No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
A Modeling Approach to Explain Mutually Exclusive and Co-Occurring Genetic Alterations in Bladder Tumorigenesis
Abstract
Relationships between genetic alterations, such as co-occurrence or mutual exclusivity, are often observed in cancer, where their understanding may provide new insights into etiology and clinical management. In this study, we combined statistical analyses and computational modelling to explain patterns of genetic alterations seen in 178 patients with bladder tumours (either muscle-invasive or non-muscle-invasive). A statistical analysis on frequently altered genes identified pair associations including co-occurrence or mutual exclusivity. Focusing on genetic alterations of protein-coding genes involved in growth factor receptor signalling, cell cycle and apoptosis entry, we complemented this analysis with a literature search to focus on nine pairs of genetic alterations of our dataset, with subsequent verification in three other datasets available publically. To understand the reasons and contexts of these patterns of associations while accounting for the dynamics of associated signalling pathways, we built a logical model. This model was validated first on published mutant mice data, then used to study patterns and to draw conclusions on counter-intuitive observations, allowing one to formulate predictions about conditions where combining genetic alterations benefits tumorigenesis. For example, while CDKN2A homozygous deletions occur in a context of FGFR3 activating mutations, our model suggests that additional PIK3CA mutation or p21CIP deletion would greatly favour invasiveness. Further, the model sheds light on the temporal orders of gene alterations, for example, showing how mutual exclusivity of FGFR3 and TP53 mutations is interpretable if FGFR3 is mutated first. Overall, our work shows how to predict combinations of the major gene alterations leading to invasiveness.
Copyright © 2015, American Association for Cancer Research.
... The logical approach has already been successfully applied to cancer biology [44][45][46][47] or immune biology [48][49][50][51] and has provided significant insights on drug identification [52,53,28]. ...
... In 2015, a model of bladder cancer was constructed to explain the co-occurrence and the mutual exclusivity in a subset of gene alterations that had been derived from a statistical analysis [44]. In this example, the model was able to support some results of the statistical analysis by providing a mechanistic explanation of the findings but with this model, a co-occurrence could be refuted. ...
... Depending on the Boolean model and the genes included in the signaling pathways, specific questions can be studied with their own in silico experiments. Some models have focused on the interplay between signaling pathways with a generic approach [47,59], while other focused on specific cancers: breast [46], colon [60][61][62][63], bladder [44], gastric [64], or prostate cancer [28]. For each of these studies, some analyses could be made on the search for drug synergies, mutation associations, personalized treatments, and the consequences of combinations of mutations on the phenotypes, showing the wide panels of questions that can be explored with these models. ...
As a result of the development of experimental technologies and the accumulation of data, biological and molecular processes can be described as complex networks of signaling pathways. These networks are often directed and signed, where nodes represent entities (genes/proteins) and arrows interactions. They are translated into mathematical models by adding a dynamic layer onto them. Such mathematical models help to understand and interpret non-intuitive experimental observations and to anticipate the response to external interventions such as drug effects on phenotypes. Several frameworks for modeling signaling pathways exist. The choice of the appropriate framework is often driven by the experimental context. In this review, we present MaBoSS, a tool based on Boolean modeling using a continuous time approach, which predicts time-dependent probabilities of entities in different biological contexts. MaBoSS was initially built to model the intracellular signaling in non-interacting homogeneous cell populations. MaBoSS was then adapted to model heterogeneous cell populations (EnsembleMaBoSS) by considering families of models rather than a unique model. To account for more complex questions, MaBoSS was extended to simulate dynamical interacting populations (UPMaBoSS), with a precise spatial distribution (PhysiBoSS). To illustrate all these levels of description, we show how each of these tools can be used with a running example of a simple model of cell fate decisions. Finally, we present practical applications to cancer biology and studies of the immune response.
... Computing the attractors reachable from a given initial configuration is at the core of many studies of biological processes with BNs [1,2,11,6,7]. These studies typically involve comparing the effect of a network mutation (forcing some components to have fixed value) on the sets of reachable attractors and their propensities. ...
... From the literature, we selected BNs modeling cell fate decision processes: the reduced cell death receptor model [1] with 14 components; the tumor invasion model [2] with 32 components; and the bladder model [11] with 35 components. These models have been designed with the fully-asynchronous update mode, and have been evaluated with respect to their ability to predict the changes of the attractors propensities subject to different mutation conditions (modeled by forcing some components to some state). ...
In systems biology, Boolean networks (BNs) aim at modeling the qualitative dynamics of quantitative biological systems. Contrary to their (a) synchronous interpretations, the Most Permissive (MP) interpretation guarantees capturing all the trajectories of any quantitative system compatible with the BN, without additional parameters. Notably, the MP mode has the ability to capture transitions related to the heterogeneity of time scales and concentration scales in the abstracted quantitative system and which are not captured by asynchronous modes. So far, the analysis of MPBNs has focused on Boolean dynamical properties, such as the existence of particular trajectories or attractors.This paper addresses the sampling of trajectories from MPBNs in order to quantify the propensities of attractors reachable from a given initial BN configuration. The computation of MP transitions from a configuration is performed by iteratively discovering possible state changes. The number of iterations is referred to as the permissive depth, where the first depth corresponds to the asynchronous transitions. This permissive depth reflects the potential concentration and time scales heterogeneity along the abstracted quantitative process. The simulation of MPBNs is illustrated on several models from the literature, on which the depth parametrization can help to assess the robustness of predictions on attractor propensities changes triggered by model perturbations.
... Computing the attractors reachable from a given initial configuration is at the core of many studies of biological processes with BNs [1,2,11,6,7]. These studies typically involve comparing the effect of a network mutation (forcing some components to have fixed value) on the sets of reachable attractors and their propensities. ...
... From the literature, we selected BNs modeling cell fate decision processes: the reduced cell death receptor model [1] with 14 components; the tumor invasion model [2] with 32 components; and the bladder model [11] with 35 components. These models have been designed with the fully-asynchronous update mode, and have been evaluated with respect to their ability to predict the changes of the attractors propensities subject to different mutation conditions (modeled by forcing some components to some state). ...
In systems biology, Boolean networks (BNs) aim at modeling the qualitative dynamics of quantitative biological systems. Contrary to their (a)synchronous interpretations, the Most Permissive (MP) interpretation guarantees capturing all the trajectories of any quantitative system compatible with the BN, without additional parameters. Notably, the MP mode has the ability to capture transitions related to the heterogeneity of time scales and concentration scales in the abstracted quantitative system and which are not captured by asynchronous modes. So far, the analysis of MPBNs has focused on Boolean dynamical properties, such as the existence of particular trajectories or attractors. This paper addresses the sampling of trajectories from MPBNs in order to quantify the propensities of attractors reachable from a given initial BN configuration. The computation of MP transitions from a configuration is performed by iteratively discovering possible state changes. The number of iterations is referred to as the permissive depth, where the first depth corresponds to the asynchronous transitions. This permissive depth reflects the potential concentration and time scales heterogeneity along the abstracted quantitative process. The simulation of MPBNs is illustrated on several models from the literature, on which the depth parametrization can help to assess the robustness of predictions on attractor propensities changes triggered by model perturbations.
