Module analysis captures pancancer (epi)genetically deregulated cancer driver genes for smoking and antiviral response

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.\n\nSoftware availabilityAMARETTO is available as an R package at https://bitbucket.org/gevaertlab/pancanceramaretto\n\nHighlightsO_LIWe present an algorithm for pancancer identification of cancer driver genes based on multiomics data fusion\nC_LIO_LIGPX2 is a novel driver gene in smoking induced cancers and validated using knockdown of GPX2 in the A549 cell line.\nC_LIO_LIOAS2 is a novel driver gene defining cancers with an antiviral signature supported by increased infiltration of tumor-associated macrophages.\nC_LI\n\nResearch in contextWe 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.