Aebersold Ruedi, Auffray Charles, Baney Erin, Barillot Emmanuel, Brazma Alvis, Brett Catherine, Brunak Søren, Butte Atul, Califano Andrea, Celis Julio, Cufer Tanja, Ferrell James, Galas David, Gallahan Daniel, Gatenby Robert, Goldbeter Albert, Hace Natasa, Henney Adriano, Hood Lee, Iyengar Ravi, Jackson Vicky, Kallioniemi Ollie, Klingmüller Ursula, Kolar Patrik, Kolch Walter, Kyriakopoulou Christina, Laplace Frank, Lehrach Hans, Marcus Frederick, Matrisian Lynn, Nolan Garry, Pelkmans Lucas, Potti Anil, Sander Chris, Seljak Marija, Singer Dinah, Sorger Peter, Stunnenberg Hendrik, Superti-Furga Giulio, Uhlen Mathias, Vidal Marc, Weinstein John, Wigle Dennis, Williams Michael, Wolkenhauer Olaf, Zhivotovsky Boris, Zinovyev Andrei, Zupan Blaz
Mol Oncol. 2009 Feb;3(1):9-17. doi: 10.1016/j.molonc.2008.11.003. Epub 2008 Dec 9.
The main conclusion is that systems biology approaches can indeed advance cancer research, having already proved successful in a very wide variety of cancer-related areas, and are likely to prove superior to many current research strategies. Major points include: Systems biology and computational approaches can make important contributions to research and development in key clinical aspects of cancer and of cancer treatment, and should be developed for understanding and application to diagnosis, biomarkers, cancer progression, drug development and treatment strategies. Development of new measurement technologies is central to successful systems approaches, and should be strongly encouraged. The systems view of disease combined with these new technologies and novel computational tools will over the next 5-20 years lead to medicine that is predictive, personalized, preventive and participatory (P4 medicine).Major initiatives are in progress to gather extremely wide ranges of data for both somatic and germ-line genetic variations, as well as gene, transcript, protein and metabolite expression profiles that are cancer-relevant. Electronic databases and repositories play a central role to store and analyze these data. These resources need to be developed and sustained. Understanding cellular pathways is crucial in cancer research, and these pathways need to be considered in the context of the progression of cancer at various stages. At all stages of cancer progression, major areas require modelling via systems and developmental biology methods including immune system reactions, angiogenesis and tumour progression.A number of mathematical models of an analytical or computational nature have been developed that can give detailed insights into the dynamics of cancer-relevant systems. These models should be further integrated across multiple levels of biological organization in conjunction with analysis of laboratory and clinical data.Biomarkers represent major tools in determining the presence of cancer, its progression and the responses to treatments. There is a need for sets of high-quality annotated clinical samples, enabling comparisons across different diseases and the quantitative simulation of major pathways leading to biomarker development and analysis of drug effects.Education is recognized as a key component in the success of any systems biology programme, especially for applications to cancer research. It is recognized that a balance needs to be found between the need to be interdisciplinary and the necessity of having extensive specialist knowledge in particular areas.A proposal from this workshop is to explore one or more types of cancer over the full scale of their progression, for example glioblastoma or colon cancer. Such an exemplar project would require all the experimental and computational tools available for the generation and analysis of quantitative data over the entire hierarchy of biological information. These tools and approaches could be mobilized to understand, detect and treat cancerous processes and establish methods applicable across a wide range of cancers.
主要结论是,系统生物学方法确实能够推动癌症研究,并且已在众多与癌症相关的领域取得成功,而且可能会被证明优于许多当前的研究策略。要点包括:系统生物学和计算方法能够为癌症及癌症治疗的关键临床方面的研发做出重要贡献,应当加以发展以用于理解和应用于诊断、生物标志物、癌症进展、药物研发及治疗策略。新测量技术的开发是成功的系统方法的核心,应大力予以鼓励。疾病的系统观与这些新技术及新型计算工具相结合,将在未来5至20年催生出具有预测性、个性化、预防性和参与性的医学(P4医学)。目前正在开展重大项目,以收集关于体细胞和种系基因变异以及与癌症相关的基因、转录本、蛋白质和代谢物表达谱的极其广泛的数据。电子数据库和储存库在存储和分析这些数据方面发挥着核心作用。这些资源需要得到开发和维持。了解细胞通路在癌症研究中至关重要,并且需要在癌症不同阶段的进展背景下考虑这些通路。在癌症进展的各个阶段,主要领域都需要通过系统生物学和发育生物学方法进行建模,包括免疫系统反应、血管生成和肿瘤进展。已经开发出一些分析或计算性质的数学模型,这些模型能够深入洞察与癌症相关系统的动态变化。这些模型应与实验室和临床数据分析相结合,在生物组织的多个层面上进一步整合。生物标志物是确定癌症的存在、其进展以及对治疗反应的主要工具。需要有高质量注释的临床样本集,以便能够在不同疾病之间进行比较,并对导致生物标志物开发和药物效果分析的主要通路进行定量模拟。教育被认为是任何系统生物学计划取得成功的关键组成部分,尤其是在应用于癌症研究方面。人们认识到,需要在跨学科的必要性与在特定领域拥有广泛专业知识的必要性之间找到平衡。本次研讨会提出的一项建议是,在癌症进展的全过程中探索一种或多种癌症类型,例如胶质母细胞瘤或结肠癌。这样一个范例项目将需要所有可用于在生物信息的整个层次结构上生成和分析定量数据的实验和计算工具。这些工具和方法可用于理解、检测和治疗癌症过程,并建立适用于广泛癌症类型的方法。