Li Qike, Schissler A Grant, Gardeux Vincent, Achour Ikbel, Kenost Colleen, Berghout Joanne, Li Haiquan, Zhang Hao Helen, Lussier Yves A
Center for Biomedical Informatics and Biostatistics, The University of Arizona, Tucson, AZ, 85721, USA.
Bio5 Institute, The University of Arizona, Tucson, AZ, 85721, USA.
BMC Med Genomics. 2017 May 24;10(Suppl 1):27. doi: 10.1186/s12920-017-0263-4.
Transcriptome analytic tools are commonly used across patient cohorts to develop drugs and predict clinical outcomes. However, as precision medicine pursues more accurate and individualized treatment decisions, these methods are not designed to address single-patient transcriptome analyses. We previously developed and validated the N-of-1-pathways framework using two methods, Wilcoxon and Mahalanobis Distance (MD), for personal transcriptome analysis derived from a pair of samples of a single patient. Although, both methods uncover concordantly dysregulated pathways, they are not designed to detect dysregulated pathways with up- and down-regulated genes (bidirectional dysregulation) that are ubiquitous in biological systems.
We developed N-of-1-pathways MixEnrich, a mixture model followed by a gene set enrichment test, to uncover bidirectional and concordantly dysregulated pathways one patient at a time. We assess its accuracy in a comprehensive simulation study and in a RNA-Seq data analysis of head and neck squamous cell carcinomas (HNSCCs). In presence of bidirectionally dysregulated genes in the pathway or in presence of high background noise, MixEnrich substantially outperforms previous single-subject transcriptome analysis methods, both in the simulation study and the HNSCCs data analysis (ROC Curves; higher true positive rates; lower false positive rates). Bidirectional and concordant dysregulated pathways uncovered by MixEnrich in each patient largely overlapped with the quasi-gold standard compared to other single-subject and cohort-based transcriptome analyses.
The greater performance of MixEnrich presents an advantage over previous methods to meet the promise of providing accurate personal transcriptome analysis to support precision medicine at point of care.
转录组分析工具通常用于跨患者队列以开发药物和预测临床结果。然而，随着精准医学追求更准确和个性化的治疗决策，这些方法并非设计用于处理单患者转录组分析。我们之前开发并验证了N-of-1通路框架，使用两种方法，即威尔科克森法和马氏距离（MD），用于对来自单患者的一对样本进行个人转录组分析。尽管这两种方法都能一致地揭示失调的通路，但它们并非设计用于检测生物系统中普遍存在的具有上调和下调基因的失调通路（双向失调）。
我们开发了N-of-1通路混合富集法，这是一种混合模型，随后进行基因集富集测试，以便一次分析一名患者的双向和一致失调的通路。我们在一项全面的模拟研究以及对头颈部鳞状细胞癌（HNSCC）的RNA测序数据分析中评估了其准确性。在通路中存在双向失调基因或存在高背景噪声的情况下，混合富集法在模拟研究和HNSCC数据分析中均显著优于先前的单受试者转录组分析方法（ROC曲线；更高的真阳性率；更低的假阳性率）。与其他单受试者和基于队列的转录组分析相比，混合富集法在每位患者中揭示的双向和一致失调的通路与准金标准在很大程度上重叠。
混合富集法的卓越性能相对于先前方法具有优势，有望在医疗点提供准确的个人转录组分析以支持精准医学。
