Transcription-based prediction of response to IFNbeta using supervised computational methods.
Transcription-based prediction of response to IFNbeta using supervised computational methods.
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DOI：
10.1371/journal.pbio.0030002
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发表时间：
2005-01
期刊：
影响因子：
9.8
通讯作者：
Oksenberg JR
中科院分区：
文献类型：
--
作者：
Baranzini SE;Mousavi P;Rio J;Caillier SJ;Stillman A;Villoslada P;Wyatt MM;Comabella M;Greller LD;Somogyi R;Montalban X;Oksenberg JR
响应于药物治疗的细胞功能的变化由特异性转录谱介导，所述特异性转录谱由许多基因活性的诱导或抑制产生，从而改变药物靶向细胞的预先存在的基因活性模式。重组人干扰素β（rIFNβ）通常用于控制多发性硬化症患者的病情加重，但仅部分成功，主要是因为不良反应和相对较大比例的无应答者。我们将先进的数据挖掘和预测建模工具应用于通过动态逆转录PCR生成的纵向70个基因表达数据集，该数据集来自52名接受rIFNβ治疗的多发性硬化症患者，以发现与治疗结果相关的高阶预测模式，并定义分子足迹rIFNβ在外周血单核细胞上雕刻。我们确定了9组基因三联体，在治疗开始前检测其表达，可以预测对干扰素β的反应，准确率高达86%。此外，时间序列分析揭示了参与干扰素β良好或不良反应的潜在关键参与者。进行随机结果类别和噪声耐受性的统计检验，以建立预测模型的稳健性。大规模动态逆转录PCR，加上先进的数据挖掘工作，可以有效地揭示与治疗效果相关的预先存在的和药物诱导的基因表达特征。通过研究多发性硬化症患者在β干扰素治疗前后的基因表达，有可能识别出与治疗效果相关的基因表达特征
Changes in cellular functions in response to drug therapy are mediated by specific transcriptional profiles resulting from the induction or repression in the activity of a number of genes, thereby modifying the preexisting gene activity pattern of the drug-targeted cell(s). Recombinant human interferon beta (rIFNβ) is routinely used to control exacerbations in multiple sclerosis patients with only partial success, mainly because of adverse effects and a relatively large proportion of nonresponders. We applied advanced data-mining and predictive modeling tools to a longitudinal 70-gene expression dataset generated by kinetic reverse-transcription PCR from 52 multiple sclerosis patients treated with rIFNβ to discover higher-order predictive patterns associated with treatment outcome and to define the molecular footprint that rIFNβ engraves on peripheral blood mononuclear cells. We identified nine sets of gene triplets whose expression, when tested before the initiation of therapy, can predict the response to interferon beta with up to 86% accuracy. In addition, time-series analysis revealed potential key players involved in a good or poor response to interferon beta. Statistical testing of a random outcome class and tolerance to noise was carried out to establish the robustness of the predictive models. Large-scale kinetic reverse-transcription PCR, coupled with advanced data-mining efforts, can effectively reveal preexisting and drug-induced gene expression signatures associated with therapeutic effects. By studying gene expression in patients with multiple sclerosis before and after therapy with beta interferon, it is possible to identify gene expression signatures that are associated with therapeutic effects
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影响因子：
2.3
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Dupont, SA;Goelz, S;Green, M
通讯作者：
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