... In [99], it was proved that the attractors of a mammal cell-cycle in several perturbed conditions were in agreement with known phenotypes in the literature. The steady states and attractors of a logical model were also proved to fit with genotyping information in [100]. Finally, in [101], [102], the authors studied a network of T-helper lymphocytes and evidenced that the steady states of the network in several environmental or gene-deletion/activation conditions were in agreement with observed clinical phenotypes. ...
... Hence, a mutation may affect the dynamics in terms of loss or gain of phenotypes, loss or gain of reachability, etc...(e.g., loss of tumors suppressor in cancer cells. The impact of mutations on biological systems represented with logical networks was highlighted for instance in [98], [100]). Intuitively, we expect that the mutation of a master gene regulator in the signature of a phenotype will strongly impact the model. ...
Le lupus systémique erythémateux est un exemple de maladie complexe, hétérogène et multi-factorielle. L'identification de signature pouvant expliquer la cause d'une maladie est un enjeu important pour la stratification des patients. De plus, les analyses statistiques classiques s'appliquent difficilement quand les populations d'intérêt sont hétérogènes et ne permettent pas de mettre en évidence la cause. Cette thèse présente donc deux méthodes permettant de répondre à cette problématique. Tout d'abord, un modèle transomique est décrit pour structurer l'ensemble des données omiques en utilisant le Web sémantique (RDF). Son alimentation repose sur une analyse à l'échelle du patient. L'interrogation de ce modèle sous forme d'une requête SPARQL a permis l'identification d'expression Individually-Consistent Trait Loci (eICTLs). Il s'agit d'une association par raisonnement d'un couple SNP-gène pour lequel la présence d'un SNP influence la variation d'expression du gène. Ces éléments ont permis de réduire la dimensionalité des données omiques et présentent un apport plus informatif que les données de génomique. Cette première méthode se base uniquement sur l'utilisation des données omiques. Ensuite, la deuxième méthode repose sur la dépendance entre les régulations existante dans les réseaux biologiques. En combinant la dynamique des systèmes biologiques et l'analyse par concept formel, les états stables générés sont automatiquement classés. Cette classification a permis d'enrichir des signatures biologiques, caractéristique de phénotype. De plus, de nouveaux phénotypes hybrides ont été identifiés.
... If such synergy exists, cancer genomes should be enriched for these co-alterations, i.e. tumors harboring alterations in both genes should be more frequent than expected by chance. Several studies have reported an abundance of co-occurring somatic alterations in various types of cancer (Bredel et al. 2009;Gorringe et al. 2010;Klijn et al. 2010;Milosevic et al. 2012;Kandoth et al. 2013;Remy et al. 2015). For somatic copy number changes, however, it has also been suggested that co-occurring alterations emerge from tumors' overall levels of genomic disruption (Zack et al. 2013). ...
... A commonly used test for both co-occurrence and mutual exclusivity is Fisher's exact test applied to a 2 × 2 contingency table (Milosevic et al. 2012;Kandoth et al. 2013;Remy et al. 2015). The test is used to support cooccurrence when the number of tumors with alterations in both genes is significantly higher than expected by chance. ...
Just like recurrent somatic alterations characterize cancer genes, mutually exclusive or co-occurring alterations across genes suggest functional interactions. Identifying such patterns in large cancer studies thus helps the discovery of unknown interactions. Many studies use Fisher’s exact test or simple permutation procedures for this purpose. These tests assume identical gene alteration probabilities across tumors, which is not true for cancer. We show that violating this assumption yields many spurious co-occurrences and misses many mutual exclusivities. We present DISCOVER, a novel statistical test that addresses the limitations of existing tests. In a comparison with six published mutual exclusivity tests, DISCOVER is more sensitive while controlling its false positive rate. A pan-cancer analysis using DISCOVER finds no evidence for widespread co-occurrence. Most co-occurrences previously detected do not exceed expectation by chance. In contrast, many mutual exclusivities are identified. These cover well known genes involved in the cell cycle and growth factor signaling. Interestingly, also lesser known regulators of the cell cycle and Hedgehog signaling are identified.
Availability
R and Python implementations of DISCOVER, as well as Jupyter notebooks for reproducing all results and figures from this paper can be found at http://ccb.nki.nl/software/discover .
... This drop in C-erbB-2 expression in recurring cases after BCG therapy was statistically significant (p < 0.005) when compared to its expression in the same group before therapy. (9). Aside from these clinicpathologic criteria, it would be advantageous if biological markers could contribute in the risk categorization of NMIBUC lesions and in predicting their risk of recurrence and progression after IVI therapy. ...
accounts for approximately 75% of all BC lesions at initial presentation; this percentage is even greater in younger patients (< 40). The high prevalence of NMIBUC can be attributed to its long-term survival and reduced risk of cancer specific mortality compared to muscle-invasive (T2-4 stages) tumors (1). These tumors are restricted to the mucosa (Ta, CIS) or submucosa (T1), and are treated by trans-urethral resection. Although trans-urethral resection of bladder tumors (TURBT) by itself can totally remove Ta/T1 lesions, they frequently recur and can progress to MIBC. The recurrence rate of such tumors is 50 to 70%. As a result, all patients should be considered for adjuvant intrav-esical instillation (IVI) therapy and surveillance, based on their risk stratification (2). The most important step in the management of NMIBUC is the transurethral resection procedure. This procedure is crucial for the complete removal of all visible/suspicious lesions and for proper grading and staging by sampling of detrusor muscle; thus, determining the next appropriate treatment (2). Because of the high risk of recurrence and progression following primary resection, adjuvant therapy and long-term surveillance should be considered in all patients (3). There is evidence that treatment with Bacillus Calmette-Guérin immunotherapy following primary resection can lower cancer recurrence rates and progression to more advanced stages (4). C-erbB-2 is a tyrosine kinase transmembrane protein that is related to epidermal growth factor receptor (EGFR) family and it is known as HER2/neu (human epidermal growth factor receptor-2). Its expression in urinary bladder transitional cell carcinoma has been described, and it has been proposed that its expression increases with tumor grade and recurrence of urothelial cancer. Bladder urothelial carcino-mas with C-erbB-2 expression have poor prognosis, hence Background: Transurethral resection (TUR) followed by adjuvant therapy is still the treatment of choice of Non-Muscle-Invasive Bladder Urothelial Carcinoma (NMIBUC). However, recurrence is one of the most troublesome features of these lesions. Early second resection and adjuvant BCG therapy has been shown to improve the outcome. Objective: To evaluate the prognostic value of C-erbB-2 (HER2/neu) expression status in Non-Muscle-Invasive Bladder Urothelial Carcinoma cases, before and after intravesical Bacillus Calmette Guerin (BCG immunotherapy). Materials and methods: HER2/neu expression was studied in 120 (Ta-T1) Non-Muscle-Invasive Urothelial Carcinoma cases. The expression was evaluated and compared to the expression after Bacillus Calmette Guerin (BCG) immunotherapy. Results: HER2/neu expression in low and high grade of the Non-Muscle-Invasive Urothelial Carcinoma was (38%) and (83%) respectively. The difference of the expression rates by tumor grade was statistically significant. In recurring lesions post BCG therapy, C-erbB-2 expression was markedly decreased (31.6%) when compared to its expression before therapy (65%). Conclusions: The HER2/neu expression increased as the tumor grade rose. The reduction in expression following BCG treatment in Non-Invasive transitional cell carcinoma cases could reflect a reduction of the potential malignancy of the tumor.
