Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM.
Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM.
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DOI：
10.1093/bioinformatics/btq182
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发表时间：
2010-06-15
期刊：
影响因子：
--
通讯作者：
Stuart JM
中科院分区：
文献类型：
--
作者：
Vaske CJ;Benz SC;Sanborn JZ;Earl D;Szeto C;Zhu J;Haussler D;Stuart JM
动机：高通量数据提供了癌症组织中分子变化的全面视图。新技术允许同时对肿瘤样本和癌细胞系的基因组拷贝数变异、基因表达、DNA 甲基化和表观遗传学状态进行全基因组分析。对当前数据集的分析发现，患者之间的基因改变可能有所不同，但通常涉及共同的途径。因此，确定癌症进展的相关途径并检测它们在不同患者中如何改变至关重要。结果：我们提出了一种推断患者特异性遗传活动的新方法，该方法结合了基因之间精心策划的途径相互作用。基因通过因子图建模为一组相互关联的变量，编码基因及其产物的表达和已知活性，从而允许合并多种类型的组学数据作为证据。该方法使用概率推理来预测患者体内通路活动（例如内部基因状态、相互作用或高水平“输出”）改变的程度。与称为 SPIA 的竞争通路活性推断方法相比，我们的方法在多形性胶质母细胞瘤 (GBM) 和乳腺癌数据集中识别了癌症相关通路中的改变活动，且假阳性较少。 PARADIGM 确定了 GBM 患者子集一致的通路水平活性，而当单独考虑基因时，这些活性会被忽视。此外，根据显着的通路扰动对 GBM 患者进行分组，将他们分为具有显着不同生存结果的临床相关亚组。这些发现表明，可以选择针对一组患者通常受到干扰的通路中关键点的基因的治疗方法。可用性：源代码可在 http://sbenz.github.com/Paradigm 联系：jstuart@soe.ucsc.edu 补充信息：补充数据可在 Bioinformatics 在线获取。
Motivation: High-throughput data is providing a comprehensive view of the molecular changes in cancer tissues. New technologies allow for the simultaneous genome-wide assay of the state of genome copy number variation, gene expression, DNA methylation and epigenetics of tumor samples and cancer cell lines. Analyses of current data sets find that genetic alterations between patients can differ but often involve common pathways. It is therefore critical to identify relevant pathways involved in cancer progression and detect how they are altered in different patients. Results: We present a novel method for inferring patient-specific genetic activities incorporating curated pathway interactions among genes. A gene is modeled by a factor graph as a set of interconnected variables encoding the expression and known activity of a gene and its products, allowing the incorporation of many types of omic data as evidence. The method predicts the degree to which a pathway's activities (e.g. internal gene states, interactions or high-level ‘outputs’) are altered in the patient using probabilistic inference. Compared with a competing pathway activity inference approach called SPIA, our method identifies altered activities in cancer-related pathways with fewer false-positives in both a glioblastoma multiform (GBM) and a breast cancer dataset. PARADIGM identified consistent pathway-level activities for subsets of the GBM patients that are overlooked when genes are considered in isolation. Further, grouping GBM patients based on their significant pathway perturbations divides them into clinically-relevant subgroups having significantly different survival outcomes. These findings suggest that therapeutics might be chosen that target genes at critical points in the commonly perturbed pathway(s) of a group of patients. Availability:Source code available at http://sbenz.github.com/Paradigm Contact: jstuart@soe.ucsc.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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