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The goal of tumor immunology is to understand the interactions between tumor cells and immune system, and ultimately to devise immune basedapproaches to fight cancer. We discuss here recent advances in tumor immunology and in the interaction between immunology and informatics that provide new perspectives for the development of cancer therapy. Cancer immunoprevention is a novel approach to cancer prevention through the use of vaccines and other immunological strategies to be applied before tumor onset. The efficacy of cancer immunoprevention has been demonstrated in several experimental systems, however, as frequently happens in novel approaches, cancer immunoprevention studies incorporate a large number of variables. We show here how a specifically designed lattice gas model can provide significant insight for the analysis of immune variables and the design of new biological experiments. A second approach that provides new perspectives in tumor immunology as the result of interactions between immunology and informatics is the use of DNA microarrays to investigate and monitor tumor-host relationships and modifications induced by immunopreventive and immunotherapeutic interventions.

The main goal of pharmacogenomics is to study the effects of genetic variation on patient responses to therapies. Its applications range from the evaluation of safety and efficacy of treatment to the optimization of therapies and therapeutic regimens. Pharmacogenomics is becoming increasingly important in immunology, for the development of new generation vaccines, immunotherapies and transplantation. The human immune system is a complex and adaptive learning system which operates at multiple levels: molecules, cells, organs, organisms, and groups of organisms. Immunologic research, both basic and applied, needs to deal with this complexity. We increasingly use mathematical modeling and computational simulation in the study of the immune system and immune responses. Thus, quantitative models that appropriately capture the complexity in architecture and function of the immune system are an integral component of the personalized medicine efforts. In silico models of the immune system can provide answers to a variety of questions, including understanding the general behavior of the immune system, the course of disease, effects of treatment, analysis of cellular and molecular interactions, and simulation of laboratory experiments. We herein present the ImmunoGrid project that integrates molecular and system level models of the immune system and processes for in silico studies of the immune function. The ImmunoGrid simulator uses Grid technologies, enabling computational simulation of the immune system at the natural scale, perform a large number of simulated experiments, capture the diversity of the immune system between individuals, and provide a basis for therapeutic approaches tailored to the individual genetic make-up.