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Tumor associated antigen variation - in silico experiments using the Sicilian Grid
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The power of modern computers allows the modeling and simulation of complex biological systems. The last decade has seen the emergence of a growing number of simulations of the immune system. In this article, Franco Celada and Philip Seiden present a model that, they suggest, is rich enough to allow computer experiments to be used as practical adjuncts to the usual biological experiments, at a saving of cost and time.
Transgenic Balb/c mice expressing the transforming rat HER-2/neu oncogene develop early and multifocal mammary carcinomas. Within the first 5 months of life the tissue-specific expression of HER-2/neu causes a progression in all their 10 mammary glands from atypical hyperplasia to invasive carcinoma. It was previously observed that chronic administration of interleukin (IL)-12 increased tumor latency, but every mouse eventually succumbed to multiple carcinomas. A significant improvement in tumor prevention was sought by administering allogeneic mammary carcinoma cells expressing HER-2/neu combined with systemic IL-12. This treatment reduced tumor incidence by 90% and more than doubled mouse lifetime. For the maximum prevention p185(neu) antigen must be expressed by allogeneic cells. IL-12 treatment strongly increased the cell vaccine efficacy. The mammary glands of mice receiving the combined treatment displayed a markedly reduced epithelial cell proliferation, angiogenesis, and HER-2/neu expression, while the few hyperplastic foci were heavily infiltrated by granulocytes, macrophages, and CD8(+) lymphocytes. Specific anti-HER-2/neu antibodies were produced and a nonpolarized activation of CD4(+) and CD8(+) cells secreting IL-4 and interferon (IFN)-gamma were evident. A central role for IFN-gamma in the preventive effect was proven by the lack of efficacy of vaccination in IFN-gamma gene knockout HER-2/neu transgenic Balb/c mice. A possible requirement for IFN-gamma is related to its effect on antibody production, in particular on IgG2a and IgG2b subclasses, that were not induced in IFN-gamma knockout HER-2/neu mice. In conclusion, our data show that an allogeneic HER-2/neu-expressing cell vaccine combined with IL-12 systemic treatment can prevent the onset of genetically determined tumors.
This study evaluated the ability of cytokine-engineered allogeneic (H-2(q)) HER-2/neu-positive cells to prevent tumor development in mammary cancer-prone virgin female BALB/c (H-2(d)) mice transgenic for the transforming rat HER-2/neu oncogene (BALB-neuT mice). Repeated vaccinations with cells engineered to release interleukin (IL)-2, IL-12, IL-15, or IFN-gamma showed that IL-12-engineered cell vaccines had the most powerful immunopreventive activity, with >80% of 1-year-old BALB-neuT mice free of tumors. On the contrary all of the untreated mice and all of the mice vaccinated with IL-12-engineered cells lacking either HER-2/neu or allogeneic antigens developed mammary carcinomas within 22 or 33 weeks, respectively. Whole mount, histology, immunohistochemistry, and gene expression profile analysis showed that vaccination with IL-12-engineered cells maintained 26-week mammary glands free of neoplastic growth, with a gene expression profile that clustered with that of untreated preneoplastic glands. The IL-12-engineered cell vaccine elicited a high production of IFN-gamma and IL-4 and a strong anti-HER-2/neu antibody response. Immune protection was lost or markedly impaired in BALB-neuT mice lacking IFN-gamma or antibody production, respectively. The protection afforded by the IL-12-engineered cell vaccine was equal to that provided by the systemic administration of recombinant IL-12 in combination with HER-2/neu H-2(q) cell vaccine. However, IL-12-engineered cell vaccine induced much lower circulating IL-12 and IFN-gamma, and therefore lower potential side effects and systemic toxicity.
We present an in silico model that simulates the immune system responses to tumor cells in naive and vaccinated mice. We have demonstrated the ability of this model to accurately reproduce the experimental results.
In vivo experiments on HER-2/neu mice have shown the effectiveness of Triplex vaccine in the protection of mice from mammary carcinoma. Full protection was conferred using chronic (prophylactic) vaccination protocol while therapeutic vaccination was less efficient. Our in silico model was able to closely reproduce the effects of various vaccination protocols. This model is the first step towards the development of in silico experiments searching for optimal vaccination protocols.
In silico experiments carried out on two large statistical samples of virtual mice showed very good agreements with in vivo experiments for all experimental vaccination protocols. They also show, as supported by in vivo experiments, that the humoral response is fundamental in controlling the tumor growth and therefore suggest the selection and timing of experiments for measuring the activity of T cells.
francesco@dmi.unict.it
http://www.dmi.unict.it/CIG/suppdata_bioinf.html.