... This drop in C-erbB-2 expression in recurring cases after BCG therapy was statistically significant (p < 0.005) when compared to its expression in the same group before therapy. (9). Aside from these clinicpathologic criteria, it would be advantageous if biological markers could contribute in the risk categorization of NMIBUC lesions and in predicting their risk of recurrence and progression after IVI therapy. ...
Background:
Transurethral resection (TUR) followed by adjuvant therapy is still the treatment of choice of Non-Muscle-Invasive Bladder Urothelial Carcinoma (NMIBUC). However, recurrence is one of the most troublesome features of these lesions. Early second resection and adjuvant BCG therapy has been shown to improve the outcome.
Objective:
To evaluate the prognostic value of C-erbB-2 (HER2/neu) expression status in Non-Muscle-Invasive Bladder Urothelial Carcinoma cases, before and after intravesical Bacillus Calmette Guerin (BCG immunotherapy).
Materials and methods:
HER2/neu expression was studied in 120 (Ta-T1) Non-Muscle-Invasive Urothelial Carcinoma cases. The expression was evaluated and compared to the expression after Bacillus Calmette Guerin (BCG) immunotherapy.
Results:
HER2/neu expression in low and high grade of the Non- Muscle-Invasive Urothelial Carcinoma was (38%) and (83%) respectively. The difference of the expression rates by tumor grade was statistically significant. In recurring lesions post BCG therapy, C-erbB-2 expression was markedly decreased (31.6%) when compared to its expression before therapy (65%).
Conclusions:
The HER2/neu expression increased as the tumor grade rose. The reduction in expression following BCG treatment in Non-Invasive transitional cell carcinoma cases could reflect a reduction of the potential malignancy of the tumor.
... See Appendix A for a presentation of logical modeling. They have demonstrated their effectiveness in a variety of biological systems [114], such as the regulation of the cellular response to DNA damage [115] or the combinatorial effect of mutations in tumorigenesis [116]. The emergence of next generation sequencing and the amount of data generated have facilitated the study of molecular regulatory networks helping the construction of numerous logical models, among others related to hematopoiesis. ...
Single-cell transcriptomic technologies enable the uncovering and characterization of cellular heterogeneity and pave the way for studies aiming at understanding the origin and consequences of it. The hematopoietic system is in essence a very well adapted model system to benefit from this technological advance because it is characterized by different cellular states. Each cellular state, and its interconnection, may be defined by a specific location in the global transcriptional landscape sustained by a complex regulatory network. This transcriptomic signature is not fixed and evolved over time to give rise to less efficient hematopoietic stem cells (HSC), leading to a well-documented hematopoietic aging. Here, we review the advance of single-cell transcriptomic approaches for the understanding of HSC heterogeneity to grasp HSC deregulations upon aging. We also discuss the new bioinformatics tools developed for the analysis of the resulting large and complex datasets. Finally, since hematopoiesis is driven by fine-tuned and complex networks that must be interconnected to each other, we highlight how mathematical modeling is beneficial for doing such interconnection between multilayered information and to predict how HSC behave while aging.
... The interest in models where variables take on more than two values can be traced back to R. Thomas (Thomas 1991). Examples of such biological models are: (i) multi-state models (Thieffry and Thomas 1995;Sánchez and Thieffry 2001), (ii) models that employ a combination of Boolean and ternary variables (Espinosa-Soto et al. 2004;Remy et al. 2015;Abou-Jaoudé et al. 2009), (iii) strictly ternary models (Chifman et al. 2017) and (iv) four state models (Setty et al. 2003). Networks of large biosystems in which variables assume more than two discrete states do present a computational challenge, but with the development of faster computational tools, it is only plausible to assume that the scientific community will see a rise in the use of multi-state models. ...
Discrete dynamical systems in which model components take on categorical values have been successfully applied to biological networks to study their global dynamic behavior. Boolean models in particular have been used extensively. However, multi-state models have also emerged as effective computational tools for the analysis of complex mechanisms underlying biological networks. Models in which variables assume more than two discrete states provide greater resolution, but this scheme introduces discontinuities. In particular, variables can increase or decrease by more than one unit in one time step. This can be corrected, without changing fixed points of the system, by applying an additional rule to each local activation function. On the other hand, if one is interested in cyclic attractors of their system, then this rule can potentially introduce new cyclic attractors that were not observed previously. This article makes some advancements in understanding the state space dynamics of multi-state network models with synchronous, sequential, or block-sequential update schedules and establishes conditions under which no new cyclic attractors are added to networks when the additional rule is applied. Our analytical results have the potential to be incorporated into modeling software and aid researchers in their analyses of biological multi-state networks.
... Mathematical modeling of such complex and intricate networks can help understand and predict experimental results (Cohen et al., 2013;Ferrell, 2015;Kolch et al., 2015;Remy et al., 2015;Abou-Jaoudé et al., 2016). However, the choice of the most appropriate mathematical formalism to model such intra-cellular processes depends on the biological question and the available data (Le Novère, 2015). ...
Mathematical modeling aims at understanding the effects of biological perturbations, suggesting ways to intervene and to reestablish proper cell functioning in diseases such as cancer or in autoimmune disorders. This is a difficult task for obvious reasons: the level of details needed to describe the intra-cellular processes involved, the numerous interactions between cells and cell types, and the complex dynamical properties of such populations where cells die, divide and interact constantly, to cite a few. Another important difficulty comes from the spatial distribution of these cells, their diffusion and motility. All of these aspects cannot be easily resolved in a unique mathematical model or with a unique formalism. To cope with some of these issues, we introduce here a novel framework, UPMaBoSS (for Update Population MaBoSS), dedicated to modeling dynamic populations of interacting cells. We rely on the preexisting tool MaBoSS, which enables probabilistic simulations of cellular networks. A novel software layer is added to account for cell interactions and population dynamics, but without considering the spatial dimension. This modeling approach can be seen as an intermediate step towards more complex spatial descriptions. We illustrate our methodology by means of a case study dealing with TNF-induced cell death. Interestingly, the simulation of cell population dynamics with UPMaBoSS reveals a mechanism of resistance triggered by TNF treatment. Relatively easy to encode, UPMaBoSS simulations require only moderate computational power and execution time. To ease the reproduction of simulations, we provide several Jupyter notebooks that can be accessed within the CoLoMoTo Docker image, which contains all software and models used for this study.
... In this sense, ternary networks lie in the middle ground between Boolean networks and ordinary differential equations, being more descriptive but keeping the qualitative nature of a Boolean network. Some example of ternary biomodels are Espinosa-Soto et al. (2004), Remy et al. (2015) and Chifman et al. (2017). ...
We introduce SteadyCellPhenotype, a browser based interface for the analysis of ternary biological networks. It includes tools for deterministically finding all steady states of a network, as well as the simulation and visualization of trajectories with publication quality graphics. Simulations allow us to approximate the size of the basin for attractors and deterministic simulations of trajectories nearby specified points allow us to explore the behavior of the system in that neighborhood.
