文章：

癌症综合基因组分析的原理和方法

Principles and methods of integrative genomic analyses in cancer

原文发布日期：2014-04-24

DOI: 10.1038/nrc3721

类型: Review Article

开放获取: 否

要点：

1. Genomic, metabolomic and clinical data on a range of solid cancers and model systems are emerging and can be used to identify novel patient subgroups for tailored therapy and monitoring.
2. Molecular markers identified at the DNA, mRNA, microRNA and protein levels have been used to develop profiles associated with taxonomy, tumour aggressiveness, response to therapy and patient outcome.
3. The information content is higher in integrated analysis than in any of the molecular levels studied separately, and a large number of statistical methods for the integration of 'omics' data have emerged.
4. The access to large data sets that have been made available by the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) has made it possible to compare the performance of some of the statistical methods of omic data integration on the same data set.
5. These recent developments will fundamentally alter the way that we statistically model and evaluate treatment strategies, from identifying patient groups that respond to treatment above random, to identifying pathways and biological entities that are druggable and altered above random.
6. A shift from large randomized clinical trials towards treatment modalities that are tailored for stratified patient groups, down to N-of-1 trials, in which a single patient constitutes the entire trial, will require new statistical methods.
7. Outsourcing data and searching for solutions in open competition will allow new ideas to instantly emerge to 'embrace the complexity' that is associated with the exponentially increasing amounts of data and find new ways of shared analysis.

要点翻译：

1. 关于多种实体癌症及模型系统的基因组学、代谢组学和临床数据正不断涌现，这些数据可用于识别新的患者亚群，以实现个体化治疗与监测。
2. 在DNA、mRNA、microRNA和蛋白质水平上发现的分子标记已被用于建立与肿瘤分类、侵袭性、治疗反应及患者预后相关的特征谱。
3. 整合分析所蕴含的信息量远超任何单一分子层面的研究，目前已有大量用于整合“组学”数据的统计方法应运而生。
4. 国际癌症基因组联盟（ICGC）和癌症基因组图谱（TCGA）提供的大规模数据集使得在相同数据基础上比较不同组学数据整合统计方法的性能成为可能。
5. 这些最新进展将从根本上改变我们通过统计模型评估治疗策略的方式：从识别疗效超越随机效应的患者群体，到发现具有成药性且变异超出随机水平的通路及生物实体。
6. 从大规模随机临床试验转向为分层患者群体量身定制的治疗模式，直至将单个患者作为完整试验单位的N-of-1试验，这一转变将需要开发新的统计方法。
7. 通过数据外包和开放竞赛寻求解决方案，将促使新思想即时涌现，从而“拥抱”伴随数据指数级增长而来的复杂性，并找到共享分析的新途径。

英文摘要：

Combined analyses of molecular data, such as DNA copy-number alteration, mRNA and protein expression, point to biological functions and molecular pathways being deregulated in multiple cancers. Genomic, metabolomic and clinical data from various solid cancers and model systems are emerging and can be used to identify novel patient subgroups for tailored therapy and monitoring. The integrative genomics methodologies that are used to interpret these data require expertise in different disciplines, such as biology, medicine, mathematics, statistics and bioinformatics, and they can seem daunting. The objectives, methods and computational tools of integrative genomics that are available to date are reviewed here, as is their implementation in cancer research.

摘要翻译： 

分子数据（如DNA拷贝数变异、mRNA和蛋白表达）的综合分析揭示，多种癌症中存在生物学功能和分子通路的失调。来自各种实体瘤和模型系统的基因组、代谢组及临床数据不断涌现，可用于识别新的患者亚群，以实现个体化治疗和监测。用于解读这些数据的整合基因组学方法需要生物学、医学、数学、统计学和生物信息学等多学科专长，看似令人生畏。本文综述了迄今可用的整合基因组学的目标、方法及计算工具，以及它们在癌症研究中的实施。

原文链接：

Principles and methods of integrative genomic analyses in cancer