In the past decades, an enormous amount of precious information has been collected about molecular and genetic characteristics of cancer. This knowledge is mainly based on a reductionistic approach, meanwhile cancer is widely recognized to be a ‘system biology disease’. The behavior of complex physiological processes cannot be understood simply by knowing how the parts work in isolation. There is not solely a matter how to integrate all available knowledge in such a way that we can still deal with complexity, but we must be aware that a deeply transformation of the currently accepted oncologic paradigm is urgently needed. We have to think in terms of biological networks: understanding of complex functions may in fact be impossible without taking into consideration influences (rules and constraints) outside of the genome. Systems Biology involves connecting experimental unsupervised multivariate data to mathematical and computational approach than can simulate biologic systems for hypothesis testing or that can account for what it is not known from high-throughput data sets. Metabolomics could establish the requested link between genotype and phenotype, providing informations that ensure an integrated understanding of pathogenic mechanisms and metabolic phenotypes and provide a screening tool for new targeted drug.

中文翻译：

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超越致癌基因范式：了解癌症发生的复杂性

在过去的几十年里，已经收集了大量关于癌症分子和遗传特征的宝贵信息。这些知识主要基于还原论的方法，同时癌症被广泛认为是一种“系统生物学疾病”。复杂生理过程的行为不能简单地通过了解各个部分如何孤立地工作来理解。不仅是如何整合所有可用知识，以便我们仍然可以应对复杂性，而且我们必须意识到迫切需要对当前公认的肿瘤学范式进行深度转变。我们必须从生物网络的角度思考：如果不考虑基因组之外的影响（规则和约束），理解复杂功能实际上是不可能的。系统生物学涉及将实验性无监督多变量数据与数学和计算方法联系起来，从而可以模拟用于假设检验的生物系统，或者可以解释从高通量数据集中未知的内容。代谢组学可以在基因型和表型之间建立所需的联系，提供信息以确保对致病机制和代谢表型的综合理解，并为新的靶向药物提供筛选工具。