Availability
https://github.com/knappa/steadycellphenotype MIT License.
Site
https://steadycellphenotype.github.io
... Results are analysed in two ways: (1) the trajectories for particular model states (states of nodes) can be interpreted as the evolution of a cell population as a function of time and (2) asymptotic solutions can be represented as pie charts to illustrate the proportions of cells in particular model states. Stochastic simulations with MaBoSS have already been successfully applied to study several Boolean models (Calzone et al., 2010;Cohen et al., 2015;Remy et al., 2015). A description of the methods we have used for the simulation of the model can be found in the Appendix 1, Section 2. ...
Prostate cancer is the second most occurring cancer in men worldwide. To better understand the mechanisms of tumorigenesis and possible treatment responses, we developed a mathematical model of prostate cancer which considers the major signalling pathways known to be deregulated. We personalised this Boolean model to molecular data to reflect the heterogeneity and specific response to perturbations of cancer patients. 488 prostate samples were used to build patient-specific models and compared to available clinical data. Additionally, eight prostate cell-line-specific models were built to validate our approach with dose-response data of several drugs. The effects of single and combined drugs were tested in these models under different growth conditions. We identified 15 actionable points of interventions in one cell-line-specific model whose inactivation hinders tumorigenesis. To validate these results, we tested nine small molecule inhibitors of five of those putative targets and found a dose-dependent effect on four of them, notably those targeting HSP90 and PI3K. These results highlight the predictive power of our personalised Boolean models and illustrate how they can be used for precision oncology.
... Since the available data is mostly of qualitative nature, we chose a qualitative modeling approach, logical modeling. This approach has already been proven to be a powerful tool in the description and analysis of cell fate decisions involved in development and cancer [20][21][22][23]. Based on the genome data, we aim for constructing patient-specific models as perturbations of a wild type B-cell model. ...
Personalized medicine aims to tailor treatment to patients based on their individual genetic or molecular background. Especially in diseases with a large molecular heterogeneity, such as diffuse large B-cell lymphoma (DLBCL), personalized medicine has the potential to improve outcome and/or to reduce resistance towards treatment. However, integration of patient-specific information into a computational model is challenging and has not been achieved for DLBCL. Here, we developed a computational model describing signaling pathways and expression of critical germinal center markers. The model integrates the regulatory mechanism of the signaling and gene expression network and covers more than 50 components, many carrying genetic lesions common in DLBCL. Using clinical and genomic data of 164 primary DLBCL patients, we implemented mutations, structural variants and copy number alterations as perturbations in the model using the CoLoMoTo notebook. Leveraging patient-specific genotypes and simulation of the expression of marker genes in specific germinal center conditions allows us to predict the consequence of the modeled pathways for each patient. Finally, besides modeling how genetic perturbations alter physiological signaling, we also predicted for each patient model the effect of rational inhibitors, such as Ibrutinib, that are currently discussed as possible DLBCL treatments, showing patient-dependent variations in effectiveness and synergies.
... Thus far, numerous platforms have been reported to help researchers develop qualitative Boolean network models. Amongst them, FluxAnalyzer (237) (244) studied mutually exclusive and cooccurring genetic alterations in bladder cancer. GIMsim also employed multi-valued logical functions, useful in simulating qualitative dynamical behavior in cancer research. ...
Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.
... The majority of these models pertains to mammalian systems and a much smaller fraction pertains to plant systems. The mammalian models include networks for signaling pathways [24,64,65], differentiation [66,67] and various cancers [68][69][70]. Among the plant models, this compilation includes cases from flower organ specification [67], root stem cells [71], and guard cell signalling [72]. ...
The properties of random Boolean networks as models of gene regulation have been investigated extensively by the statistical physics community. In the past two decades, there has been a dramatic increase in the reconstruction and analysis of Boolean models of biological networks. In such models, neither network topology nor Boolean functions (or logical update rules) should be expected to be random. In this contribution, we focus on biologically meaningful types of Boolean functions, and perform a systematic study of their preponderance in gene regulatory networks. By applying the k[P] classification based on number of inputs k and bias P of functions, we find that most Boolean functions astonishingly have odd bias in a reference biological dataset of 2687 functions compiled from published models. Subsequently, we are able to explain this observation along with the enrichment of read-once functions (RoFs) and its subset, nested canalyzing functions (NCFs), in the reference dataset in terms of two complexity measures: Boolean complexity based on string lengths in formal logic which is yet unexplored in the biological context, and the average sensitivity. Minimizing the Boolean complexity naturally sifts out a subset of odd-biased Boolean functions which happen to be the RoFs. Finally, we provide an analytical proof that NCFs minimize not only the Boolean complexity, but also the average sensitivity in their k[P] set.
... The interest in models where variables take on more than two values can be traced back to R. Thomas [1]. Examples of such biological models are: (i) multi-state models [2,3], (ii) models that employ a combination of Boolean and ternary variables [4][5][6], (iii) strictly ternary models [7] and (iv) four state models [8]. Networks of large biosystems in which variables assume more than two discrete states do present a computational challenge, but with the development of faster computational tools, it is only plausible to assume that the scientific community will see a rise in the use of multi-state models. ...
Discrete dynamical systems in which model components take on categorical values have been successfully applied to biological networks to study their global dynamic behavior. Boolean models in particular have been used extensively. However, multi-state models have also emerged as effective computational tools for the analysis of complex mechanisms underlying biological networks. Models in which variables assume more than two discrete states provide greater resolution, but this scheme introduces discontinuities. In particular, variables can increase or decrease by more than one unit in one time step. This can be corrected, without changing fixed points of the system, by applying an additional rule to each local activation function. On the other hand, if one is interested in cyclic attractors of their system, then this rule can potentially introduce new cyclic attractors that were not observed previously. This article makes some advancements in understanding the state space dynamics of multi-state network models with a synchronous update schedule and establishes conditions under which no new cyclic attractors are added to networks when the additional rule is applied. Our analytical results have the potential to be incorporated into modeling software and aid researchers in their analyses of biological multi-state networks.
... Logical formalism is a discrete qualitative approach that provides a solution for large regulatory, not requiring precise quantitative knowledge of rate constants (Le Novère, 2015). Logical modelling of large regulatory networks has been successful applied for exploring multiple hypotheses, describing observed behaviours and identifying novel biomarkers in cancer (Steinway et al., 2014;Cohen et al., 2015;Flobak et al., 2015;Remy et al., 2015). Logical network models of EMT have been also quite successful for describing hepatocarcinoma and other cancer systems, explaining synergistic effects of signalling pathways and phenotype stability (Steinway et al., 2014(Steinway et al., , 2015Cohen et al., 2015). ...
MODEL DESCRIPTION, VALIDATION AND SIMULATION DETAILS of the work presented in main article Simulation of multiple microenvironments shows a pivot role of RPTPs on the control of Epithelial-to-Mesenchymal Transition.
... In this sense, ternary networks lie in the middle ground between Boolean networks and ODEs, being more descriptive but keeping the qualitative nature of a Boolean network. Some example of ternary biomodels are Espinosa-Soto et al. (2004); Remy et al. (2015); Chifman et al. (2017). ...
