Dynamic changes of RNA-sequencing expression for precision medicine: N-of-1-pathways Mahalanobis distance within pathways of single subjects predicts breast cancer survival.
Dynamic changes of RNA-sequencing expression for precision medicine: N-of-1-pathways Mahalanobis distance within pathways of single subjects predicts breast cancer survival.
复制标题
DOI：
10.1093/bioinformatics/btv253
复制
发表时间：
2015-06-15
期刊：
影响因子：
--
通讯作者：
Lussier YA
中科院分区：
文献类型：
--
作者：
Schissler AG;Gardeux V;Li Q;Achour I;Li H;Piegorsch WW;Lussier YA
动机：个性化医疗的传统方法依赖于多个患者的分子数据分析。精准医学的道路在于分子数据分析，可以发现可解释的单一受试者信号（N-of-1）。我们开发了一个全球性的框架，N-of-1-pathways，用于单受试者基因表达数据分析的机械锚定方法。我们之前采用了一种度量标准，可以优先考虑单个受试者中去调控途径的统计学显著性，但是，它缺乏定量可解释性（例如，相当于基因表达倍数变化）。结果如下：在这项研究中，我们扩展了我们以前的方法与统计马氏距离（MD）的应用，以量化个人路径水平的放松管制。我们证明了这种方法，N-of-1-pathways配对样本MD（N-OF-1-PATHWAYS-MD），检测失调的途径（经验模拟），而不是使用生物学重复的研究膨胀假阳性率。最后，我们确定N-OF-1-PATHWAYS-MD评分具有生物学意义、临床相关性，并且可预测乳腺癌存活率（P < 0.05，n = 80例浸润性癌; TCGA RNA序列）。结论：N-of-1-pathways MD为精准医学提供了一种实用的方法。该方法产生的幅度和生物学意义的个人解除管制的途径的结果，仅来自患者的转录组。这些途径提供了获得临床可行决策的机会，这些决策有可能补充从DNA获得或遗传多态性和突变获得的个人多态性的临床可解释性。此外，它提供了一个机会，适用于DNA变化可能不相关的疾病，从而扩大了“可解释的”组学“的单一主题（例如personalome）。可用性和实施情况：http://www.lussierlab.net/publications/N-of-1-pathways。联系方式：yves@email.arizona.edu或piegorsch@math.arizona.edu补充信息：补充数据可在生物信息学在线获得。
Motivation: The conventional approach to personalized medicine relies on molecular data analytics across multiple patients. The path to precision medicine lies with molecular data analytics that can discover interpretable single-subject signals (N-of-1). We developed a global framework, N-of-1-pathways, for a mechanistic-anchored approach to single-subject gene expression data analysis. We previously employed a metric that could prioritize the statistical significance of a deregulated pathway in single subjects, however, it lacked in quantitative interpretability (e.g. the equivalent to a gene expression fold-change). Results: In this study, we extend our previous approach with the application of statistical Mahalanobis distance (MD) to quantify personal pathway-level deregulation. We demonstrate that this approach, N-of-1-pathways Paired Samples MD (N-OF-1-PATHWAYS-MD), detects deregulated pathways (empirical simulations), while not inflating false-positive rate using a study with biological replicates. Finally, we establish that N-OF-1-PATHWAYS-MD scores are, biologically significant, clinically relevant and are predictive of breast cancer survival (P < 0.05, n = 80 invasive carcinoma; TCGA RNA-sequences). Conclusion: N-of-1-pathways MD provides a practical approach towards precision medicine. The method generates the magnitude and the biological significance of personal deregulated pathways results derived solely from the patient’s transcriptome. These pathways offer the opportunities for deriving clinically actionable decisions that have the potential to complement the clinical interpretability of personal polymorphisms obtained from DNA acquired or inherited polymorphisms and mutations. In addition, it offers an opportunity for applicability to diseases in which DNA changes may not be relevant, and thus expand the ‘interpretable ‘omics’ of single subjects (e.g. personalome). Availability and implementation: http://www.lussierlab.net/publications/N-of-1-pathways. Contact: yves@email.arizona.edu or piegorsch@math.arizona.edu Supplementary information: Supplementary data are available at Bioinformatics online.