Functional characterization of somatic mutations in cancer using network-based inference of protein activity.
Functional characterization of somatic mutations in cancer using network-based inference of protein activity.
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DOI：
10.1038/ng.3593
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发表时间：
2016-08
期刊：
影响因子：
30.8
通讯作者：
Califano, Andrea
中科院分区：
文献类型：
--
作者：
Alvarez, Mariano J.;Shen, Yao;Giorgi, Federico M.;Lachmann, Alexander;Ding, B. Belinda;Ye, B. Hilda;Califano, Andrea
确定多种失调的癌蛋白，有助于肿瘤发生在一个给定的病人是至关重要的制定个性化的治疗计划。然而，准确推断生物样品中的异常蛋白质活性仍然具有挑战性，因为遗传改变仅是部分预测性的，并且蛋白质活性的直接测量通常是不可行的。为了解决这个问题，我们引入并实验验证了一种新的算法，VIPER（虚拟推断蛋白质活性的富集调节子分析），从基因表达数据的蛋白质活性的准确评估。我们使用VIPER来评估所有TCGA样本中调节蛋白的遗传改变的功能相关性。除了准确地推断由已建立的突变诱导的异常蛋白质活性，我们还确定了一个显着比例的肿瘤与异常活性的药物致癌蛋白，尽管缺乏突变，反之亦然。体外试验证实，VIPER推断的蛋白质活性在预测对靶向抑制剂的敏感性方面优于突变分析。
Identifying the multiple dysregulated oncoproteins that contribute to tumorigenesis in a given patient is crucial for developing personalized treatment plans. However, accurate inference of aberrant protein activity in biological samples is still challenging as genetic alterations are only partially predictive and direct measurements of protein activity are generally not feasible. To address this problem we introduce and experimentally validate a new algorithm, VIPER (Virtual Inference of Protein-activity by Enriched Regulon analysis), for the accurate assessment of protein activity from gene expression data. We use VIPER to evaluate the functional relevance of genetic alterations in regulatory proteins across all TCGA samples. In addition to accurately inferring aberrant protein activity induced by established mutations, we also identify a significant fraction of tumors with aberrant activity of druggable oncoproteins—despite a lack of mutations, and vice-versa. In vitro assays confirmed that VIPER-inferred protein activity outperforms mutational analysis in predicting sensitivity to targeted inhibitors.
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影响因子：
3.7
作者：
Boorsma, Andre;Lu, Xiang-Jun;Zakrzewska, Anna;Klis, Frans M.;Bussemaker, Harmen J.
通讯作者：
Bussemaker, Harmen J.
影响因子：
64.5
作者：
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通讯作者：
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影响因子：
20.3
作者：
Basso, Katia;Saito, Masumichi;Dalla-Favera, Riccardo
通讯作者：
Dalla-Favera, Riccardo
影响因子：
12.3
作者：
Alvarez, Mariano Javier;Sumazin, Pavel;Califano, Andrea
通讯作者：
Califano, Andrea
影响因子：
64.8
作者：
Alizadeh, AA;Eisen, MB;Staudt, LM
通讯作者：
Staudt, LM