Complexity in cancer biology: is systems biology the answer?

• Published in: Cancer medicine (Malden, MA) MA), 2013-04, Vol.2 (2), p.164-177
• Main Authors: Koutsogiannouli, Evangelia, Papavassiliou, Athanasios G., Papanikolaou, Nikolaos A.
• Format: Article
• Language: English
• Subjects: Algorithms; Biology; Biomarkers; Cancer; Cancer Biology; Cell cycle; Cell growth; Collections; complex; Computational Biology; Connectivity; cyclin‐dependent kinase; cyclin‐dependent kinase inhibitors signaling; Deoxyribonucleic acid; DNA; Ecosystem biology; Ecosystems; Epigenetics; Gene expression; Genome - genetics; Humans; Kinases; Macromolecules; Medical research; Metabolism; module; Neoplasm Proteins - genetics; Neoplasm Proteins - metabolism; Neoplasms - genetics; Neoplasms - metabolism; Neoplasms - pathology; network; oncogene; oncoprotein; Phenotypes; Proteins; Signal Transduction; Statistical analysis; system; Systems Biology; Transcription factors; tumor suppressor gene; tumor suppressor protein
• ISSN: 2045-7634; 2045-7634
• DOI: 10.1002/cam4.62

Complex phenotypes emerge from the interactions of thousands of macromolecules that are organized in multimolecular complexes and interacting functional modules. In turn, modules form functional networks in health and disease. Omics approaches collect data on changes for all genes and proteins and statistical analysis attempts to uncover the functional modules that perform the functions that characterize higher levels of biological organization. Systems biology attempts to transcend the study of individual genes/proteins and to integrate them into higher order information. Cancer cells exhibit defective genetic and epigenetic networks formed by altered complexes and network modules arising in different parts of tumor tissues that sustain autonomous cell behavior which ultimately lead tumor growth. We suggest that an understanding of tumor behavior must address not only molecular but also, and more importantly, tumor cell heterogeneity, by considering cancer tissue genetic and epigenetic networks, by characterizing changes in the types, composition, and interactions of complexes and networks in the different parts of tumor tissues, and by identifying critical hubs that connect them in time and space. Tumor tissues consist of different types of cells with altered genetic, epigenetic, and protein networks. We propose a simple conceptual model to account for oncogenic and tumor suppressor proteins forming different complexes within and between tumor cells.