Integration of omics data for cancer research

The development of high-throughput techniques for analyzing cell components has provided vast amounts of data in recent years. This development of gene-sequencing methods was followed by advances in techniques for analyzing and managing data from transcriptomes, proteomes, and other omics data. The so-called omics revolution has led to the development of numerous databases describing specific cell components. A recent study suggests that cell behavior cannot be modeled by analyzing its constituents separately, but rather calls for an integrative approach (Barabási and Oltvai 2004). Thus, specialized techniques are being developed to integrate omics information. To enable new research avenues that can take advantage of and apply this information to new therapies – e.g. in cancer research – methods must be designed that provide a seamless integration of these new databases with classical clinical data. The problem of database integration has been studied at length over the last 15 years, with special emphasis in the post-genomic era on publicly available online data. In the field of genomic medicine, the integration of phenotype and genotype data is of special interest for the prevention of patient intolerance to specific drugs and for defining personalized therapies. Patients’ individual characteristics will play a fundamental role in future treatment design. These characteristics include, of course, genetic profiles. To address the issues surrounding omics data integration, this chapter is organized as follows. Section 14.1 describes the role of data integration in cancer research. Section 14.2 analyzes omics data integration problems and techniques. Section 14.3 introduces a range of international efforts in database integration. Finally, Section 14.4 presents future trends in omics data integration for cancer research