Cancer is a collective term for a number of multi-factorial and heterogeneous diseases characterized by uncontrolled cellular growth. In the multi-step process, normal cells are initiated and transition through hyperplasia, different degrees of dysplasia and carcinoma in situ and eventually become invasive to adjacent tissue and metastasize to other organs and tissues. Cancer prevention aims to disrupt oncogenesis by chemical, biological, or nutritional intervention and thereby prevent, reverse or delay the development or recurrence of cancer. Primary prevention aims to block initiation and the secondary prevention strives to delay or reverse promotion or progression of carcinogenesis.
Biological systems (organism, organ, tissue, cellular, subcellular, molecular systems) are comprised of multiple interactive complex networks with redundant, convergent and divergent signaling pathways including numerous positive and negative feedback loops. They may be represented by abstract biological networks which aim to depict the essential elements and activities of the former via integrative and dynamic simulations. Systems biology represents an integrated approach to understand functions of biological systems and effects of perturbations on them.
Attempts to inject systemic views into biology have a long history. For example, an interesting perspective entitled “The Systems View of Man: Implications for Medicine, Science, and Ethics” has been published as early as in 1973 [1
]. From a biological standpoint, systems biology is the large-scale dynamic study of functional and physical relationships between the molecules that make up life. This includes interactions within cells, between cells and between cells and their environments [2
]. Systems biology aims to understand and describe complex biological systems and develop predictive models for physiological and pathological processes and apply them to control of disease states such as carcinogenesis. Four distinct aspects (system structure, system dynamics, system controls, and system design to introduce desired modifications) are considered in applying system biology approach to biological systems [6
]. It is necessary to understand the functionality of interconnected complex biological networks in order to effectively devise appropriate cancer preventive measures and avoid any unwanted side effects. Numerous dynamic biological processes ranging from milliseconds (conformational changes) to minutes (post-translational protein changes) to hours and days (gene expression) and years (epigenetic control), maintain biological systems in certain quasi-equilibrium states. Application of engineering tools and concepts (e.g. networks, robustness, modularity, stochasticity, etc.) to biological studies is gaining increasing popularity and showing promise [6
]. For example, integrative systems level approach has considerably increased the understanding of the EGFR signaling pathway, one of the most studied pathways [9
]. However, it is also important to go beyond the cell and employ a more holistic approach in terms of the entire organism. For example, a role of cell-cell interaction/communication, locally and distally, has been implicated in carcinogenesis [10
] and is based on earlier made observations [12
Biological systems in general and cancer in particular exhibit inherent resistance against internal and external perturbations, a characteristic termed robustness [9
]. Robustness differs from principles of stability and homeostasis in that it deals with maintaining system function as opposed to system states. This trait is ubiquitous in nature and largely due to extensive built-in redundancies (fail-safe mechanisms relying on alternative components or functionalities to maintain the system function), modularity (isolation of perturbation of one component on the whole system), decoupling (buffering of noise and fluctuations) and system controls via feedback loops (negative, positive, feed-forward) [6
]. However, it should be kept in mind that there is always a trade-off among robustness, fragility, resource demands and performance [6
]. Kitano had proposed that cancer may be viewed as a breakdown of normal physiological robustness and change to pathological state that develops its own robustness in addition to using the host’s robustness [6
]. He had examined the theory of biological robustness in relation to cancer, inhibition of carcinogenesis, and drug design [6
] and proposed a need for cancer robustness theory motivated approach to cancer prevention and therapy [6
One of the re-emergent theories of carcinogenesis involves cancer stem cells (see, http://dcp.cancer.gov/newsandevents/eventsarchive/20070514-15
].) Cancer stem cells represent a small subpopulation of cancer cells within a tumor and are characterized by their ability for self-renewal and pluri-potency. These cells are resistant to common intervention treatments and are thought to be responsible for cancer formation and growth, relapse and different stages of carcinogenesis. The microenvironment also plays an important bidirectional role [19
Cancer is a dynamic multi-step, multi-mechanism disease involving complex interactive and redundant pathways, e.g. upregulation of survival pathways (e.g. bcl2, NFkB, AKT and receptor kinases) and genetic and epigenetic changes in relevant targets during cancer progression [20
]. Consequently, it is important to understand the dynamic progression of cancer and apply appropriate preventive interventions accordingly. Heterogeneity, robustness, system dynamics and importance of different molecular targets changes during the carcinogenesis process greatly limit usefulness of a single “magic bullet” approach to intervention. There is a growing movement from a single target drug to multi-target drug paradigm [21
