Lund Eiliv, Dumeaux Vanessa
Institute of Community Medicine, University of Tromsø, 9037 Tromsø, Norway.
Cancer Epidemiol Biomarkers Prev. 2008 Nov;17(11):2954-7. doi: 10.1158/1055-9965.EPI-08-0519.
Prospective studies in cancer epidemiology have conserved their study design over the last decades. In this context, current epidemiologic studies investigating gene-environment interactions are based on biobank for the analysis of genetic variation and biomarkers, using notified cancer as outcome. These studies result from the use of high-throughput technologies rather than from the development of novel design strategies. In this article, we propose the globolomic design to run integrated analyses of cancer risk covering the major -omics in blood and tumor tissue. We defined this design as an extension of the existing prospective design by collecting tissue and blood samples at time of diagnosis, including biological material suitable for transcriptome analysis. The globolomic design opens up for several new analytic strategies and, where gene expression profiles could be used to verify mechanistic information from experimental biology, adds a new dimension to causality in epidemiology. This could improve, for example, the interpretation of risk estimates related to single nucleotide polymorphisms in gene-environment studies by changing the criterion of biological plausibility from a subjective discussion of in vitro information to observational data of human in vivo gene expression. This ambitious design should consider the complexity of the multistage carcinogenic process, the latency time, and the changing lifestyle of the cohort members. This design could open the new research discipline of systems epidemiology, defined in this article as a counterpart to systems biology. Systems epidemiology with a focus on gene functions challenges the current concept of biobanking, which focuses mainly on DNA analyses.
在过去几十年里，癌症流行病学的前瞻性研究一直保持着其研究设计。在此背景下，当前调查基因-环境相互作用的流行病学研究基于生物样本库，以已报告的癌症作为结局，分析基因变异和生物标志物。这些研究源于高通量技术的应用，而非新设计策略的发展。在本文中，我们提出了全基因组设计，以对涵盖血液和肿瘤组织中主要“组学”的癌症风险进行综合分析。我们将这种设计定义为现有前瞻性设计的扩展，即在诊断时收集组织和血液样本，包括适合转录组分析的生物材料。全基因组设计开启了多种新的分析策略，并且在基因表达谱可用于验证实验生物学的机制信息的情况下，为流行病学中的因果关系增添了新的维度。例如，通过将生物学合理性标准从对体外信息的主观讨论转变为人类体内基因表达的观察数据，这可以改进基因-环境研究中与单核苷酸多态性相关的风险估计的解释。这种雄心勃勃的设计应考虑多阶段致癌过程的复杂性、潜伏期以及队列成员不断变化的生活方式。这种设计可能会开启系统流行病学这一新的研究领域，本文将其定义为系统生物学的对应领域。专注于基因功能的系统流行病学挑战了当前主要侧重于DNA分析的生物样本库概念。
