A genome-by-environment interaction classifier for precision medicine: personal transcriptome response to rhinovirus identifies children prone to asthma exacerbations.
A genome-by-environment interaction classifier for precision medicine: personal transcriptome response to rhinovirus identifies children prone to asthma exacerbations.
复制标题
DOI：
10.1093/jamia/ocx069
复制
发表时间：
2017-11-01
期刊：
影响因子：
--
通讯作者：
Lussier YA
中科院分区：
文献类型：
--
作者：
Gardeux V;Berghout J;Achour I;Schissler AG;Li Q;Kenost C;Li J;Shang Y;Bosco A;Saner D;Halonen MJ;Jackson DJ;Li H;Martinez FD;Lussier YA
介绍一个疾病预后框架，该框架由来自患者特异性转录组对刺激的反应的稳健分类方案实现。在一个预测哮喘急性发作的说明性病例研究中，我们设计了一种刺激试验，揭示了对人鼻病毒的个体化转录组反应。从23名儿童哮喘患者的外周血单个核细胞中定量基因表达，并在体外用人鼻病毒刺激。通过单一受试者基因集测试方法“1个途径中的N个途径”获得应答。在相关的独立训练数据集（n = 19）上训练分类器。提供了个人转录组响应的新颖可视化。在23例儿童哮喘患者中，12例发生复发性急性发作。我们的分类器使用个性化的响应并在独立的数据集上进行训练，获得了74%的准确率（接受者工作曲线下面积为71%;双侧P = 0.039）。使用病毒暴露样本中信使RNA（mRNA）表达的传统分类器不成功（所有患者均预测复发性加重;准确度为52%）。基于单个时间点的预后，单独的静态mRNA表达忽略了表型呈现中动态基因组与环境相互作用的重要性。在途径（基因集）水平量化的个体化转录组学反应揭示了与临床结局相关的可解释信号。拟议的框架为精准医疗提供了一种创新方法。我们表明，量化个人通路水平转录组对疾病相关环境挑战的反应可预测疾病进展。这种基因组与环境相互作用分析提供了一个非侵入性的机会，通过提高预测疾病恶化的能力和增加产生更有效的治疗决策的潜力，将组学数据转化为临床实践。
To introduce a disease prognosis framework enabled by a robust classification scheme derived from patient-specific transcriptomic response to stimulation. Within an illustrative case study to predict asthma exacerbation, we designed a stimulation assay that reveals individualized transcriptomic response to human rhinovirus. Gene expression from peripheral blood mononuclear cells was quantified from 23 pediatric asthmatic patients and stimulated in vitro with human rhinovirus. Responses were obtained via the single-subject gene set testing methodology “N-of-1-pathways.” The classifier was trained on a related independent training dataset (n = 19). Novel visualizations of personal transcriptomic responses are provided. Of the 23 pediatric asthmatic patients, 12 experienced recurrent exacerbations. Our classifier, using individualized responses and trained on an independent dataset, obtained 74% accuracy (area under the receiver operating curve of 71%; 2-sided P = .039). Conventional classifiers using messenger RNA (mRNA) expression within the viral-exposed samples were unsuccessful (all patients predicted to have recurrent exacerbations; accuracy of 52%). Prognosis based on single time point, static mRNA expression alone neglects the importance of dynamic genome-by-environment interplay in phenotypic presentation. Individualized transcriptomic response quantified at the pathway (gene sets) level reveals interpretable signals related to clinical outcomes. The proposed framework provides an innovative approach to precision medicine. We show that quantifying personal pathway–level transcriptomic response to a disease-relevant environmental challenge predicts disease progression. This genome-by-environment interaction assay offers a noninvasive opportunity to translate omics data to clinical practice by improving the ability to predict disease exacerbation and increasing the potential to produce more effective treatment decisions.
登录
查看更多内容
DOI：
10.1136/amiajnl-2013-002519
发表时间：
2014-11
期刊：
Journal of the American Medical Informatics Association : JAMIA
影响因子：
--
作者：
Gardeux V;Achour I;Li J;Maienschein-Cline M;Li H;Pesce L;Parinandi G;Bahroos N;Winn R;Foster I;Garcia JG;Lussier YA
通讯作者：
Lussier YA
DOI：
10.1056/nejmoa1605086
发表时间：
2016-12-15
期刊：
The New England journal of medicine
影响因子：
--
作者：
Khera AV;Emdin CA;Drake I;Natarajan P;Bick AG;Cook NR;Chasman DI;Baber U;Mehran R;Rader DJ;Fuster V;Boerwinkle E;Melander O;Orho-Melander M;Ridker PM;Kathiresan S
通讯作者：
Kathiresan S
影响因子：
3.3
作者：
Forno E;Celedón JC
通讯作者：
Celedón JC
使用筛选测试和分层错误发现率控制的组合来鉴定重要的基因环境相互作用。
影响因子：
2.1
作者：
Frost, H. Robert;Shen, Li;Saykin, Andrew J.;Williams, Scott M.;Moore, Jason H.
通讯作者：
Moore, Jason H.
影响因子：
12.3
作者：
Gentleman RC;Carey VJ;Bates DM;Bolstad B;Dettling M;Dudoit S;Ellis B;Gautier L;Ge Y;Gentry J;Hornik K;Hothorn T;Huber W;Iacus S;Irizarry R;Leisch F;Li C;Maechler M;Rossini AJ;Sawitzki G;Smith C;Smyth G;Tierney L;Yang JY;Zhang J
通讯作者：
Zhang J