Modeling the competition between lung metastases and the immune system using agents

Background: The Triplex cell vaccine is a cancer cellular vaccine that can prevent almost completely the mammary tumor onset in HER-2/neu transgenic mice. In a translational perspective, the activity of the Triplex vaccine was also investigated against lung metastases showing that the vaccine is an effective treatment also for the cure of metastases. A future human application of the Triplex vaccine should take into account several aspects of biological behavior of the involved entities to improve the efficacy of therapeutic treatment and to try to predict, for example, the outcomes of longer experiments in order to move faster towards clinical phase I trials. To help to address this problem, MetastaSim, a hybrid Agent Based - ODE model for the simulation of the vaccineelicited immune system response against lung metastases in mice is presented. The model is used as in silico wetlab. As a first application MetastaSim is used to find protocols capable of maximizing the total number of prevented metastases, minimizing the number of vaccine administrations. Results: The model shows that it is possible to obtain “in silico” a 45% reduction in the number of vaccinations. The analysis of the results further suggests that any optimal protocol for preventing lung metastases formation should be composed by an initial massive vaccine dosage followed by few vaccine recalls. Conclusions: Such a reduction may represent an important result from the point of view of translational medicine to humans, since a downsizing of the number of vaccinations is usually advisable in order to minimize undesirable effects. The suggested vaccination strategy also represents a notable outcome. Even if this strategy is commonly used for many infectious diseases such as tetanus and hepatitis-B, it can be in fact considered as a relevant result in the field of cancer-vaccines immunotherapy. These results can be then used and verified in future “in vivo” experiments, and their outcome can be used to further improve and refine the model. Background The metastatic process is an extraordinary complex process. In order to colonize as econdary site and to become metastases cancer cells must complete a sequential chain of steps which include the detachment from the primary tumor, the invasion through surrounding tissues and basement membranes, the survival in the circulation, lymphatics or peritoneal space and the settlement in a distant target organ. In spite of this intrinsic inefficiency, metastases represent one of the major concerns in the clinical management of cancer. The majority of cancer mortality is associated with this disseminated disease rather than the primary tumor [1]. In most cases cancer patients with localized primary tumors have significantly better prognoses than those with disseminated tumors. Recent evidence shows that metastases can be an early event [2] and that 60% to 70% of patients have already initiated the metastatic process by the time of diagnosis. Even patients that have no evidence of tumor dissemination at diagnosis are at