Li Qike, Schissler A Grant, Gardeux Vincent, Berghout Joanne, Achour Ikbel, Kenost Colleen, 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; Department of Medicine, The University of Arizona, Tucson, AZ 85721, USA; Graduate Interdisciplinary Program in Statistics, The University of Arizona, Tucson, AZ 85721, USA.
Center for Biomedical Informatics and Biostatistics, The University of Arizona, Tucson, AZ 85721, USA; Bio5 Institute, The University of Arizona, Tucson, AZ 85721, USA; Department of Medicine, The University of Arizona, Tucson, AZ 85721, USA.
J Biomed Inform. 2017 Feb;66:32-41. doi: 10.1016/j.jbi.2016.12.009. Epub 2016 Dec 19.
Understanding dynamic, patient-level transcriptomic response to therapy is an important step forward for precision medicine. However, conventional transcriptome analysis aims to discover cohort-level change, lacking the capacity to unveil patient-specific response to therapy. To address this gap, we previously developed two N-of-1-pathways methods, Wilcoxon and Mahalanobis distance, to detect unidirectionally responsive transcripts within a pathway using a pair of samples from a single subject. Yet, these methods cannot recognize bidirectionally (up and down) responsive pathways. Further, our previous approaches have not been assessed in presence of background noise and are not designed to identify differentially expressed mRNAs between two samples of a patient taken in different contexts (e.g. cancer vs non cancer), which we termed responsive transcripts (RTs).
We propose a new N-of-1-pathways method, k-Means Enrichment (kMEn), that detects bidirectionally responsive pathways, despite background noise, using a pair of transcriptomes from a single patient. kMEn identifies transcripts responsive to the stimulus through k-means clustering and then tests for an over-representation of the responsive genes within each pathway. The pathways identified by kMEn are mechanistically interpretable pathways significantly responding to a stimulus.
In ∼9000 simulations varying six parameters, superior performance of kMEn over previous single-subject methods is evident by: (i) improved precision-recall at various levels of bidirectional response and (ii) lower rates of false positives (1-specificity) when more than 10% of genes in the genome are differentially expressed (background noise). In a clinical proof-of-concept, personal treatment-specific pathways identified by kMEn correlate with therapeutic response (p-value<0.01).
Through improved single-subject transcriptome dynamics of bidirectionally-regulated signals, kMEn provides a novel approach to identify mechanism-level biomarkers.
了解患者层面的动态转录组对治疗的反应是精准医学向前迈出的重要一步。然而，传统的转录组分析旨在发现队列层面的变化，缺乏揭示患者对治疗的特异性反应的能力。为了弥补这一差距，我们之前开发了两种单病例通路方法，即威尔科克森法和马氏距离法，以使用来自单个受试者的一对样本检测通路内单向反应性转录本。然而，这些方法无法识别双向（上调和下调）反应性通路。此外，我们之前的方法尚未在存在背景噪声的情况下进行评估，也未设计用于识别在不同背景下（例如癌症与非癌症）采集的患者的两个样本之间差异表达的mRNA，我们将其称为反应性转录本（RTs）。
我们提出了一种新的单病例通路方法，即k均值富集法（kMEn），该方法使用来自单个患者的一对转录组，即使存在背景噪声也能检测双向反应性通路。kMEn通过k均值聚类识别对刺激有反应的转录本，然后测试每个通路内反应性基因的过度表达情况。kMEn识别出的通路是对刺激有显著反应的可进行机制解释的通路。
在改变六个参数的约9000次模拟中，kMEn相对于之前的单病例方法具有卓越性能，这体现在：（i）在不同双向反应水平下提高了精确召回率，以及（ii）当基因组中超过10%的基因差异表达（背景噪声）时，假阳性率（1-特异性）更低。在一项临床概念验证中，kMEn识别出的针对个人治疗的特异性通路与治疗反应相关（p值<0.01）。
通过改进双向调节信号的单病例转录组动态，kMEn提供了一种识别机制层面生物标志物的新方法。
