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

<p>Abstract</p> <p>Background</p> <p>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 vaccine-elicited immune system response against lung metastases in mice is presented. The model is used as in silico wet-lab. 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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p

A note on the uniform asymptotic normality of location M-estimates

M-estimates, U-statistics, GS-estimates, Robust estimation, 62F35,

Computational Models as Novel Tools for Cancer Vaccines

none6Prevention 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.mixedF. Castiglione; P.-L. Lollini; S. Motta; A. Palladini; F. Pappalardo; M. Pennisi.F. Castiglione; P.-L. Lollini; S. Motta; A. Palladini; F. Pappalardo; M. Pennisi

Algorithmic Stability Theory

The notion of Stability&nbsp;[1\u20133] allows to answer a fundamental question in learning theory: which are the properties that a learning algorithm A should fulfill in order to achieve good generalization performance? Stability answers this question in a very intuitive way: if A selects similar models, even if the training data are (slightly) modified, then we can be confident that the learning algorithm is stable