We present a systematic approach to search for an effective vaccination schedule using mathematical computerized models. Our study is based on our previous model that simulates the cancer vs immune system competition activated by tumor vaccine. This model accurately reproduces in-vivo experiments results on HER-2/neu mice treated with the immuno-prevention cancer vaccine (Triplex) for mammary carcinoma. In vivo experiments have shown the effectiveness of Triplex vaccine in protection of mice from mammary carcinoma. The full protection was conferred using chronic (prophylactic) vaccination protocol while therapeutic vaccination was less effcient.
In the present paper we use the computer simulations to systematically search for a vaccination schedule which prevents solid tumor formation. The strategy we used for defining a successful vaccination schedule is to control the number of cancer cells with vaccination cycles. We found that, applying the vaccination scheme used in in-vivo experiments, the number of vaccine injections can be reduced roughly by 30%.
Immunological prevention of cancer has been obtained in HER-2/neu transgenic mice using a vaccine that combines 3 different immune stimuli (Triplex vaccine) that is repeatedly administered for the entire lifespan of the host (Chronic protocol). Biological experiments leave open the question of whether the Chronic protocol is indeed the minimal vaccination schedule affording 100% protection, or whether shorter protocols could be applied that would result in the same efficacy. A biological solution would require an enormous number of experiments, each lasting at least one year. Therefore we approached this problem by developing a simulator (SimTriplex) which describes the immune response activated by Triplex vaccine. This simulator, tested against in vivo experiments on HER-2/neu mice, reproduces all the vaccination protocols used in the in vivo experiments. The simulator should describe any vaccination protocol within the tested range. A possible solution to the former open question using a minimal search strategy based on a genetic algorithm is presented. This is the first step toward a more general approach of biological or clinical constraints for the search of an effective vaccination schedule.
The results suggest that the Chronic protocol included a good number of redundant vaccine administrations, and that maximal protection could still be obtained with a number of vaccinations approximately 40% less than with the Chronic protocol.
This approach may have important connotations with regard to translation of cancer immunopreventive approaches to human situations, in which it is desirable to minimize the number of vaccinations. We are currently setting up experiments in mice to test whether the actual effectiveness of the vaccination protocol agrees with the genetic algorithm.
Elsewhere [1] we put forward a network model of the immune system that explicitly incorporates cross-reactivity between clones. Receptor shape and hence reactivity was described by a single real number, x, whose value might be viewed as representing the depth of the antibody combining site or the height of a complementary idiotypic “bump”. Each clone was thus described by a unique value of a shape parameter x, and a rule was specified for determining the degree of reactivity of clone x with other clones y. A population dynamic model of interacting clones in a one-dimensional “shape space” was then developed that led to certain strategic insights concerning the operation of the immune system.
The immune system is capable of learning, memory, and pattern recognition. By employing genetic operators on a time scale fast enough to observe experimentally, the immune system is able to recognize novel shapes without preprogramming. Here we describe a dynamical model for the immune system that is based on the network hypothesis of Jerne, and is simple enough to simulate on a computer. This model has a strong similarity to an approach to learning and artificial intelligence introduced by Holland, called the classifier system. We demonstrate that simple versions of the classifier system can be cast as a nonlinear dynamical system, and explore the analogy between the immune and classifier systems in detail. Through this comparison we hope to gain insight into the way they perform specific tasks, and to suggest new approaches that might be of value in learning systems.
A conceptual foundation for modeling tumor progression, growth, and heterogeneity is presented. The purpose of such models is to aid understanding, test ideas, formulate experiments, and to model cancer 'in machina' to address the dynamic features of tumor cell heterogeneity, progression, and growth. The descriptive capabilities of such an approach provides a consistent language for qualitatively reasoning about tumor behavior. This approach provides a schema for building conceptual models that combine three key phenomenological driving elements: growth, progression, and genetic instability. The growth element encompasses processes contributing to changes in tumor bulk and is distinct from progression per se. The progression element subsumes a broad collection of processes underlying phenotypic progression. The genetics elements represents heritable changes which potentially affect tumor character and behavior. Models, conceptual and mathematical, can be built for different tumor situations by drawing upon the interaction of these three distinct driving elements. These models can be used as tools to explore a diversity of hypotheses concerning dynamic changes in cellular populations during tumor progression, including the generation of intratumor heterogeneity. Such models can also serve to guide experimentation and to gain insight into dynamic aspects of complex tumor behavior.
We present a further, yet important, step in applying Catania Mouse Model & simulator (SimTriplex) of immune system response to vaccination. In particular we show that, as immune response can induce toxicity, one can calibrate the vaccine administrations in such a way to avoid toxicity effects, keeping immunoprevention from cancer. This result increases the model's potential applications.
Matzinger P: An innate sense of danger
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