Integrating the increasing number of available multi-omics cancer data remains 
one of the main challenges to improve our understanding of cancer. To this aim, 
different strategies can be set up, among others AMARETTO, an algorithm that 
integrates DNA methylation, DNA copy number and gene expression data to identify 
cancer driver genes and associates them to modules of co-expressed genes. A 
pancancer version of AMARETTO, which connects all modules in pancancer 
communities, can also lead to the identification of major oncogenic pathways and 
master regulators involved in different cancers.