We introduce SteadyCellPhenotype , a browser based interface for the analysis of ternary biological networks. It includes tools for deterministically finding all steady states of a network, as well as the simulation and visualization of trajectories with publication quality graphics. Stochastic simulations allow us to approximate the size of the basin for attractors and deterministic simulations of trajectories nearby specified points allow us to explore the behavior of the system in that neighborhood.
Availability
https://github.com/knappa/steadycellphenotypeMITLicense
Contact
chifman@american.edu
... Results are analyzed in two ways: (1) the trajectories for particular model states (states of nodes) can be interpreted as the evolution of a cell population as a function of time and (2) asymptotic solutions can be represented as pie charts to illustrate the proportions of cells in particular model states. Stochastic simulations with MaBoSS have already been successfully applied to study several Boolean models (Calzone et al, 2010;Remy et al, 2015;Cohen et al, 2015). ...
Prostate cancer is the second most occurring cancer in men worldwide. To better understand the mechanisms of tumorigenesis and possible treatment responses, we developed a mathematical model of prostate cancer which considers the major signalling pathways known to be deregulated.
We personalised this Boolean model to molecular data to reflect the heterogeneity and specific response to perturbations of cancer patients. 488 prostate samples were used to build patient-specific models and compared to available clinical data. Additionally, eight prostate cell-line-specific models were built to validate our approach with dose-response data of several drugs.
The effects of single and combined drugs were tested in these models under different growth conditions. We identified 15 actionable points of interventions in one cell-line-specific model whose inactivation hinders tumorigenesis. To validate these results, we tested nine small molecule inhibitors of five of those putative targets and found a dose-dependent effect on four of them, notably those targeting HSP90 and PI3K. These results highlight the predictive power of our personalized Boolean models and illustrate how they can be used for precision oncology.
... As most of the available data are qualitative, we opted for using a qualitative approach. Logical modelling is well suited to represent such qualitative data and has been applied to similar processes [13][14][15]. This qualitative formalism relies on the construction of a regulatory graph, whose nodes denote molecular components (e.g. ...
Dendritic cells (DCs) are the major specialized antigen-presenting cells, thereby connecting innate and adaptive immunity. Because of their role in establishing adaptive immunity, they constitute promising targets for immunotherapy. Monocytes can differentiate into DCs in vitro in the presence of colony-stimulating factor 2 (CSF2) and interleukin 4 (IL4), activating four signalling pathways (MAPK, JAK/STAT, NFKB and PI3K). However, the downstream transcriptional programme responsible for DC differentiation from monocytes (moDCs) remains unknown. By analysing the scientific literature on moDC differentiation, we established a preliminary logical model that helped us identify missing information regarding the activation of genes responsible for this differentiation, including missing targets for key transcription factors (TFs). Using ChIP-seq and RNA-seq data from the Blueprint consortium, we defined active and inactive promoters, together with differentially expressed genes in monocytes, moDCs and macrophages, which correspond to an alternative cell fate. We then used this functional genomic information to predict novel targets for previously identified TFs. By integrating this information, we refined our model and recapitulated the main established facts regarding moDC differentiation. Prospectively, the resulting model should be useful to develop novel immunotherapies targeting moDCs.
... Another logical modeling platform, GIMsim(257), published byNaldi et al., in 2009, also employed asynchronous state transition graphs to perform qualitative logical modeling which is especially useful for networks with large state space. This platform was employed by Flobak et al.(258) to map cell fate decisions in gastric adenocarcinoma cell-line towards evaluating drug synergies for treatment purposes, while Remy et al.(259) studied mutually exclusive and co-occurring genetic alterations in bladder cancer. ...
Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built on top of this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- or multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multiscale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes by highlighting that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.
... In contrast, an overexpression of EGFR or an FGFR3 activating mutation, combined with a stimulus of the TGFB receptor, would lead to growth arrest and apoptosis. Still, regarding bladder cancer, and considering the same cellular responses, a logical model focusing on the E2F pathway was developed by Remy et al. to explain patterns of genetic alterations (co-occurrences and mutual exclusivities) observed in tumour data [36]. The authors identified diverse. ...
The multistep development of cancer involves the cooperation between multiple molecular lesions, as well as complex interactions between cancer cells and the surrounding tumour microenvironment. The search for these synergistic interactions using experimental models made tremendous contributions to our understanding of oncogenesis. Yet, these approaches remain labour-intensive and challenging. To tackle such a hurdle, an integrative, multidisciplinary effort is required. In this article, we highlight the use of logical computational models, combined with experimental validations, as an effective approach to identify cooperative mechanisms and therapeutic strategies in the context of cancer biology. In silico models overcome limitations of reductionist approaches by capturing tumour complexity and by generating powerful testable hypotheses. We review representative examples of logical models reported in the literature and their validation. We then provide further analyses of our logical model of Epithelium to Mesenchymal Transition (EMT), searching for additional cooperative interactions involving inputs from the tumour microenvironment and gain of function mutations in NOTCH.
... One of these approaches is the logical modeling, which uses discrete variables governed by logical rules. Its explicit syntax facilitates the interpretation of mechanisms and drug response [9,10] and despite its simplicity, semi-quantitative analyses have already been performed on complex systems [11] for both cancer applications [9,12] and drug response studies [13,14], and have proved their efficacy [15,16]. ...
The study of response to cancer treatments has benefited greatly from the contribution of different omics data but their interpretation is sometimes difficult. Some mathematical models based on prior biological knowledge of signaling pathways, facilitate this interpretation but often require fitting of their parameters using perturbation data. We propose a more qualitative mechanistic approach, based on logical formalism and on the sole mapping and interpretation of omics data, and able to recover differences in sensitivity to gene inhibition without model training. This approach is showcased by the study of BRAF inhibition in patients with melanomas and colorectal cancers who experience significant differences in sensitivity despite similar omics profiles.
We first gather information from literature and build a logical model summarizing the regulatory network of the mitogen-activated protein kinase (MAPK) pathway surrounding BRAF, with factors involved in the BRAF inhibition resistance mechanisms. The relevance of this model is verified by automatically assessing that it qualitatively reproduces response or resistance behaviors identified in the literature. Data from over 100 melanoma and colorectal cancer cell lines are then used to validate the model’s ability to explain differences in sensitivity. This generic model is transformed into personalized cell line-specific logical models by integrating the omics information of the cell lines as constraints of the model. The use of mutations alone allows personalized models to correlate significantly with experimental sensitivities to BRAF inhibition, both from drug and CRISPR targeting, and even better with the joint use of mutations and RNA, supporting multi-omics mechanistic models. A comparison of these untrained models with learning approaches highlights similarities in interpretation and complementarity depending on the size of the datasets.
This parsimonious pipeline, which can easily be extended to other biological questions, makes it possible to explore the mechanistic causes of the response to treatment, on an individualized basis.
... It is well known that genes perturbing the same pathway often avoid co-mutation in tumor samples. This phenomenon is called mutual exclusivity 16,17 . Mutual exclusivity has been widely used to determine whether genes belong to the same pathway [18][19][20][21] . ...
