G Model
ARTICLE IN PRESS
YSCBI 1150 1–3
Seminars in Cancer Biology xxx (2014) xxx–xxx
Contents lists available at ScienceDirect
Seminars in Cancer Biology journal homepage: www.elsevier.com/locate/semcancer
1
2
3
Editorial
Cancer modeling and network biology: Accelerating toward personalized medicine
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5 12
a r t i c l e
i n f o
a b s t r a c t
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11
Keywords: Cancer modeling Network reconstruction Personalized medicine Multiscale model
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1. Systems biology and personalized medicine
7 8 9 10
14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
The complexity of cancer progression can manifests itself on at least three scales that can be described using mathematical models, namely microscopic, mesoscopic and macroscopic scales. Multiscale cancer models have proven to be advantageous in this context because they can simultaneously incorporate the many different characteristics and scales of complex diseases such as cancer. This has driven the expansion of more predictive data-driven models, coupled to experimental and clinical data. These models are defining the foundations that facilitate the forthcoming design of patient specific cancer therapy. This should be considered as a great leap toward the era of personalized medicine. Consequently, further improvements in mathematical modeling of cancer will lead to the design of more sophisticated cancer therapy approaches. © 2014 Published by Elsevier Ltd.
Knowing that cancer is a complex disease characterized by many different traits, with the potential to develop in various tissues, and that it follows a common strategy of progression [1], makes it ideal target for various modeling approaches on different time and space scales incorporating both extra- and intracellular factors. The validation of these models and predictive simulations of cancer are largely dependent on the mutual cooperation between theoretical and experimental scientists as well as clinical data [2]. Importantly, such validated models of cancer can drive the design of more sophisticated cancer therapy approaches. In addition, the more accurate the raw data, i.e. transcriptional and/or translational, the more precise and predictive the corresponding models will be. From a modeling perspective, there are different levels of abstraction and simplification that can be used to describe the complexity of cancer, which should be tied to the resolution of the data being used. One should pay much attention to ensure that the formalism along with the level of abstraction and simplification are chosen such that the least amount of crucial data are lost. Multiscale cancer models have been shown to be preferable in that they can simultaneously incorporate many processes and bridge multiple scales that are relevant in cancer. This approach, in addition to its experimental and clinical trial achievements, can provide us with the underpinnings that facilitate the future design of patient specific cancer therapy, which should be seen as a great leap toward entering the era of personalized medicine. In cancer research, high-throughput technology has generated enormous quantities of data, comprising genome sequences, SNPs and microarray gene expression, etc. [3]. The masses of data
generated by high throughput technologies are onerous to manage, visualize, and convert to useful knowledge required to render more accurate patient outcomes. High-throughput in vitro proliferation/viability drug screening assays have been constructed to facilitate a comprehensive evaluation of the antitumor efficacy of drugs across multiple cell lines that are representative of different cancer types, specific subtypes of certain cancers, and of the various genomic, transcriptomic and proteomic aberrations that are present in cancer [4]. Systems biology signifies the integration of theoretical and computational modeling, biological experimentation and large-scale data analysis. A systems biology approach to utilize new high throughput technologies will be required to efficiently fulfill the promise of personalized molecular medicine [5]. Systems biology incorporates engineering and mathematical methods with biological and medical methodology to conceptualize the interrelated events within the cell, tissue and micro-environment. The examination of gene expression using genomic and computational approaches and identification of sequence motifs can considerably help cancer researchers and medical practitioners [6]. Clinical systems biology has been brought into play to assist with early cancer detection and management, risk reduction, risk identification and cancer prevention. In systems biology, experimental studies have generated the high-throughput quantitative data needed to assist simulation-based clinical research. Combined with rapidly advancing genome and proteome projects, the data explosion has convinced most researchers that a system-level approach is of considerable importance. At the same time, striking improvements in computational efficiency in clinical systems biology have eased the creation and analysis of sensibly realistic yet intricate biological models [7].
http://dx.doi.org/10.1016/j.semcancer.2014.06.005 1044-579X/© 2014 Published by Elsevier Ltd.
