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EBioMedicine. 2018 Jan;27:156-166. doi: 10.1016/j.ebiom.2017.11.028. Epub 2017 Dec 1.

Module Analysis Captures Pancancer Genetically and Epigenetically Deregulated Cancer Driver Genes for Smoking and Antiviral Response.

Author information

1
Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine & Biomedical Data Science, Stanford University, United States.
2
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.
3
Department of Medicine, Center for Cancer Systems Biology, Stanford University, United States.
4
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.
5
Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine & Biomedical Data Science, Stanford University, United States. Electronic address: ogevaert@stanford.edu.

Abstract

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.

SOFTWARE AVAILABILITY:

AMARETTO is available as an R package at https://bitbucket.org/gevaertlab/pancanceramaretto.

KEYWORDS:

Cancer driver gene discovery; Data fusion; Module network

PMID:
29331675
PMCID:
PMC5828545
DOI:
10.1016/j.ebiom.2017.11.028
[Indexed for MEDLINE]
Free PMC Article

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