PT  - JOURNAL ARTICLE
AU  - Magali Champion
AU  - Kevin Brennan
AU  - Tom Croonenborghs
AU  - Andrew J. Gentles
AU  - Nathalie Pochet
AU  - Olivier Gevaert
TI  - Module analysis captures pancancer genetically and epigenetically deregulated cancer driver genes for smoking and antiviral response
AID  - 10.1101/216754
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 216754
4099  - http://biorxiv.org/content/early/2017/11/29/216754.short
4100  - http://biorxiv.org/content/early/2017/11/29/216754.full
AB  - 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 HighlightsWe present an algorithm for pancancer identification of cancer driver genes based on multiomics data fusionGPX2 is a novel driver gene in smoking induced cancers and validated using knockdown of GPX2 in the A549 cell line.OAS2 is a novel driver gene defining cancers with an antiviral signature supported by increased infiltration of tumor-associated macrophages.Research in context We present an algorithm that combines multiple sources of molecular data to identify novel genes that are involved in cancer development. We applied this algorithm on multiple cancers in a combined fashion and identified a network of pancancer driver genes. We highlighted two genes in detail GPX2 and OAS2. We showed that GPX2 is an important cancer gene in smoking induced cancers, and validated our predictions using experimental data where GPX2 was inactivated in a lung cancer cell line. Similarly we showed that OAS2 is an important cancer driver gene in cancers that show an antiviral signature.