Abstract
The immune-mediated eradication of cancer is made difficult both by immunosuppressive changes that occur within the tumor micro- environment as well as by systemic effects that lead to chronic inflammation and immunologic exhaustion. At present, the temporal dynamics and interconnectivity of these local and systemic impediments to antitumor immunity are poorly understood. Over the past several years, our laboratory has identified methods to concep - tualize and study the complex immunobiologic interactions driving the balance between toler- ance and immune activation over time in can- cer patients. We have recognized that dynamic immune responses in cancer may mirror natural self-organizing processes, such as species disper- sal into new ecological environments (1) and suburban sprawl (2) and, as such, are amenable to hypothesis testing and data-driven predictive modeling. In the following sections, we describe our approach to understanding alterations in immunity that occur as a result of the malig- nancy, as well as challenges posed by animal models to study this phenomenon. We show that tumor-induced immune modulation facilitates metastases and how comparative models for tol- erance may help us better understand this pro- cess. Finally, we discuss a method for discovery of immune biorhythms and predictive modeling of immune responses developed with the help of aerospace engineers. With our multidisciplinary approach, we are developing methods of dynamic immune network recognition that embrace the complexities of tumor/immune interactions with the goal of real-time, individualized, curative immunotherapy for cancer patients. Many immunotherapeutic strategies have been investigated in the treatment of advanced malignancies, including our laboratory's primary
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