Please cite this article in press as: Masoudi-Nejad A, Wang E. Cancer modeling and network biology: Accelerating toward personalized medicine. Semin Cancer Biol (2014), http://dx.doi.org/10.1016/j.semcancer.2014.06.005
42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71
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ARTICLE IN PRESS Editorial / Seminars in Cancer Biology xxx (2014) xxx–xxx
2. Importance of cancer modeling and network biology Tumors are remarkable instances of complex systems that can experience self-organization. Because of their inherent complexity, it is essential to analyze their growth on different scales. It includes a number of phenomena that take place over a variety of spatial scales ranging from tissue to molecular length scales. The timescales go from seconds for signaling to years for prostate cancer progression. The complexity of cancer development manifests itself on at least three scales that can be distinguished and described using mathematical models, namely, microscopic, mesoscopic and macroscopic scales. Masudi-Nejad et al. [8] have compared the modeling formalisms and tried to address their applicability to rapidly evolving cancer modeling and simulation approaches. Different scales of cancer modeling, i.e. microscopic, mesoscopic and macroscopic scales are described, followed by an illustration of angiogenesis on the microscopic scale of the cancer modeling. Then, modeling of cancer cell proliferation and survival are observed on a microscopic scale and modeling of multiscale tumor growth is explained in addition to its advantages. They have tried to explain how macroscopic, mesoscopic and microscopic scales are interrelated because tumor growth is dependent on e.g. cell population density, nutrient concentration and chemical factors, cell–cell communication, cell behavior, intracellular mechanisms, pressure, each one modeled on a specific scale. These factors reveal that the three scales are related and inevitably woven together. Powathila et al. [9] have investigated a multiscale computational mathematical model for cancer growth and spread, integrating the multiple effects of radiation therapy and chemotherapy into patient survival probability. The model was implemented using two different cell based modeling techniques. Their focus is on two recent multiscale models of a solid tumor undergoing radioand chemotherapy treatments and they have determined how the computational simulation results can be applied to optimize treatment regimes. They showed that the strategies provided by such multiscale modeling approaches could ultimately guide us to optimal patient-specific multi-modality treatment strategies that may increase the patients’ quality of life. Multi-agent systems are capable of handling the complexity of solutions through modeling, decomposition and managing the interconnections between the corresponding components [10]. Agent-based modeling (ABM) is a particular discrete-based hybrid modeling approach that empowers us to simulate the role of diversity in cell populations in addition to within each individual cell; therefore, it has become a powerful modeling method widely used by computational cancer researchers. Wang et al. [11] introduced some of the emerging ABMs that have provided insight into the understanding of cancer growth and metastasis, extending over various biological scales in time and space, and they described several experimentally testable hypotheses generated by those models. Ultimately, they discuss a number of the current challenges of multiscale agent-based cancer models. Late phase cancer cells may shift back to an early phase of organismal development [12]. Therefore, tumor cell populations could be designated by reversible switches between plastic and rigid network states. Cancer stem cell networks may change between plastic and rigid states very intensively. The switching of network plasticity and rigidity may explain central principles in both cancer development and cancer stem cell behavior. Late stage tumor cells can refer to on the one hand late stage primary tumor cells, but on the other hand it may denote metastatic cells, which have already settled in their novel tissue environment. Csermely et al. [13] have demonstrated how environmental variations increase the adaptation potential of cancer cells triggering the bypass of cellular senescence and the development of cancer stem cells. Also, they have proposed that the elevated evolvability
