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New Challenges for Cancer Systems Biomedicine pp 227–248Cite as

Computational Models as Novel Tools for Cancer Vaccines

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Abstract

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.

Keywords

  • Major Histocompatibility Complex
  • Mammary Carcinoma
  • Cancer Vaccine
  • Vaccination Schedule
  • Vaccine Administration

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Nessuna umana investigazione si può dimandare vera scienza, se essa non passa per le matematiche dimostrazioni.

Leonardo da Vinci,

Trattato della pittura, I,1

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Author information

Authors and Affiliations

  1. Institute for Computing Application ℌM. Piconeℍ, National Research Council of Italy, Viale Manzoni 30, 00185, Rome, Italy

    Filippo Castiglione

  2. Department of Hematology and Oncologic Sciences “L. e A. Seragnoli”, University of Bologna, Via Massarenti 9, 40138, Bologna, Italy

    Pier Luigi Lollini & Arianna Paladini

  3. Department of Mathematics and Computer Science, University of Catania, V.le A. Doria 6, 95125, Catania, Italy

    Santo Motta

  4. Department of Pharmaceutical Sciences, University of Catania, V.le A. Doria 6, 95125, Catania, Italy

    Francesco Pappalardo & Marzio Pennisi

Authors
  1. Filippo Castiglione
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  2. Pier Luigi Lollini
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  3. Santo Motta
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  4. Arianna Paladini
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  5. Francesco Pappalardo
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  6. Marzio Pennisi
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Editor information

Editors and Affiliations

  1. Department of Experimental Oncology, European Institute of Oncology, Milan, Italy

    Alberto d’Onofrio

  2. Department of Mathematics, University of Pisa, Italy

    Paola Cerrai

  3. Istituto di Analisi dei Sistemi ed Informatica “A. Ruberti“ — CNR, Rome, Italy

    Alberto Gandolfi

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Castiglione, F., Lollini, P.L., Motta, S., Paladini, A., Pappalardo, F., Pennisi, M. (2012). Computational Models as Novel Tools for Cancer Vaccines. In: d’Onofrio, A., Cerrai, P., Gandolfi, A. (eds) New Challenges for Cancer Systems Biomedicine. SIMAI Springer Series. Springer, Milano. https://doi.org/10.1007/978-88-470-2571-4_12

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