To investigate molecular mechanism of diseases, we need to understand how genes are functionally associated. Computational researchers have tried to capture functional relationships among genes by constructing an embedding space of genes from multiple sources of high-throughput data. However, correlations in high-throughput data does not necessarily imply functional relations. In this study, we generated gene embedding from literature by constructing semantic representation for each gene. This approach enabled us to cover genes less mentioned in literature and revealed novel functional relationships among genes. Evaluation showed that the learned gene embedding was consistent with pathway knowledge and enhanced the search for cancer driver genes. We further applied our gene embedding to identify protein complexes and functional modules from gene networks. Performance in both scenarios was significantly improved with gene embedding.
... • The bladder cancer network of 35 nodes allows one to identify the deregulated pathways and their influence on bladder tumourigenesis [52]. ...
We study the target control of asynchronous Boolean networks, to identify efficacious interventions that can drive the dynamics of a given Boolean network from any initial state to the desired target attractor. Based on the application time, the control can be realised with three types of perturbations, including instantaneous, temporary and permanent perturbations. We develop efficient methods to compute the target control for a given target attractor with three types of perturbations. We compare our methods with the stable motif-based control on a variety of real-life biological networks to evaluate their performance. We show that our methods scale well for large Boolean networks and they are able to identify a rich set of solutions with a small number of perturbations.
... Logical formalism has been used in particular for its simplicity and its versatility, considering the limited available data that are used to build these models [10]. Some models focusing on signalling pathways altered in cancers [11][12][13][14], on processes related to the immune response [15,16], and on the effects of drug treatments [17][18][19] have already shown that a lot of insight can be gained with such a formalism. Some of these logical models have explored, in particular, the regulation of T cells in diverse contexts such as HIV [20], CD8+ exhaustion [9], and CD4+ activation [21]). ...
After the success of the new generation of immune therapies, immune checkpoint receptors have become one important center of attention of molecular oncologists. The initial success and hopes of anti-programmed cell death protein 1 (anti-PD1) and anti-cytotoxic T-lymphocyte-associated protein 4 (anti-CTLA4) therapies have shown some limitations since a majority of patients have continued to show resistance. Other immune checkpoints have raised some interest and are under investigation, such as T cell immunoglobulin and ITIM (immunoreceptor tyrosine-based inhibition motif) domain (TIGIT), inducible T-cell costimulator (ICOS), and T cell immunoglobulin and mucin domain-containing protein 3 (TIM3), which appear as promising targets for immunotherapy. To explore their role and study possible synergetic effects of these different checkpoints, we have built a model of T cell receptor (TCR) regulation including not only PD1 and CTLA4, but also other well studied checkpoints (TIGIT, TIM3, lymphocyte activation gene 3 (LAG3), cluster of differentiation 226 (CD226), ICOS, and tumour necrosis factor receptors (TNFRs)) and simulated different aspects of T cell biology. Our model shows good correspondence with observations from available experimental studies of anti-PD1 and anti-CTLA4 therapies and suggest effcient combinations of immune checkpoint inhibitors (ICI). Among the possible candidates, TIGIT appears to be the most promising drug target in our model. The model predicts that signal transducer and activator of transcription 1 (STAT1)/STAT4-dependent pathways, activated by cytokines such as interleukin 12 (IL12) and interferon gamma (IFNG), could improve the effect of ICI therapy via upregulation of Tbet, suggesting that the effect of the cytokines related to STAT3/STAT1 activity is dependent on the balance between STAT1 and STAT3 downstream signalling.
... This approach has been applied to the study of a wide range of networks controlling, for example, the lysis-lysogeny decision of the bacteriophage λ [45], the specification of flower organs in arabidopsis [4,29], the segmentation of drosophila embryo [27,39,40,41], the formation of compartment in drosophila imaginal disks [21,22], drosophila egg shell patterning [18], cell cycle in mammals and yeast [16,17,48], the specification of immune cells from common progenitors [12,28], the differentiation of T-helper lymphocytes [1,25,30], neural differentiation [14], as well as cancer cell fate decisions [8,19,23,37,38], etc. ...
The logical formalism is well adapted to model large cellular networks, for which detailed kinetic data are scarce. This tutorial focuses on this well-established qualitative framework. Relying on GINsim (release 3.0), a software implementing this formalism, we guide the reader step by step towards the definition, the analysis and the simulation of a four-node model of the mammalian p53-Mdm2 network.
... They are mainly divided into de novo and knowledge-based approaches, where experimental information is integrated into the algorithms. Even if many tools for knowledge-based investigation are available [2,5,7,8,12,13,15,31,34,38,40,47,48,59,61,65,78,80,83,92,96,97,100,113,116,118,119], the fact that they require information on either pathways, interaction networks, or functional phenotypes data makes their broad application limited. Hence, de novo methods will be the focus of this review. ...
Motivation:
Cancer is a complex and heterogeneous disease involving multiple somatic mutations that accumulate during its progression. In the past years, the wide availability of genomic data from patients' samples opened new perspectives in the analysis of gene mutations and alterations. Hence, visualizing and further identifying genes mutated in massive sets of patients are nowadays a critical task that sheds light on more personalized intervention approaches.
Results:
Here, we extensively review existing tools for visualization and analysis of alteration data. We compare different approaches to study mutual exclusivity and sample coverage in large-scale omics data. We complement our review with the standalone software AVAtar ('analysis and visualization of alteration data') that integrates diverse aspects known from different tools into a comprehensive platform. AVAtar supplements customizable alteration plots by a multi-objective evolutionary algorithm for subset identification and provides an innovative and user-friendly interface for the evaluation of concurrent solutions. A use case from personalized medicine demonstrates its unique features showing an application on vaccination target selection.
Availability:
AVAtar is available at: https://github.com/sysbio-bioinf/avatar.
Contact:
hans.kestler@uni-ulm.de, phone: +49 (0) 731 500 24 500, fax: +49 (0) 731 500 24 502.
... Co-occurrence of copy number gains/amplifications. For co-occurrence of CNAs a 2 × 2 contingency table was calculated for each gene where imputs were: the number of samples in altered in group 1 (for example, having a gain in NAA-LADL2), the number of samples not altered in group 1 (diploid), compared to the equivalent in group 2 (alteration in gene X) then compared using a Fisher's exact test 66 . All p values were converted to q values to account for false-discovery rate and account for multiple testing (using the qvalue package). ...
Large-scale genetic aberrations that underpin prostate cancer development and progression, such as copy-number alterations (CNAs), have been described but the consequences of specific changes in many identified loci is limited. Germline SNPs in the 3q26.31 locus are associated with aggressive prostate cancer, and is the location of NAALADL2, a gene overexpressed in aggressive disease. The closest gene to NAALADL2 is TBL1XR1, which is implicated in tumour development and progression. Using publicly-available cancer genomic data we report that NAALADL2 and TBL1XR1 gains/amplifications are more prevalent in aggressive sub-types of prostate cancer when compared to primary cohorts. In primary disease, gains/amplifications occurred in 15.99% (95% CI: 13.02–18.95) and 14.96% (95% CI: 12.08–17.84%) for NAALADL2 and TBL1XR1 respectively, increasing in frequency in higher Gleason grade and stage tumours. Gains/amplifications result in transcriptional changes and the development of a pro-proliferative and aggressive phenotype. These results support a pivotal role for copy-number gains in this genetic region. Benjamin Simpson et al. use publicly available cancer genomic data to investigate copy number changes at the 3q26.31–32 locus, which has been associated with aggressive prostate cancer based on single-nucleotide polymorphisms. They find that gains of NAALADL2 and TBL1XR1 in this locus are associated with more aggressive subtypes of prostate cancer and the transcription of pro-proliferative signalling processes.
