CINECA, Bologna, Italy.
BMC Med Inform Decis Mak. 2012 Nov 13;12:129. doi: 10.1186/1472-6947-12-129.
Immunological strategies that achieve the prevention of tumor growth are based on the presumption that the immune system, if triggered before tumor onset, could be able to defend from specific cancers. In supporting this assertion, in the last decade active immunization approaches 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 in silico specific vaccination schedules in the quest for optimality. Specifically here we present a parallel genetic algorithm able to suggest optimal vaccination schedule.
RESULTS & CONCLUSIONS: The enormous complexity of combinatorial space to be explored makes this approach the only possible one. The suggested schedule was then tested in vivo, giving good results. Finally, biologically relevant outcomes of optimization are presented.
旨在预防肿瘤生长的免疫策略基于这样一种假设，即免疫系统如果在肿瘤发生之前被触发，就能够抵御特定的癌症。支持这一说法的是，在过去十年中，主动免疫方法预防了人类的一些与病毒相关的癌症。最近开发了一种用于非病毒相关人类乳腺癌的免疫预防细胞疫苗。这种名为 Triplex 的疫苗针对 HER-2/neu 转基因小鼠中的 HER-2-neu 致癌基因，当按照强化和终身方案给药时，已显示出几乎完全预防 HER-2/neu 驱动的乳腺肿瘤发生。
为了更好地了解 Triplex 疫苗在减少方案中的预防效果，我们采用了计算方法。所开发的计算机模型使我们能够在寻求最佳方案时在计算机上测试特定的疫苗接种方案。具体来说，这里我们提出了一种并行遗传算法，能够提出最佳的疫苗接种方案。
组合空间的巨大复杂性使得这种方法成为唯一可行的方法。然后在体内测试了建议的方案，结果良好。最后，提出了优化的生物学相关结果。
