Dipartimento di Scienze del Farmaco, Università degli Studi di Catania, V.le A. Doria 6, 95125 Catania, Italy.
Biomed Res Int. 2013;2013:106407. doi: 10.1155/2013/106407. Epub 2012 Dec 23.
Cancer vaccines are a real application of the extensive knowledge of immunology to the field of oncology. Tumors are dynamic complex systems in which several entities, events, and conditions interact among them resulting in growth, invasion, and metastases. The immune system includes many cells and molecules that cooperatively act to protect the host organism from foreign agents. Interactions between the immune system and the tumor mass include a huge number of biological factors. Testing of some cancer vaccine features, such as the best conditions for vaccine administration or the identification of candidate antigenic stimuli, can be very difficult or even impossible only through experiments with biological models simply because a high number of variables need to be considered at the same time. This is where computational models, and, to this extent, immunoinformatics, can prove handy as they have shown to be able to reproduce enough biological complexity to be of use in suggesting new experiments. Indeed, computational models can be used in addition to biological models. We now experience that biologists and medical doctors are progressively convinced that modeling can be of great help in understanding experimental results and planning new experiments. This will boost this research in the future.
癌症疫苗是将免疫学的广泛知识真正应用于肿瘤学领域的产物。肿瘤是一个动态的复杂系统，其中有几个实体、事件和条件相互作用，导致生长、侵袭和转移。免疫系统包括许多协同作用以保护宿主生物体免受外来物质侵害的细胞和分子。免疫系统和肿瘤之间的相互作用包括大量的生物因素。仅通过生物模型的实验来测试一些癌症疫苗的特性，如疫苗接种的最佳条件或候选抗原刺激物的鉴定，可能非常困难甚至不可能，因为需要同时考虑大量变量。在这方面，计算模型，并且在这个程度上，免疫信息学，可以证明是有用的，因为它们已经显示出能够复制足够的生物复杂性，以便在提出新的实验时发挥作用。事实上，计算模型可以与生物模型一起使用。我们现在发现，生物学家和医生越来越相信，建模可以极大地帮助理解实验结果和规划新的实验。这将推动未来的这项研究。
