Identification of critical regulatory genes in cancer signaling network using controllability analysis
Introduction
Cancer is a complex disease which is characterized by subtle interplay of regulatory mechanisms underlying its phenotype [1], [2]. Dysregulation of multiple pathways governing fundamental cell processes (such as death, proliferation, migration and differentiation) is known to be a key cause for emergence of cancer. The crosstalk between signaling pathways reflecting salient aspects of disease have been used to model this pathology [3], [4]. The focus behind building such integrative models has been to create a meaningful molecular picture of cancer so as to find ways for controlling the disease [5], [6], [7].
Systems that exhibit complex phenomena owing to interconnected mechanisms underlying its architecture can be studied using graph theoretical paradigm [8], [9], [10]. Study of such networked systems of social, technological and biological origin has added to the understanding of their structure, function and evolution. Availability of rich data mapping biological processes in the postgenomic era has facilitated creation of molecular interaction catalogues (protein interactomes, gene regulatory networks, metabolic pathways and co-expression networks), and better understanding of cellular functions [11], [12], [13]. Using these graph theoretical approaches, various studies have attempted to identify regulatory mechanisms which are central to the disease [14], [15], [16], [17]. Rapid advances in network biology have provided a new conceptual framework revolutionizing the view of biology and disease pathology.
From control systems perspective, cellular processes can be viewed as an intricately controlled orchestra of regulatory mechanisms that lend the cell its functional repertoire. Diseases, therefore, can be seen as the result of errors in cellular information processing. Beyond systems modeling of diseases, the focus has also been on finding ways of controlling the disease through therapeutic interventions. Recent excitement in controllability of networks and identification of driver nodes as agents for steering the state of the network has added much needed impetus in this direction [18], [19], [20], [21], [22]. Control systems theory proposes that the state of complex networks can be controlled with the help of a set of ‘driver nodes’. Driver nodes may then be fed with external inputs to steer the state of the network.
Analysis of systems biological models of diseases has provided crucial insights into their mechanisms and potential drug targets [4], [23]. The understanding garnered through such studies has often taken a static perspective of disease interactomes, ignoring dynamical aspects. Study of controllability of diseases and search for driver nodes as potential therapeutic targets provides a new dimension. Such integration of control theory with disease interactomes could pave way for better strategies to assist drug discovery process as well as improve our understanding of the disease [24], [25], [26].
Cancer systems biology has proved to be helpful in implementing new therapeutic strategies [5], [27]. While most studies [23], [28], [29] focus on identification of epigenetic changes and mutations in genes, and target them as means of controlling the disease, control theory offers a framework for arriving at driver genes that could be used for steering the state of the regulatory network. We propose that identification of specific molecules as drivers of regulatory dynamics could be a promising step towards targeted cancer therapies.
Here, we modeled the human cancer signaling network as a directed graph and probed for its critical regulators using structural controllability. We implemented the maximum matching algorithm to identify driver nodes. The driver nodes were divided into backbone, peripheral and ordinary based on their role in regulatory interactions and control of the network. Based on node deletion studies that enumerate impact of a node on ease of network control, indispensable nodes were identified. These indispensable backbone driver nodes were found to be critical for driving the regulatory network into cancer phenotype (via mutations) as well as for steering into healthy phenotype (as drug targets). Thus they emerged as central to control, both as causal elements by virtue of mutations and also as therapeutic agents in the form of cancer drug targets. This study illustrates an application of control theory for investigation of regulatory mechanisms underlying complex diseases.
Section snippets
Human cancer signaling network
We created the Human Cancer Signaling Network (HCSN) starting from the data of human signaling network [30] comprising of 1634 genes and 5089 regulatory relations by integrating genetically and epigenetically altered cancer associated genes and signaling pathways for cancer. Thus HCSN embeds molecular correlates associated to cancer. The nodes in HCSN are signaling molecules (such as genes, proteins and other small molecules) and links represent effector actions such as activation or
Topological characterization of HCSN driver nodes
HCSN is a molecular interactome of signaling mechanisms of cancer (Fig. 1). Driver nodes, that facilitate control of the network state, could provide means of controlling the dynamical state of cancer associated signaling network. Using maximum matching algorithm, we identified driver nodes and classified them into PDNs, ODNs and BDNs, depending on their presence in MDNS across multiple sample sets. A total of 584 nodes (out of 1232) were driver nodes with 29.38% PDNs, 31.01% ODNs and 39.61%
Discussion and conclusions
Our study integrates the systems biological approach to cancer regulatory mechanisms with control theory to identify biological implications of ‘driver nodes’. We propose the notion of regulatory network as an underlying molecular framework that is subject to control through indispensable backbone driver nodes. These nodes could either steer the network from healthy state to disorder by means of mutations, or could be leveraged as drug targets for driving the network into healthy state. These
Acknowledgments
VR acknowledges the Inspire Fellowship from Department of Science and Technology (DST/INSPIRE/IF120809). VR, VS and GB acknowledge DA-IICT for providing computational facility. GB also acknowledges support from IIIT-Delhi.
References (33)
- et al.
The hallmarks of cancer
Cell
(2000) - et al.
Hallmarks of cancer: the next generation
Cell
(2011) - et al.
Network medicine
FEBS Lett.
(2008) - et al.
Analysis of cancer signaling networks by systems biology to develop therapies
Sem. Cancer Biol.
(2011) - et al.
Network medicine strikes a blow against breast cancer
Cell
(2012) - et al.
Network medicine: a network-based approach to human disease
Nature Rev. Genet.
(2011) - et al.
Complexity in cancer biology: is systems biology the answer?
Cancer Med.
(2013) - et al.
Statistical mechanics of complex networks
Rev. Modern Phys.
(2002) Networks: An Introduction
(2010)- et al.
Evolution of Networks: From Biological Nets to the Internet and WWW
(2013)