] due to recognition that an alteration of a single target may be inadequate to produce desired biological effects. Instead, a partial modification of several targets may be more effective than a complete inhibition of a single target based on network models. Recently, an importance of targeting entire pathways has been strongly emphasized in [22
]. These authors conclude their work by the following notable statement [22
]: “In addition to yielding insights into tumor pathogenesis, such studies provide the data required for personalized cancer medicine. Unlike certain forms of leukemia, in which tumorigenesis appears to be driven by a single, targetable oncogene, pancreatic cancers result from genetic alterations of a large number of genes that function through a relatively small number of pathways and processes. Our studies suggest that the best hope for therapeutic development may lie in the discovery of agents that target the physiologic effects of the altered pathways and processes rather than their individual gene components. Thus, rather than seeking agents that target specific mutated genes, agents that broadly target downstream mediators or key nodal points may be preferable. Pathways that could be targeted include those causing metabolic disturbances, neoangiogenesis, misexpression of cell surface proteins, alterations of the cell cycle, cytoskeletal abnormalities, and an impaired ability to repair genomic damage.” Vogelstein had further elaborated (www.bio-itworld.com/pb/2008/09/25/gbm-vogelstein.html
): “By targeting the pathways, it’s possible new drugs could be effective against a much greater fraction of tumors. This is a very different perspective from what’s now operative in the drug development community”. In line with these ideas, it has been proposed in [21
] that low affinity, multi-target drugs, representing weak links in cellular networks, may have a greater tendency to stabilize complex networks. Lack of effectiveness or presence of undesirable side effects have been ascribed to emphasis on drugs against a single target [15
]. Single target interventions ignore redundancy, cross-talk, heterogeneity and pleiotropy. For example, selective inhibition of oncogenic AKT could have detrimental effects on glycogen metabolism which could be avoided by multi-component intervention downstream instead [3
]. Another example of oncogenic pathway redundancy and crosstalk involves TGF-β [3
]. Inhibition of TGF receptor would inhibit growth promoting SMAD2 and SMAD3, but would also activate oncogenic MAPK signaling. Inhibition of either SMAD2 or SMAD3 would likely lead to compensatory upregulation of its redundant counterpart. Many drugs have multiple targets and rational design of multi-target drugs will require much more temporo-spatial information about metabolic pathways, receptor signaling and signal transduction. While the reductionist approach has provided valuable information on individual molecular targets and their function, additional knowledge on spatial and temporal dynamic characteristics and complex interconnections in biological systems are needed for understanding and modulation of biological processes [8
]. In fact, spatial and temporal dynamics of downstream signaling pathways may determine the specificity and nature of biological response [26
]. Therefore, it is expected that application of systems biology to cancer prevention in terms of time dependent drug target selection should improve efficacy and decrease toxicity of preventive interventions. Drug combinations are common in antibacterial and cancer chemotherapy and traditional medicine. In fact, many drugs exhibit biological effects via multiple simultaneous activities at different targets [27
]. Cancer prevention, like prevention of other complex diseases, would benefit from combination therapy based on dynamic systems biology approach as opposed to isolated, static view of the disease. There is a need to avoid reductionism and consider the entire biological system. It has been proposed that control of cellular dynamics may be more effective against cancer than that of its components. Therefore, a need for systems biology approach to improved understanding and control of disease progression and multicomponent intervention in network systems in general and cancer and cancer prevention in particular is obvious. Application of systems biology to complex biological systems is in its infancy but the need and rewards are great.
The most concise definition of the term “systems biology” is that systems biology is the theory of systems applied to biology. Theory of Systems, as a separate discipline with its own methodology, philosophy, mathematical instrumentation and fields of applications, has existed for almost a century. It is an interdisciplinary field of science which studies complex systems in nature and society such as an organism, organization, mechanism or informational network. Theory of Systems stems from the Bogdanov’s “Tectology” [28
] and Bertalanffy’s “General System Theory” [29
]. The General System Theory (GST) is widely regarded as an alternative view to that based on fundamental, often called first
, principles of natural sciences. As such, the GST has introduced a number of new concepts and categories not reducible to those of physics, chemistry or biology. Among them are the concepts of complexity, adaptation, evolution, robustness, self-organization, catastrophes, chaos, criticality and numerous others. The GST stimulated development of a number of mathematical disciplines with central role of the concept of network
and deep connections to graph theory and algebraic geometry.
By definition, a complex system is composed of interconnected parts that as a whole exhibits properties not obvious from the properties of the individual parts. Examples of complex systems include socio-economic structures, language, crowd psychology, termite colonies, biochemical networks, organizational culture, nervous system, social networks, cells and living things, internet, terrorist movements, energy infrastructure, traffic patterns, etc.
