Visualizing multidimensional cancer genomics data

Cancer genomics benefits from high-throughput technologies that allow the comparison of the genomic sequences, epigenomic profiles, and transcriptomes of tumor cells with those of normal cells. These technologies often characterize different types of somatic alterations (or variations) in a tumor cell population that are absent from normal cells - including copy number alterations (CNAs), mutations, gene expression changes and methylation changes [1-4]. Together, these somatic alterations constitute multidimensional oncogenomics datasets that describe the variations that coexist in common elements (for example, the genes) of the genome (or transcriptome) of a particular cohort of tumor cells. Such data are currently being used to identify cancer-driver genes and pathways, to discover molecular targets for new therapies, and to define molecular profiles that characterize clinically meaningful patient categories. An array of analytical methods are currently used to exploit the information contained within this multidimensional layout [5-12].Along with computational and statistical methodologies, effective visual exploration by experts is crucial to successful extraction of knowledge from oncogenomics data. For example, this step might be key to unraveling rare genomic events, verifying data quality at maximum resolution or identifying key players in cancer development. Thus, researchers need intuitive tools that allow the visual integration and simultaneous exploration of both different types of alterations and clinical information. Many data visualization tools have been developed in recent years to support genomic studies. In this review, we revisit the most common ways in which these data are visualized, and present selected tools that allow researchers to visualize multidimensional oncogenomics datasets effectively (Table 1).To aid our review of the tools, we describe four case studies that illustrate their use: the visual exploration of 1) alterations in cancer-d