of cancer stem cells is assisted by their repeated shifts between plastic (proliferative, symmetrically dividing) and rigid (quiescent, asymmetrically dividing, often more invasive) phenotypes that have plastic and rigid networks. They explained network models potentially describing cancer stem cell-like behavior. A number of hypothetical network behaviors were portrayed in this review, which may explain the tremendously high evolvability of cancer stem cells. In this regard, network rigidity/plasticity markers may aid in the development of novel biomarkers of cancer stem cells. Gerlee et al. [14] have reviewed the use of neural networks as models to depict the relationship between the micro-environment, genotype and cellular phenotype in tumor cells. They have concentrated specifically on individual-based models, which use neural networks as a means of connecting such different biological scales of tumor growth. The neural network methodology has the superiority over others in that it has the capability of capturing dynamics on two different time scales. The first one is pathway activity, with gene activity being influenced by micro-environmental factors and the states of other genes. The second one is the time scale of evolutionary changes, where the expressed phenotypes are selected for and the corresponding subclones may alter the microenvironment in a feedback loop. This makes such models appropriate tools for investigating and interrogating the evolutionary dynamics of tumor growth. They discuss the abundant spatial heterogeneity in cancer, highlighting the role of context in driving selection, as well as the role of the tumor in producing context, a key idea that is becoming progressively important. In the review by Bolouri H. [15], two critical emerging trends have been reviewed and suggested. First, the author has reviewed the discussions indicating that the events within the tumor microenvironment (TME) can trigger fate determining consequences including growth advantages, resistance to interventions and remaining dormant for long times. The roles of computational, mathematical and statistical tools in studying the TME are emphasized. Also, some exemplars of TME modeling are discussed herein. Second, a trend toward data-rich, molecularly detailed computational models is highlighted. Regarding the multifactorial nature of cancer, revealing its metabolic alterations and evaluating their implications is a major challenge in biomedical sciences, which if properly understood will aid in the optimal design of personalized treatments. The most studied metabolic modification in cancer is the degradation of glucose via aerobic glycolysis, a less efficient pathway for generating ATP compared with oxidative phosphorylation, which occurs even in the presence of oxygen. This finding was reported almost a century ago by Otto Warburg [16] in his seminal investigations of metabolism in cancer cells. Remarkably, studies in cancer biology have proved that cancer genes are intimately connected to the Warburg effect and other metabolic modifications [17]. Masoudi-Nejad et al. [18] have provided a manuscript in which the recent achievements in structural-based analysis of cancer from a metabolic disease point of view have been reviewed. The metabolic function of a cancer cell, called the Warburg effect, has come into the limelight in recent years and structural-based analysis has been shown to be appropriate for developing diagnostic and prognostic molecular signatures, as well as for finding new drug targets. Two separate structural methods are considered here: topological and constraint-based methods. They highlighted the fact that although structural perspective of cancer cellular metabolism may introduce new opportunities for disease treatment, crucial challenges such as data integration platforms, discovery of details about mutations and enzyme regulation, and expanding models to explain the other hallmarks of cancer remain for further investigations. ResendisAntonio et al. [19] have concentrated on suggesting a common view of some of these approaches whose application and integration will be pivotal in the move from local to global conclusions in cancer research as a metabolic disease. They have tried to describe
Please cite this article in press as: Masoudi-Nejad A, Wang E. Cancer modeling and network biology: Accelerating toward personalized medicine. Semin Cancer Biol (2014), http://dx.doi.org/10.1016/j.semcancer.2014.06.005
137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202
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ARTICLE IN PRESS Editorial / Seminars in Cancer Biology xxx (2014) xxx–xxx
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the importance of constraint-based modeling and multidisciplinary methods in cancer systems biology. Wang et al. [20] discussed a system framework in which interplay of cancer hallmark networks has been proposed based on the evolutionary dynamics of cancer. New concepts such as the gene regulatory phenotype and the network operational signature have been proposed in the framework in order to construct predictive models for personalized drug targets, drug resistance and metastasis of cancer patients using genome sequencing data.
