Computational models as novel tools for cancer vaccines.

Prevention of tumor growth by immunological approaches is based on the assumption that the immune system, if adequately stimulated before tumor onset, could be able to protect from specific cancers. In the last decade active immunization strategies effectively prevented some virus-related cancers in humans. An immunopreventive cell vaccine for the non-virus-related human breast cancer has been recently developed. This vaccine, called Triplex, targets the HER-2-neu oncogene in HER-2/neu transgenic mice and has shown to almost completely prevent HER-2/neu-driven mammary carcinogenesis when administered with an intensive and life-long schedule. To better understand the preventive efficacy of the Triplex vaccine in reduced schedules we employed a computational approach. The computer model developed allowed us to test specific vaccination schedules in the quest for optimality. Furthermore, another computational model was developed to simulate the scenario arising from the immunotherapy experiments with the Triplex vaccine as a therapeutic approach against lung metastases derived by mammary carcinoma. This chapter describes the trail we followed starting from the problem of evaluating immunopreventive schedules with a generic computer model for the immune system response to a model of metastasis passing through an in-silico detailed model of the cancer-immune system interaction in HER-2/neu transgenic mice. Altogether it provides an example of the successful use of a combination of animal and computational modeling to speed up the way from lab to the bedside and even the patient.