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Cancer Cell & Microenvironment 2015; 2: e943. doi: 10.14800/ccm.943; © 2015 by Enrico Capobianco http://www.smartscitech.com/index.php/ccm RESEARCH HIGHLIGHT Cancer hallmarks through the network lens Enrico Capobianco1, 2 1 Center for Computational Science, University of Miami, Miami, FL, USA 2 LISM-IFC, National Research Council of Italy, Pisa, IT Correspondence: Enrico Capobianco E-mail: [email protected] Received: August 01, 2015 Published online: September 25, 2015 Given the role and relevance of the heterogeneity hallmark in cancer, a current need is to advance the knowledge of the network dynamics building the complex architecture underlying the relationships between cell sub-populations. This step would shed light over obscure mechanisms such as acquired resistance to drugs, for instance. Network medicine has established itself as a systems integrative approach that leverages on the interpretation offered by community-like behavior of bio-entities and clinical variables. Translated over cell sub-population cells, there is a strong case for deciphering cooperative versus antagonist network dynamics in order to determine better therapeutic strategies. Keywords: cancer hallmarks; networks; communities; microenvironment To cite this article: Enrico Capobianco. Cancer hallmarks through the network lens. Can Cell Microenviron 2015; 2: e943. doi: 10.14800/ccm.943. Cancer Hallmarks Cancer hallmarks [1, 2] lead the cancer field, and stimulate advances and discoveries, together with renewed interest and debate over their sufficiency and adequacy. In particular, immune response [3-6], drug resistance [7-9] and complex interactions with micro-environment [10, 11] keep generating influencing ideas, assuming critical roles in oncology research. Computational oncology scientists are heavily engaged in modeling such effort, and particularly through the experimental omics and discovery pipelines. These pipelines are currently more focused on empirical data-driven solution than model-centered ones. While the former follow various methodological directions, especially integrative ones, the latter partially lack insight and depth. Reasons for such limitations are the role of evolving technologies, and the complexity of the data generation processes. In our view, these limitations suggest that there is also margin for improvements, and it is in the networks direction that we look in particular with this work. Networks play a well-recognized integrative inference role in elucidating complex cancer connectivity patterns and cross-links [12]. In principle, and by operating systems-wise, the network inference approaches can adapt to the hallmarks of oncogenic signaling [13-15] and of cell-cell communication [16]. Further specializations may occur by integrating clinical, genetics, omics, and imaging data, in a so-called multiplexed way [17, 18]. Despite all the complexities, an advantage of networks compared to other techniques is that their structures can be naturally simplified by partitioning strategies, from which hierarchies of sub-modules or communities arise and appear associated to characteristics shared by group members. Importance attractors of modularity and relationships with Modular configurations in networks (Figure 1) provide spatiotemporal examination of cellular components, thus linking molecular pathways to phenotypes especially by exploiting similarity metrics that underlie the network topology. Typically, cancer hotspots consider two properties: Page 1 of 5 Cancer Cell & Microenvironment 2015; 2: e943. doi: 10.14800/ccm.943; © 2015 by Enrico Capobianco http://www.smartscitech.com/index.php/ccm Figure 1. Cancer Networks. Adapted from [19], the central rhomboid in this representation involves two properties of cancer hallmarks that might be translated in a network-centric context. Plasticity reflects the capacity of cancer to adapt under the pressure of its inherent heterogeneity. Change in the system calls for resilience, leading to stability at some point in order to permit establishment or re-modulation of phenotypic features. Cancer networks have configurations with several properties, such as: 1) Redundancy, through hierarchized connectivity structure typical of community; 2) Robustness, to allow structural change in response to anomalies or partial failures; 3) Heterogeneity, by embedding functional variety into the degree distribution; 4) Adaptation, through flexibility of topological configurations in response to external stimuli and perturbations. There are corresponding computational strategies that can be enabled to exploit these properties, from model selection and dimensionality reduction to contextual enrichment. Cancer hallmarks represent systems forces whose presence and action requires the network to dynamically evolve. In principle, one could also reverse this view, and consider the host network and its immune mediated modulation. hubbiness and centrality. Networks identify as hub nodes those with relatively high connectivity degree, and as central nodes or links those measuring high load or traffic, therefore indicating relevance. An important premise of many studies is that tumor processes may reveal quite different topological measures from processes in normal conditions. Nevertheless, the possibility of accurately measuring these different features depends on many factors, which in turn suggests that a systems approach is needed. Given the complexity of the model, its reliability is also an element needing careful evaluation. Complexity and reliability both increase with the number of features under consideration and with the type of measurement. Network modules make a lot of sense biologically and clinically as they do not act as separated entities, but cross-linked aggregates. Consequently, differential network configurations should be identified to reveal the diversified propagation laws of the effects that ultimately determine the phenotypes of interest, with reference to cancer hallmarks. This transition involves changes in ‘attractor’ or stable equilibrium states [20-23]. The states in a network are associated to points of an N-dimensional state space, depending on the number of nodes. Also, the modules of a network are associated to ‘state communities’ which are linked, as they assemble fractions of nodes and links in either Page 2 of 5 Cancer Cell & Microenvironment 2015; 2: e943. doi: 10.14800/ccm.943; © 2015 by Enrico Capobianco http://www.smartscitech.com/index.php/ccm transient or persistent communication with each other. Attractors, as stable points in the expression state space, tell where the system would return after small perturbations within a smooth landscape. The impacts of external cues and also internal perturbation of microenvironment origin should be accounted for. Consequently, the treatment at systems scale of phenotypic response patterns would be recommended. Such non-stationary patterns can disrupt cancer network attractors, when they are of systemic nature. Accordingly, these dynamics drive the network