212
Conflicts of interest
203 204 205 206 207 208 209 210
213
214
The authors declare no conflicts of interest. Acknowledgements
221
Authors thank Eva Klein, the former Editor-in-Chief of Seminars in Cancer Biology, for initial acceptance of this special issue, also full support and encouragement of current Editor-in-Chief of the journal, Theresa Vincent for compilation of this exciting set of papers. We appreciate great help of Alexander Anderson for editing this manuscript. Last but not least, we thank the authors of this special issue for their excellent and timely contributions and support.
222
References
215 216 217 218 219 220
223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 Q3 240 241
[1] Martins ML, Ferreira Jr SC, Vilela MJ. Multiscale models for the growth of avascular tumors. Phys Life Rev 2007;4:128–56. [2] Gibbs JB. Mechanism-based target identification and drug discovery in cancer research. Science 2000;287:1969–73. [3] Chang PL. Clinical bioinformatics. Chang Gung Med J 2005;28:201–11. [4] Monks A, Scudiero D, Skehan P, Shoemaker R, Paull K, Vistica D, et al. Feasibility of a high-flux anticancer drug screen using a diverse panel of cultured human tumor cell lines. J Natl Cancer Inst 1991;83:757–66. [5] Gonzalez-Angulo AM, Hennessy BT, Mills GB. Future of personalized medicine in oncology: a systems biology approach. J Clin Oncol 2010;28:2777–83. [6] Gonzalez-Perez A, Mustonen V, Reva B, Ritchie GR, Creixell P, Karchin R, et al. Computational approaches to identify functional genetic variants in cancer genomes. Nat Methods 2013;10:723–9. [7] Kitano H. Computational systems biology. Nature 2002;420:206–10. [8] Masoudi-Nejad A, Bidkhori G, Hosseini Ashtiani S, Najafi A, Bozorgmehr JH, Wang E. Cancer systems biology and modeling: microscopic scale and multiscale approaches. Semin Cancer Biol 2014. [9] Powathil GG, Swat M, Chaplain MA. Systems oncology: towards patient-specific treatment regimes informed by multiscale mathematical modelling. Semin Cancer Biol 2014.
3
[10] Najafi A, Bidkhori G, Bozorgmehr JH, Koch I, Masoudi-Nejad A. Genome scale modeling in systems biology: algorithms and resources. Curr Genomics 2014;15:130–59. [11] Wang Z, Butner JD, Kerketta R, Cristini V, Deisboeck TS. Simulating cancer growth with multiscale agent-based modeling. Semin Cancer Biol 2014. [12] Meacham CE, Morrison SJ. Tumour heterogeneity and cancer cell plasticity. Nature 2013;501:328–37. [13] Csermely P, Hodsagi J, Korcsmaros T, Modos D, Perez-Lopez AR, Szalay K, et al. Cancer stem cells display extremely large evolvability: alternating plastic and rigid networks as a potential mechanism: network models, novel therapeutic target strategies, and the contributions of hypoxia, inflammation and cellular senescence. Semin Cancer Biol 2014. [14] Gerlee P, Kim E, Anderson AR. Bridging scales in cancer progression: mapping genotype to phenotype using neural networks. Semin Cancer Biol 2014. [15] Bolouri H. Network dynamics in the tumor microenvironment. Semin Cancer Biol 2014. [16] Warburg O. On the origin of cancer cells. Science 1956;123:309–14. [17] Hsu PP, Sabatini DM. Cancer cell metabolism: Warburg and beyond. Cell 2008;134:703–7. [18] Masoudi-Nejad A, Asgari Y. Metabolic cancer biology: structural-based analysis of cancer as a metabolic disease, new sights and opportunities for disease treatment. Semin Cancer Biol 2014. [19] Resendis-Antonio O, Gonzalez-Torres C, Jaime-Munoz G, Hernandez-Patino CE, Salgado-Munoz CF. Modeling metabolism: a window toward a comprehensive interpretation of networks in cancer. Semin Cancer Biol 2014. [20] Wang E, Zaman N, McGee S, Milanese JS, Masoudi-Nejad A, O’Connor-McCourt M. Predictive genomics: a cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data. Semin Cancer Biol 2014.
