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Special issue: cancer OMICS.

This special issue ‘‘Cancer OMICS’’ includes both invited lectures and poster articles presented at the tenth Advanced Meeting of the series on Experimental Oncology and its Clinical Application, on Cancer Omics (From bedside to desktop cancer medicine and backwards: the implementation of omic sciences in the prevention and treatment of human tumors) that was held in Erice (Italy) on May 3–8, 2010. In the last decade, the completion of the Human Genome Project has represented a fundamental milestone in biomedical sciences (Collins et al., 2003). Recently, large-scale analysis of gene expression profiles, namely, the assessment of gene transcriptional activity (genomics), has been made possible thanks to the development of high-throughput technologies, namely, microarrays. This methodology has provided scientists with unprecedented opportunities to effectively approach and untangle critical unresolved issues in research on cancer and other chronic, life-threatening diseases. This approach, combined with more conventional ones, might ultimately lead to define biomolecular fingerprints of individual tumors and, hence, to develop personalized diagnostic, prognostic, and therapeutic measures. In addition, with the advance of some highly complementary technologies, such as laser capture microdissection, unbiased RNA amplification, customized functional arrays (e.g., single-nucleotide polymorphism chips), and user-friendly bioinformatics software, this technology is becoming increasingly used by investigators in the field. A pointed example is represented today by the so-called MammaPrint, the first multivariate in vitro diagnostic assay, approved for use by the U.S. FDA, that is based on the Amsterdam 70-gene signature for breast cancer (van’t Veer et al., 2002). This assay translates transcriptional activity of the selected genes into a score that allows discriminating breast cancer patients at high risk and those at low risk of relapse, thereby leading to deliver the most appropriate individual treatment according to each patient’s risk classification. Although genomics may provide important information about significant genetic changes that are associated with the outcome of disease and response to treatment, alteration in the amazingly large repertoire of cellular proteins (proteome), caused by posttranslational and/or epigenetic modifications, is implicated in the majority of human diseases, including cancer. The human proteome is far more complex than the genome, and its alterations in cancer can occur in ways that cannot be predicted by the genomic analysis alone. Hence, proteomics represents a more direct approach to studying disease at the individual patient level. In addition, because most novel biomolecular targets are proteins and the mapping of entire networks rather than individual markers often appears to be necessary for robust diagnostics and for tailoring of therapy, proteomic analysis may potentially have a crucial role to improve current capacities to cure cancer and other diseases. However, there is a need to develop highthroughput multiplexed proteomic techniques to dissect changes in the proteome that are relevant to diseases development and progression. Novel protein arrays, combined with advanced bioinformatics, are currently being tested to identify protein signatures of individual tumors. Deranged cell proliferation in cancer is regulated by complex, interwoven feedback loops at multiple levels. These loops are based upon the dynamic interaction of small molecular-weight substances (metabolites) with molecular targets implicated in cellular signaling pathways, including cell proliferation and differentiation, apoptosis, angiogenesis, cell cycle regulation, oxidative stress, inflammation, and immune response. Metabolomics explores the subtle changes that may occur in these metabolites, their patterns and interaction prior to the detection of a gross phenotypic change reflecting disease, including cancer. In this context, specific changes in the metabolome might be used to predict and/or to prevent subsequent phenotypic changes that are associated with tumor development and/or progression. It is envisaged that the integration metabolomics with other ‘‘omics’’ would provide both a far more comprehensive approach for a better understanding of biomolecular mechanisms implicated in cancer and an experimental basis for the development of individualized preventive and therapeutic anticancer strategies. High-throughput omic technologies are today being established and validated in cancer research aiming to create better predictive models for diagnosis, prognosis, and therapy, to identify and characterize key signaling networks and to find new targets for drug development. However, these advanced technologies present investigators with the challenge of extracting meaningful statistical and biological information from high-dimensional data spaces, wherein each sample is defined by several thousands of measurements obtained concurrently. In this framework, several aspects limit today the use of omic technologies in cancer research and, especially, the translation of their results into clinical practice. In particular, disputed data consistency and reproducibility, underutilized data-mining tools, inadequate level of bioinformatics, limited access to and use of neural networking and machine-learning techniques, as well as the elevated cost of