state transitions through adaptive mechanisms controlling and modulating cascades of events, including compensatory effects at the pathway level. For instance, critical aspects of sensitivity and resistance mechanisms are emerging in clinical work. Inference on signaling states across tumor phenotypes may follow perturbation experiments having key modules as targets, and/or considering component nodes/edges (i.e. edgetic perturbations [24]). From the observed network configuration changes, the dynamics of complex signaling pathways can be inferred, for instance with regard to the effects of their inhibition with regard to key mechanisms such as drug resistance. Endogenous and exogenous network dynamics The complexity of signaling networks (cell cycle phases, apoptosis, etc.) is due to non-linearly convoluted dynamics. These latter depend on both the level and the activation state of proteins which exert time-course effects on multiple key pathways. Organizing the signaling data in a mathematical framework is a complex task. Computational data-driven and exploratory tools, as said before, remain the main proposal currently available and preferred in general to theoretical models. Intuitively, each hallmark refers to potential barriers that a neoplasia must overcome in order to survive and proliferate. Of a particular relevance from a clinical perspective are angiogenesis, and tissue invasion and/or metastasis formation. Assessment of angiogenesis requires the consideration of a spectrum of localized scales co-participating in measuring the influence exerted by the cell states. Typically, while models would hold for the entire network, data may refer to just sampled sub-networks. Whole network parameter estimates obtained at sub-network scale work under the assumption of model consistency under sampling. A general way to reduce the typical complexity of high-dimensional networks is to turn to ensemble models averaging out possible erratic aspects. Invasion and metastasis determine patient survival, and require the acquisition of specific phenotypes for the cells to be able to detach from the tumor mass, migrate and survive in a different tissue. It is now recognized [25] that the development of tumor networks depends on the interaction of cancer cells with the surrounding microenvironment. Further studies [26] of tumor microenvironment have revealed the role of some specific mechanism, for instance those involved in tissue infiltration and metastasis formation. These examples recall both epithelial-to-mesenchymal transition (EMT) and degradation of the extracellular matrix (ECM), responsible for the cell morphology change that enables the detachment from the primary tumor mass and the migration through the extracellular space, with the aim of invading the surrounding tissue. Such detached cells can reach lymphatic or blood vessels, thus becoming circulating tumor cells and possibly forming tumor colonies (metastases) in other body parts [27]. Networks offer a unique opportunity to characterize tumor phenotypes [28]. In particular, modularity suggests the relevance of novel strategies addressing marker panels. This choice in turn is almost necessary in view of combinatorial treatments. Due to the complexity of heterogeneous proliferative mechanisms, multi-marker signatures are expected to improve prediction power over the singlemarker signatures, thus delivering network fingerprints. Basically, first network profiles are built from modular configurations, and on the basis of specific hallmarks or phenotypes. Then, the fingerprint makes a synthesis of such profiles, building an ensemble of them. Such fingerprint is strictly correlated with the tumor stage, and thus with the hallmarks. Perspectives Substantial intra-tumor heterogeneity can be interpreted by metrics underlying the network topology. As a matter of fact, there is the possibility that multiple mechanisms of drug resistance or sensitivity co-act within the same tumor, explaining the response to therapy or the deviations in tumor evolution. Therefore, clonal sub-populations of cancer cells may be able to join forces or modularize genotypically and phenotypically within the same cancer. When such hierarchies form to characterize intra-tumor heterogeneity, consequences are expected in terms of metastasis. Modular networks naturally match sub-structures induced by special signatures. The process of growth in cancer is highly complex and dynamic. One aspect that has been established is that a variety of contextual cells (fibroblasts, leukocytes, endothelial, extra-cellular matrix etc.) are activated, with the context usually defined as tumor. Recently, stromal therapeutic agents have been pursued due to the increasingly Page 3 of 5 Cancer Cell & Microenvironment 2015; 2: e943. doi: 10.14800/ccm.943; © 2015 by Enrico Capobianco http://www.smartscitech.com/index.php/ccm important role played by stroma (macrophages, chemokines, cytokines, etc.) for anticancer drug activity and chemoresistance. A few questions arise: how therapies affect the tumor microenvironment? How changes in it can be exploited for improving treatment? What’s the role of the various cell sub-populations? Conceiving the tumor microenvironment as a dynamic multi-type cellular network linking clonal sub-populations with stromal cells and immune cells, can suggest novel computational models [28]. The resulting interactome is expected to be heterogeneous and multiscale, thus requiring integrative approaches to describe intra- and inter-cellular interactions. So-called community effects should be taken into account, as cell sub-populations may underlie tumorigenic or progressive growth differently, in part facilitated by specialized tasks and roles and in part engaged in different dynamics with regard to phenotypic characterization. Changes in cell state can be triggered exogeneously, i.e. due to microenvironmental signals, thus in addition to both the specific intrinsic variability in gene expression and the general patterns observed in view of cancer plasticity [29]. The latter concept is linked to heterogeneity, implying the fact that cell sub-populations affect in a distinct way cancer phenotypes (growth, differentiation, and apoptosis). It can be hypothesized that during cancer development individual cells acquire various states in dependence on the microenvironment, thus representing a primary source of variation. Therefore, an interesting question is deciphering the regulatory mechanisms through which cells can compromise between stability (enabling functional differentiation) and plasticity (allowing state transitions), influencing metastasis. Cell sub-populations vary in growth rate, immunogenicity, metastatic power and drug response. From such diversity, both functional and phenotypic heterogeneity can be explained, but only rationally and not yet dynamically through models. Basically, interference, competition and synergisms are all possible dynamics between clonal populations, suggesting a community-like behavior [30]. References 1. 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