Ali Masoudi-Nejad ∗ Q1 Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran Wang a,b,∗∗
a
Edwin National Research Council Canada, Montreal, QC H4P 2R2, Canada b Center for Bioinformatics, McGill University, Montreal, QC H3G 0B1, Canada ∗ Corresponding
author.
∗∗ Corresponding
author at: National Research Council Canada, Montreal, QC H4P 2R2, Canada. Q2 E-mail addresses: [email protected] (A. Masoudi-Nejad), [email protected] (E. Wang).
Please cite this article in press as: Masoudi-Nejad A, Wang E. Cancer modeling and network biology: Accelerating toward personalized medicine. Semin Cancer Biol (2014), http://dx.doi.org/10.1016/j.semcancer.2014.06.005
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ARTICLE IN PRESS
YSCBI 1150 1–3
Seminars in Cancer Biology xxx (2014) xxx–xxx
Contents lists available at ScienceDirect
Seminars in Cancer Biology journal homepage: www.elsevier.com/locate/semcancer
1
2
3
Editorial
Cancer modeling and network biology: Accelerating toward personalized medicine
4
5 12
a r t i c l e
i n f o
a b s t r a c t
6
11
Keywords: Cancer modeling Network reconstruction Personalized medicine Multiscale model
13
1. Systems biology and personalized medicine
7 8 9 10
14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
The complexity of cancer progression can manifests itself on at least three scales that can be described using mathematical models, namely microscopic, mesoscopic and macroscopic scales. Multiscale cancer models have proven to be advantageous in this context because they can simultaneously incorporate the many different characteristics and scales of complex diseases such as cancer. This has driven the expansion of more predictive data-driven models, coupled to experimental and clinical data. These models are defining the foundations that facilitate the forthcoming design of patient specific cancer therapy. This should be considered as a great leap toward the era of personalized medicine. Consequently, further improvements in mathematical modeling of cancer will lead to the design of more sophisticated cancer therapy approaches. © 2014 Published by Elsevier Ltd.
Knowing that cancer is a complex disease characterized by many different traits, with the potential to develop in various tissues, and that it follows a common strategy of progression [1], makes it ideal target for various modeling approaches on different time and space scales incorporating both extra- and intracellular factors. The validation of these models and predictive simulations of cancer are largely dependent on the mutual cooperation between theoretical and experimental scientists as well as clinical data [2]. Importantly, such validated models of cancer can drive the design of more sophisticated cancer therapy approaches. In addition, the more accurate the raw data, i.e. transcriptional and/or translational, the more precise and predictive the corresponding models will be. From a modeling perspective, there are different levels of abstraction and simplification that can be used to describe the complexity of cancer, which should be tied to the resolution of the data being used. One should pay much attention to ensure that the formalism along with the level of abstraction and simplification are chosen such that the least amount of crucial data are lost. Multiscale cancer models have been shown to be preferable in that they can simultaneously incorporate many processes and bridge multiple scales that are relevant in cancer. This approach, in addition to its experimental and clinical trial achievements, can provide us with the underpinnings that facilitate the future design of patient specific cancer therapy, which should be seen as a great leap toward entering the era of personalized medicine. In cancer research, high-throughput technology has generated enormous quantities of data, comprising genome sequences, SNPs and microarray gene expression, etc. [3]. The masses of data
generated by high throughput technologies are onerous to manage, visualize, and convert to useful knowledge required to render more accurate patient outcomes. High-throughput in vitro proliferation/viability drug screening assays have been constructed to facilitate a comprehensive evaluation of the antitumor efficacy of drugs across multiple cell lines that are representative of different cancer types, specific subtypes of certain cancers, and of the various genomic, transcriptomic and proteomic aberrations that are present in cancer [4]. Systems biology signifies the integration of theoretical and computational modeling, biological experimentation and large-scale data analysis. A systems biology approach to utilize new high throughput technologies will be required to efficiently fulfill the promise of personalized molecular medicine [5]. Systems biology incorporates engineering and mathematical methods with biological and medical methodology to conceptualize the interrelated events within the cell, tissue and micro-environment. The examination of gene expression using genomic and computational approaches and identification of sequence motifs can considerably help cancer researchers and medical practitioners [6]. Clinical systems biology has been brought into play to assist with early cancer detection and management, risk reduction, risk identification and cancer prevention. In systems biology, experimental studies have generated the high-throughput quantitative data needed to assist simulation-based clinical research. Combined with rapidly advancing genome and proteome projects, the data explosion has convinced most researchers that a system-level approach is of considerable importance. At the same time, striking improvements in computational efficiency in clinical systems biology have eased the creation and analysis of sensibly realistic yet intricate biological models [7].
