Complexity in cancer biology: is systems biology the answer?

Abstract

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.

Keywords: Cell cycle; complex; cyclin-dependent kinase; cyclin-dependent kinase inhibitors signaling; module; network; oncogene; oncoprotein; system; tumor suppressor gene; tumor suppressor protein.

Figure 1

Representation of heterogeneous functional tumor tissue organization. Binary DNA information expressed as proteins/RNA is organized as intracellular networks (protein complexes, regulatory complexes, pathways, etc.) are identified with genomic/proteomic methods and deconvoluted into functional modules. Each cell type within tumors possesses different networks and modules that perform specific tasks, such as, for example, chromosome condensation, mitosis, motility, etc., and in a coordinated manner. Interactions between intracellular or intercellular modules via important hubs (protein nodes in different complexes or networks that interact functionally or physically with multiple other nodes in other complexes/modules) give rise to broad tumor interacting signaling networks. Many oncogenes are hubs and most are developmental genes. It is these tumor-wide interactions that endow tumors with robustness and survivability. The model suggests that identification of tumor-wide networks and the elucidation of intracellular and intercellular hub interactions allow redundancy in communication within the tumor mass and between tumor and adjacent “normal” tissues and could be prime pharmacological targets.

Figure 2

Simplified scheme for conceptual differences of different levels of biological organization in normal and tumor cells. (A) Recapitulation of different levels of organization and function in physiologically constrained cells. (B) In tumor cells, genetic (e.g., DNA mutations, chromosome translocations, etc.) and epigenetic alterations (aberrant methylation of CpG islands, acetylation of histones, etc.) generate altered biomolecules (activated oncogenic proteins, inactivated tumor suppressor proteins, or aberrantly regulated micro RNAs). Altered molecules form pathophysiological complexes that differ in quantitative and qualitative aspects from equivalent complexes in normal cells in composition due to over- or underexpression/inactivation of oncogene/tumor suppression genes, respectively, and are influenced by cell type and signals such as deregulated expression of other oncogenes/tumor suppressor genes, cell–cell interactions, etc. These complexes form “aberrant” signaling and other networks that regulate each other reciprocally and ultimately reorganize modules in novel ways that confer cells with autonomous growth and spread. Oncogene/tumor suppressor proteins may be important hubs in clusters that make multiple (functional and/or physical) connections with nodes in modules that, to a large extent, control tumor cell behavior. It is currently unclear how oncogene/tumor suppressor products and associated proteins in their complexes are organized into networks. However, experimental evidence suggests that they may in fact be organized as critical hubs in clusters between complexes, networks, and modules as (a) some are mutated in most cancers (e.g., Ras and Myc in ~30% and p53 in ~50% of all cancers), (b) their overexpression/mutation affects multiple pathways (and systems/modules) required for cell growth and proliferation, and (c) their effects depend on cell context. For example, their forced expression in normal cells causes growth arrest or apoptosis whereas in transformed cells they favor tumorigenesis, suggesting that their multiprotein complexes differ between normal and tumor cell populations.

Figure 3

Schematic depiction of a eukaryotic cell cycle module with some critical regulators. Cyclin D1/Cdk4,6 complex formation and function depend, among other factors, on the levels of the Cdk inhibitor p27. Surprisingly, at relatively low levels, p27 aids in complex formation and stabilization (corroborated by the fact that p27 haploinsufficiency is found in tumors) whereas at higher levels – as in quiescent cells – it inhibits complex formation. It is unknown how complex formation and function is affected and how the signaling network that controls cell cycle progression is reorganized in terms of the participation of critical hub proteins in the depicted complexes. Thus, when INK genes are mutated, MDM2 and cyclin D1/Cdk4,6/p27 complex composition and function changes but it remains to be determined how their signaling networks are reorganized.

Figure 4

Schematic depiction of c-Myc functional interactions with other pathways (for full discussion, see section Induction–Reversion Tumorigenesis in Animal Models by Oncogene-Driven Cancer Network Formation). Blue arrows and sticks indicate (tumor) growth suppressive, functional interactions, and red tumorigenic interactions. Identifying Myc and other oncogene networks, especially in the context of others such as the p53 or Ras, in tumor samples using multidimentional analysis promises to illuminate how oncogenes and tumor suppressor proteins are functionally networked in tumor cells and, in turn, how these networks determine cell growth autonomy. Key steps include determining which oncogenes or associated proteins in complexes and networks are important hubs or clusters and if so whether perturbing them is pharmaceutically feasible. Note: A pathway is different from a network in that it refers to an experimentally determined series of events that lead to a functional outcome as measured by commonly accepted methods. In contrast, a network is a grid of interactions between components of a system under study and can represent (a) direct structural interactions between proteins in a complex or a cluster, (b) functional gene–gene or genetic protein–protein interactions (obtained from DNA or protein microarray experiments). Genetic protein–protein interactions do not imply direct physical interactions between network-associated proteins.