Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine & Biomedical Data Science, Stanford University, United States.
Program in Translational Neuropsychiatric Genomics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Broad Institute of Harvard and Massachusetts Institute of Technology, United States; Advanced Integrated Sensing Lab, Campus Geel, Department of Computer Science, University of Leuven, Belgium.
EBioMedicine. 2018 Jan;27:156-166. doi: 10.1016/j.ebiom.2017.11.028. Epub 2017 Dec 1.
The availability of increasing volumes of multi-omics profiles across many cancers promises to improve our understanding of the regulatory mechanisms underlying cancer. The main challenge is to integrate these multiple levels of omics profiles and especially to analyze them across many cancers. Here we present AMARETTO, an algorithm that addresses both challenges in three steps. First, AMARETTO identifies potential cancer driver genes through integration of copy number, DNA methylation and gene expression data. Then AMARETTO connects these driver genes with co-expressed target genes that they control, defined as regulatory modules. Thirdly, we connect AMARETTO modules identified from different cancer sites into a pancancer network to identify cancer driver genes. Here we applied AMARETTO in a pancancer study comprising eleven cancer sites and confirmed that AMARETTO captures hallmarks of cancer. We also demonstrated that AMARETTO enables the identification of novel pancancer driver genes. In particular, our analysis led to the identification of pancancer driver genes of smoking-induced cancers and 'antiviral' interferon-modulated innate immune response.
AMARETTO is available as an R package at https://bitbucket.org/gevaertlab/pancanceramaretto.
越来越多的多种组学图谱在许多癌症中得到了应用，这有望提高我们对癌症相关调控机制的理解。主要的挑战是整合这些多层次的组学图谱，特别是在许多癌症中进行分析。在这里，我们提出了 AMARETTO 算法，它通过三个步骤来解决这两个挑战。首先，AMARETTO 通过整合拷贝数、DNA 甲基化和基因表达数据来识别潜在的癌症驱动基因。然后，AMARETTO 将这些驱动基因与它们所调控的共表达靶基因连接起来，定义为调控模块。第三，我们将来自不同癌症部位的 AMARETTO 模块连接成一个泛癌症网络，以识别癌症驱动基因。在这里，我们在一个包含 11 个癌症部位的泛癌症研究中应用了 AMARETTO，并证实了 AMARETTO 能够捕获癌症的特征。我们还证明了 AMARETTO 能够识别新的泛癌症驱动基因。特别是，我们的分析确定了吸烟诱导的癌症和“抗病毒”干扰素调节的先天免疫反应的泛癌症驱动基因。
AMARETTO 可作为 R 包在 https://bitbucket.org/gevaertlab/pancanceramaretto 上获得。