http://dx.doi.org/10.1016/j.semcancer.2014.06.005 1044-579X/© 2014 Published by Elsevier Ltd.
Please cite this article in press as: Masoudi-Nejad A, Wang E. Cancer modeling and network biology: Accelerating toward personalized medicine. Semin Cancer Biol (2014), http://dx.doi.org/10.1016/j.semcancer.2014.06.005
42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71
G Model YSCBI 1150 1–3 2 72
73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136
ARTICLE IN PRESS Editorial / Seminars in Cancer Biology xxx (2014) xxx–xxx
2. Importance of cancer modeling and network biology Tumors are remarkable instances of complex systems that can experience self-organization. Because of their inherent complexity, it is essential to analyze their growth on different scales. It includes a number of phenomena that take place over a variety of spatial scales ranging from tissue to molecular length scales. The timescales go from seconds for signaling to years for prostate cancer progression. The complexity of cancer development manifests itself on at least three scales that can be distinguished and described using mathematical models, namely, microscopic, mesoscopic and macroscopic scales. Masudi-Nejad et al. [8] have compared the modeling formalisms and tried to address their applicability to rapidly evolving cancer modeling and simulation approaches. Different scales of cancer modeling, i.e. microscopic, mesoscopic and macroscopic scales are described, followed by an illustration of angiogenesis on the microscopic scale of the cancer modeling. Then, modeling of cancer cell proliferation and survival are observed on a microscopic scale and modeling of multiscale tumor growth is explained in addition to its advantages. They have tried to explain how macroscopic, mesoscopic and microscopic scales are interrelated because tumor growth is dependent on e.g. cell population density, nutrient concentration and chemical factors, cell–cell communication, cell behavior, intracellular mechanisms, pressure, each one modeled on a specific scale. These factors reveal that the three scales are related and inevitably woven together. Powathila et al. [9] have investigated a multiscale computational mathematical model for cancer growth and spread, integrating the multiple effects of radiation therapy and chemotherapy into patient survival probability. The model was implemented using two different cell based modeling techniques. Their focus is on two recent multiscale models of a solid tumor undergoing radioand chemotherapy treatments and they have determined how the computational simulation results can be applied to optimize treatment regimes. They showed that the strategies provided by such multiscale modeling approaches could ultimately guide us to optimal patient-specific multi-modality treatment strategies that may increase the patients’ quality of life. Multi-agent systems are capable of handling the complexity of solutions through modeling, decomposition and managing the interconnections between the corresponding components [10]. Agent-based modeling (ABM) is a particular discrete-based hybrid modeling approach that empowers us to simulate the role of diversity in cell populations in addition to within each individual cell; therefore, it has become a powerful modeling method widely used by computational cancer researchers. Wang et al. [11] introduced some of the emerging ABMs that have provided insight into the understanding of cancer growth and metastasis, extending over various biological scales in time and space, and they described several experimentally testable hypotheses generated by those models. Ultimately, they discuss a number of the current challenges of multiscale agent-based cancer models. Late phase cancer cells may shift back to an early phase of organismal development [12]. Therefore, tumor cell populations could be designated by reversible switches between plastic and rigid network states. Cancer stem cell networks may change between plastic and rigid states very intensively. The switching of network plasticity and rigidity may explain central principles in both cancer development and cancer stem cell behavior. Late stage tumor cells can refer to on the one hand late stage primary tumor cells, but on the other hand it may denote metastatic cells, which have already settled in their novel tissue environment. Csermely et al. [13] have demonstrated how environmental variations increase the adaptation potential of cancer cells triggering the bypass of cellular senescence and the development of cancer stem cells. Also, they have proposed that the elevated evolvability
