient outcomes.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.