... • The network of hematopoietic cell specification covers major transcription factors and signalling pathways for lymphoid and myeloid development [4]. • The bladder cancer network allows us to identify deregulated pathways and their influence on bladder tumourigenesis [33]. • The MAPK network is constructed to study MAPK responses to different stimuli and their contributions to cell fates [12]. ...
We study the target control problem of asynchronous Boolean networks, to identify a set of nodes, the perturbation of which can drive the dynamics of the network from any initial state to the desired steady state (or attractor). We are particularly interested in temporary perturbations, which are applied for sufficient time and then released to retrieve the original dynamics. Temporary perturbations have the apparent advantage of averting unforeseen consequences, which might be induced by permanent perturbations. Despite the infamous state-space explosion problem, in this work, we develop an efficient method to compute the temporary target control for a given target attractor of a Boolean network. We apply our method to a number of real-life biological networks and compare its performance with the stable motif-based control method to demonstrate its efficacy and efficiency.
... Hence, the deregulation of one specific pathway often leads to non-intuitive e↵ects. Mathematical modeling of such complex and intricate networks can help to understand and predict experimental results [1,2,3,4,5]. The choice of the mathematical formalism is made on the basis of the biological question, and the available data [6], and the nodes contained in the network correspond to genes or proteins whose activity shows an impact on the altered pathways and the biological responses [1]. ...
One of the aims of mathematical modeling is to understand and simulate the effects of biological perturbations and suggest ways to intervene and reestablish proper cell functioning. However, it remains a challenge, especially when considering the dynamics at the level of a cell population, with cells dying, dividing and interacting. Here, we introduce a novel framework for the dynamical modelling of cell populations packaged into a dedicated tool, UPMaBoSS. We rely on the preexisting tool MaBoSS, which enables probabilistic simulations of cellular networks, and add a novel layer to account for cell interactions and population dynamics. We illustrate our methodology by means of a case study dealing with TNF-induced cell death. Interestingly, the simulation of cell population dynamics with UPMaBoSS reveals a mechanism of resistance triggered by TNF treatment. This appoach can be applied to diverse models of cellular networks, for example to study the impact of ligand release or drug treatments on cell fate decisions, such as commitment to proliferation, differentiation, apoptosis, etc. Relatively easy to encode, UPMaBoSS simulations require only moderate computational power and execution time.
To ease the reproduction of simulations, we provide several Jupyter notebooks that can be accessed within a new release of the CoLoMoTo Docker image, which contains all required software and the example models.
Nested canalization (NC) is a property of Boolean functions which has been recently extended to multivalued functions. We study the effect of the Van Ham mapping (from multivalued to Boolean functions) on this property. We introduce the class of softly nested canalizing (SNC) multivalued functions, and prove that the Van Ham mapping sends SNC multivalued functions to NC Boolean functions. Since NC multivalued functions are SNC, this preservation property holds for NC multivalued functions as well. We also study the relevance of SNC functions in the context of gene regulatory network modelling.
Background
Boolean Networks (BNs) are a popular dynamical model in biology where the state of each component is represented by a variable taking binary values that express, for instance, activation/deactivation or high/low concentrations. Unfortunately, these models suffer from the state space explosion, i.e., there are exponentially many states in the number of BN variables, which hampers their analysis.
Results
We present Boolean Backward Equivalence (BBE), a novel reduction technique for BNs which collapses system variables that, if initialized with same value, maintain matching values in all states. A large-scale validation on 86 models from two online model repositories reveals that BBE is effective, since it is able to reduce more than 90% of the models. Furthermore, on such models we also show that BBE brings notable analysis speed-ups, both in terms of state space generation and steady-state analysis. In several cases, BBE allowed the analysis of models that were originally intractable due to the complexity. On two selected case studies, we show how one can tune the reduction power of BBE using model-specific information to preserve all dynamics of interest, and selectively exclude behavior that does not have biological relevance.
Conclusions
BBE complements existing reduction methods, preserving properties that other reduction methods fail to reproduce, and vice versa. BBE drops all and only the dynamics, including attractors, originating from states where BBE-equivalent variables have been initialized with different activation values The remaining part of the dynamics is preserved exactly, including the length of the preserved attractors, and their reachability from given initial conditions, without adding any spurious behaviours. Given that BBE is a model-to-model reduction technique, it can be combined with further reduction methods for BNs.
Epithelial to Mesenchymal Transition (EMT) plays an important role in tissue regeneration, embryonic development, and cancer metastasis. Several signaling pathways are known to regulate EMT, among which the modulation of TGFβ (Transforming Growth Factor-β) induced EMT is crucial in several cancer types. Several mathematical models were built explore the role of core regulatory circuit of ZEB/miR-200, SNAIL/miR-34 double negative feedback loops in modulating TGFβ induced EMT. Different emergent behavior including tristability, irreversible switching, existence of hybrid EMT states were inferred though these models. Some studies have explored the role of TGFβ receptor activation, SMADs nucleocytoplasmic shuttling and complex formation. Recent experiments have revealed that MDM2 along with SMAD complex regulates SNAIL expression driven EMT. Encouraged by this, in the present study we developed a mathematical model for p53/MDM2 dependent TGFβ induced EMT regulation. Inclusion of p53 brings in an additional mechanistic perspective in exploring the EM transition. The network formulated comprises a C1FFL moderating SNAIL expression involving MDM2 and SMAD complex, which functions as a noise filter and persistent detector. The C1FFL was also observed to operate as a coincidence detector driving the SNAIL dependent downstream signaling into phenotypic switching decision. Systems modeling and analysis of the devised network, displayed interesting dynamic behavior, systems response to various inputs stimulus, providing a better understanding of p53/MDM2 dependent TGF-β induced Epithelial to Mesenchymal Transition.
We study the target control of asynchronous Boolean networks, to identify interventions that can drive the dynamics of a given Boolean network from any initial state to the desired target attractor. Based on the application time, the control can be realised with three types of perturbations, including instantaneous, temporary and permanent perturbations. We develop efficient methods to compute the target control for a given target attractor with these three types of perturbations. We compare our methods with the stable motif-based control on a variety of real-life biological networks to evaluate their performance. We show that our methods scale well for large Boolean networks and they are able to identify a rich set of solutions with a small number of perturbations.
Boolean network is a modeling tool that describes a dynamic system with binary variables and their logical transition formulas. Recent studies in precision medicine use a Boolean network to discover critical genetic alterations that may lead to cancer or target genes for effective therapies to individuals. In this paper, we study a logical inference problem in a Boolean network to find all such critical genetic alterations in a minimal (parsimonious) way. We propose a bilevel integer programming model to find a single minimal genetic alteration. Using the bilevel integer programming model, we develop a branch and bound algorithm that effectively finds all of the minimal alterations. Through a computational study with eleven Boolean networks from the literature, we show that the proposed algorithm finds solutions much faster than the state-of-the-art algorithms in large data sets.