of cancer stem cells is assisted by their repeated shifts between plastic (proliferative, symmetrically dividing) and rigid (quiescent, asymmetrically dividing, often more invasive) phenotypes that have plastic and rigid networks. They explained network models potentially describing cancer stem cell-like behavior. A number of hypothetical network behaviors were portrayed in this review, which may explain the tremendously high evolvability of cancer stem cells. In this regard, network rigidity/plasticity markers may aid in the development of novel biomarkers of cancer stem cells. Gerlee et al. [14] have reviewed the use of neural networks as models to depict the relationship between the micro-environment, genotype and cellular phenotype in tumor cells. They have concentrated specifically on individual-based models, which use neural networks as a means of connecting such different biological scales of tumor growth. The neural network methodology has the superiority over others in that it has the capability of capturing dynamics on two different time scales. The first one is pathway activity, with gene activity being influenced by micro-environmental factors and the states of other genes. The second one is the time scale of evolutionary changes, where the expressed phenotypes are selected for and the corresponding subclones may alter the microenvironment in a feedback loop. This makes such models appropriate tools for investigating and interrogating the evolutionary dynamics of tumor growth. They discuss the abundant spatial heterogeneity in cancer, highlighting the role of context in driving selection, as well as the role of the tumor in producing context, a key idea that is becoming progressively important. In the review by Bolouri H. [15], two critical emerging trends have been reviewed and suggested. First, the author has reviewed the discussions indicating that the events within the tumor microenvironment (TME) can trigger fate determining consequences including growth advantages, resistance to interventions and remaining dormant for long times. The roles of computational, mathematical and statistical tools in studying the TME are emphasized. Also, some exemplars of TME modeling are discussed herein. Second, a trend toward data-rich, molecularly detailed computational models is highlighted. Regarding the multifactorial nature of cancer, revealing its metabolic alterations and evaluating their implications is a major challenge in biomedical sciences, which if properly understood will aid in the optimal design of personalized treatments. The most studied metabolic modification in cancer is the degradation of glucose via aerobic glycolysis, a less efficient pathway for generating ATP compared with oxidative phosphorylation, which occurs even in the presence of oxygen. This finding was reported almost a century ago by Otto Warburg [16] in his seminal investigations of metabolism in cancer cells. Remarkably, studies in cancer biology have proved that cancer genes are intimately connected to the Warburg effect and other metabolic modifications [17]. Masoudi-Nejad et al. [18] have provided a manuscript in which the recent achievements in structural-based analysis of cancer from a metabolic disease point of view have been reviewed. The metabolic function of a cancer cell, called the Warburg effect, has come into the limelight in recent years and structural-based analysis has been shown to be appropriate for developing diagnostic and prognostic molecular signatures, as well as for finding new drug targets. Two separate structural methods are considered here: topological and constraint-based methods. They highlighted the fact that although structural perspective of cancer cellular metabolism may introduce new opportunities for disease treatment, crucial challenges such as data integration platforms, discovery of details about mutations and enzyme regulation, and expanding models to explain the other hallmarks of cancer remain for further investigations. ResendisAntonio et al. [19] have concentrated on suggesting a common view of some of these approaches whose application and integration will be pivotal in the move from local to global conclusions in cancer research as a metabolic disease. They have tried to describe
Please cite this article in press as: Masoudi-Nejad A, Wang E. Cancer modeling and network biology: Accelerating toward personalized medicine. Semin Cancer Biol (2014), http://dx.doi.org/10.1016/j.semcancer.2014.06.005
137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202
G Model YSCBI 1150 1–3
ARTICLE IN PRESS Editorial / Seminars in Cancer Biology xxx (2014) xxx–xxx
211
the importance of constraint-based modeling and multidisciplinary methods in cancer systems biology. Wang et al. [20] discussed a system framework in which interplay of cancer hallmark networks has been proposed based on the evolutionary dynamics of cancer. New concepts such as the gene regulatory phenotype and the network operational signature have been proposed in the framework in order to construct predictive models for personalized drug targets, drug resistance and metastasis of cancer patients using genome sequencing data.