A number of prominent organizations in the U.S.A. and around the world are engaged in research and consulting pertaining to GST. Among them are the Santa Fe Institute (www.santafe.edu
), RAND Corporation (www.rand.org
), Center for the Study of Complex Systems (University of Michigan, www.cscs.umich.edu
), Northwestern Institute on Complex Systems (www.northwestern.edu/nico
), New England Complex Systems Institute (www.necsi.org
), Department of Complexity Science and Engineering (University of Tokyo, www.k.u-tokyo.ac.jp/complex
), Institute for Quantitative Social Science (Harvard University, www.iq.harvard.edu
), and other.
The Living Systems
] is an outgrowth of GST intended to formalize the concept of life. The discipline of Systems Biology
is an aspect of the Living Systems theory which intends to integrate
an ever growing body of knowledge about individual processes on all the levels of living systems using the conceptual frameworks of GST. There are (at least) three salient concepts crucial for understanding complex biological systems in addition to those existing in the GST. These are emergence, robustness and modularity
]. The concept of emergence
means that complex systems display properties that are not demonstrated by their individual parts and cannot be predicted even with full understanding of these parts alone. Comprehensive understanding of such emergent properties requires the system-level conceptualization and cannot be derived from the reductionist perspective focused on the system’s components. Robustness
is an inherent property of all biological systems and consists in their ability to maintain functional stability in the presence of adverse influences imposed by the environment. Robustness is manifested through the feedback loops and other forms of self-control. A module
is a functional unit possessing certain intrinsic properties regardless of its interactions with the external world. In biology, a module consists of the subunits that have strong mutual interactions and participate in common function. Modularity
provides robustness to the system by confining the damage to a single part and preventing its spread throughout the system.
A wealth of information has been accumulated in the twentieth century regarding the individual cellular components and their functions. On top of the knowledge inherited from the past, an explosive influx of new data is currently emerging due to high-throughput technologies such as microarrays and protein mass-spectrometry. With such abundance of information, it becomes increasingly clear that complex biological functions cannot be generally attributed to individual molecules or molecular complexes such as DNA, mRNA or proteins. A key challenge for modern biology is to put forward an integrated approach capable of envisioning the system’s functionality from the properties of the individual parts of which it consists [8
The scope of work in systems biology is enormous. Scientific journals with the key words “systems biology” in the title are numbered in dozens. A substantial fraction of the publications is devoted directly or indirectly to the systems biology of cancer. The discipline of Mathematical Oncology
has emerged which attempts to integrate the gigantic and ever-growing body of knowledge on individual processes of tumor onset and proliferation with large-scale data mining, mathematical modeling and high-performance computing. Increasingly, cancer is seen as a “systems biology disease” [32
] as it has become obvious that there is literally no hope to defeat cancer by mere isolating individual “targets” and inventing strategies for their modification. Development of a systemic view, however difficult, is in fact the only way to proceed. It is stated in [33
]: “While the amount of gene expression data has explosively grown in recent years, an integrated theory of gene expression and regulatory network is not yet available. This divergence is the major bottleneck for making a progress in understanding biological systems.” This opinion is echoed by the NCI Strategic Plan, Nation’s Investment in Cancer Research
). It states “integration of experimental biology with mathematical modeling will provide new insights in the biology and new approaches to the management of cancer.”
Against the backdrop of such monumental efforts in systems biology in general, and in the systems biology of cancer in particular, it seems almost surreal, if not regrettable, how small is still the role that applications of systems biology play in cancer prevention
. It is noted in [34
]: “Remarkably, despite the wealth of information, clinical oncologists and tumor biologists possess virtually no comprehensive theoretical model to serve as a framework for understanding, organizing and applying these data. Heeding lessons from the physical sciences, one might expect to find oncology aggressively, almost desperately, pursuing quantitative methods to consolidate its vast body of data and integrate the rapidly accumulating new information. In fact, quite the contrary situation exists.” It is not to say that there is lack of proposals in the literature to use various systemic approaches for identifying therapeutic and chemo-preventive targets (mostly based on experimentation with animal models and in-vitro human cell lines) [35
]. Rather, that means that no integrated approach yet exists that would summarize existing consensus knowledge for application and decision making in the domain of cancer prevention. There are many reasons behind such a situation, not only purely scientific but also logistical, historical, cultural and socio-economic. It is much easier, however, to express frustration regarding the status quo rather than to propose a workable approach that would be both realistic in terms of available resources and capable of producing a noticeable impact in the near future. This paper is intended to provide a view on how to initiate a major effort to activate the role of systems biology in cancer prevention.