212
Conflicts of interest
203 204 205 206 207 208 209 210
213
214
The authors declare no conflicts of interest. Acknowledgements
221
Authors thank Eva Klein, the former Editor-in-Chief of Seminars in Cancer Biology, for initial acceptance of this special issue, also full support and encouragement of current Editor-in-Chief of the journal, Theresa Vincent for compilation of this exciting set of papers. We appreciate great help of Alexander Anderson for editing this manuscript. Last but not least, we thank the authors of this special issue for their excellent and timely contributions and support.
222
References
215 216 217 218 219 220
223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 Q3 240 241
[1] Martins ML, Ferreira Jr SC, Vilela MJ. Multiscale models for the growth of avascular tumors. Phys Life Rev 2007;4:128–56. [2] Gibbs JB. Mechanism-based target identification and drug discovery in cancer research. Science 2000;287:1969–73. [3] Chang PL. Clinical bioinformatics. Chang Gung Med J 2005;28:201–11. [4] Monks A, Scudiero D, Skehan P, Shoemaker R, Paull K, Vistica D, et al. Feasibility of a high-flux anticancer drug screen using a diverse panel of cultured human tumor cell lines. J Natl Cancer Inst 1991;83:757–66. [5] Gonzalez-Angulo AM, Hennessy BT, Mills GB. Future of personalized medicine in oncology: a systems biology approach. J Clin Oncol 2010;28:2777–83. [6] Gonzalez-Perez A, Mustonen V, Reva B, Ritchie GR, Creixell P, Karchin R, et al. Computational approaches to identify functional genetic variants in cancer genomes. Nat Methods 2013;10:723–9. [7] Kitano H. Computational systems biology. Nature 2002;420:206–10. [8] Masoudi-Nejad A, Bidkhori G, Hosseini Ashtiani S, Najafi A, Bozorgmehr JH, Wang E. Cancer systems biology and modeling: microscopic scale and multiscale approaches. Semin Cancer Biol 2014. [9] Powathil GG, Swat M, Chaplain MA. Systems oncology: towards patient-specific treatment regimes informed by multiscale mathematical modelling. Semin Cancer Biol 2014.
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Ali Masoudi-Nejad ∗ Q1 Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran Wang a,b,∗∗
a
Edwin National Research Council Canada, Montreal, QC H4P 2R2, Canada b Center for Bioinformatics, McGill University, Montreal, QC H3G 0B1, Canada ∗ Corresponding
author.
∗∗ Corresponding
author at: National Research Council Canada, Montreal, QC H4P 2R2, Canada. Q2 E-mail addresses: [email protected] (A. Masoudi-Nejad), [email protected] (E. Wang).
Please cite this article in press as: Masoudi-Nejad A, Wang E. Cancer modeling and network biology: Accelerating toward personalized medicine. Semin Cancer Biol (2014), http://dx.doi.org/10.1016/j.semcancer.2014.06.005
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