Beyond its genetic mechanisms, cancer can be understood as a network disease that often results from the interactions between different perturbations in a cellular regulatory network. The dynamics of these networks and associated signaling pathways are complex and require integrated approaches. One approach is to design mechanistic models that translate the biological knowledge of networks in mathematical terms to simulate computationally the molecular features of cancers. However, these models only reflect the general mechanisms at work in cancers.This thesis proposes to define personalized mechanistic models of cancer. A generic model is first defined in a logical (or Boolean) formalism, before using omics data (mutations, RNA, proteins) from patients or cell lines in order to make the model specific to each one profile. These personalized models can then be compared with the clinical data of patients in order to validate them. The response to treatment is investigated in particular in this thesis. The explicit representation of the molecular mechanisms by these models allows to simulate the effect of different treatments according to their targets and to verify if the sensitivity of a patient to a drug is well predicted by the corresponding personalized model. An example concerning the response to BRAF inhibitors in melanomas and colorectal cancers is thus presented.The comparison of mechanistic models of cancer, those presented in this thesis and others, with clinical data also encourages a rigorous evaluation of their possible benefits in the context of medical use. The quantification and interpretation of the prognostic value of outputs of some mechanistic models is briefly presented before focusing on the particular case of models able to recommend the best treatment for each patient according to his molecular profile. A theoretical framework is defined to extend causal inference methods to the evaluation of such precision medicine algorithms. An illustration is provided using simulated data and patient derived xenografts.All the methods and applications put forward a possible path from the design of mechanistic models of cancer to their evaluation using statistical models emulating clinical trials. As such, this thesis provides one framework for the implementation of precision medicine in oncology.
The multistep development of cancer involves the cooperation between multiple molecular lesions, as well as complex interactions between cancer cells and the surrounding tumour microenvironment. The search for these synergistic interactions using experimental models made tremendous contributions to our understanding of oncogenesis. Yet, these approaches remain labour intensive and challenging. To tackle such a hurdle, an integrative, multidisciplinary effort is required. In this article, we highlight the use of logical computational models combined to experimental validations as an effective approach to identify cooperative mechanisms and therapeutic strategies in the context of cancer biology. In silico models overcome limitations of reductionist approaches by capturing tumour complexity, and by generating powerful testable hypotheses. We review representative examples of logical models reported in the literature and their validation. We then provide further analyses of our logical model of Epithelium to Mesenchymal Transition (EMT), searching for additional cooperative interactions involving inputs from the tumour microenvironment and gain of function mutations in NOTCH.
Developing efficient computational methods to assess the impact of external interventions on the dynamics of a network model is an important problem in systems biology. This paper focuses on quantifying the global changes that result from the application of an intervention to produce a desired effect, which we define as the total effect of the intervention. The type of mathematical models that we will consider are discrete dynamical systems which include the widely used Boolean networks and their generalizations. The potential interventions can be represented by a set of nodes and edges that can be manipulated to produce a desired effect on the system. We use a class of regulatory rules called nested canalizing functions that frequently appear in published models and were inspired by the concept of canalization in evolutionary biology. In this paper, we provide a polynomial normal form based on the canalizing properties of regulatory functions. Using this polynomial normal form, we give a set of formulas for counting the maximum number of transitions that will change in the state space upon an edge deletion in the wiring diagram. These formulas rely on the canalizing structure of the target function since the number of changed transitions depends on the canalizing layer that includes the input to be deleted. We also present computations on random networks to compare the exact number of changes with the upper bounds provided by our formulas. Finally, we provide statistics on the sharpness of these upper bounds in random networks.
Background
ARID1A is a key subunit of the SWItch/Sucrose Non-Fermentable (SWI/SNF) complex which regulates dynamic repositioning of nucleosomes to repair DNA damage. Only small pilot studies have evaluated the role of ARID1A mutation in colorectal cancer (CRC). The aim of the present study was to explore the potential impact of ARID1A mutation on clinicopathological and molecular characteristics in CRC.
Methods
We used integrated data sets of 7978 CRC cases (one data set from a clinical laboratory improvement amendments [CLIA]-certified laboratory and three independent published data sets). The associations of ARID1A mutation with molecular characteristics including immune profile (the status of microsatellite instability [MSI], tumour mutational burden [TMB], programmed death ligand 1 [PD-L1] and estimated infiltrating immune cells), clinicopathological features and related pathways were analysed using next-generation sequencing, RNA sequencing and immunohistochemistry.
Results
ARID1A mutant samples had more genomically unstable tumour features (MSI-high and TMB-high) and exhibited more characteristics of a T-cell–inflamed microenvironment (PD-L1 expression and high estimated infiltrating cytotoxic T lymphocytes [CTLs]) than ARID1A wild-type samples in the discovery and validation cohorts. Even ARID1A mutant samples without MSI-high status were TMB-high, had high levels of PD-L1 expression and high estimated infiltrating CTLs. ARID1A mutations were more common with right-sided primary and earlier stage tumours. ARID1A mutant tumours mainly had co-occurring gene mutations related to chromatin modifying, DNA repair, WNT signalling and epidermal growth factor receptor inhibitor resistance pathways, and ARID1A mutations strongly regulated DNA repair pathways. Key genes for chemotherapy/radiotherapy sensitivity were suppressed in ARID1A mutant samples.
Conclusions
Our findings may provide novel insights to develop individualised approaches for treatment of CRC based on ARID1A mutation status.
Boolean networks (BNs)play a crucial role in modeling and analyzing biological systems. One of the central issues in the analysis of BNs is attractor detection, i.e., identification of all possible attractors. This problem becomes more challenging for large asynchronous random Boolean networks (ARBNs)because of the asynchronous and non-deterministic updating scheme. In this paper, we present and formally prove several relations between feedback vertex sets (FVSs)and dynamics of BNs. From these relations, we propose an FVS-based method for detecting attractors in ARBNs. Our approach relies on the principle of removing arcs in the state transition graph to get a candidate set and the reachability property to filter the candidate set. We formally prove the correctness of our method and show its efficiency by conducting experiments on real biological networks and randomly generated
$N$
-
$K$
networks. The obtained results are very promising since our method can handle large networks whose sizes are up to 101 without using any network reduction technique.
Direct cell reprogramming makes it feasible to reprogram abundant somatic cells into desired cells. It has great potential for regenerative medicine and tissue engineering. In this work, we study the control of biological networks, modelled as Boolean networks, to identify control paths driving the dynamics of the network from a source attractor (undesired cells) to the target attractor (desired cells). Instead of achieving the control in one step, we develop attractor-based sequential temporary and permanent control methods (AST and ASP) to identify a sequence of interventions that can alter the dynamics in a stepwise manner. To improve their feasibility, both AST and ASP only use biologically observable attractors as intermediates. They can find the shortest sequential control paths and guarantee reachability of the target attractor. We apply the two methods to several real-life biological networks and compare their performance with the attractor-based sequential instantaneous control (ASI). The results demonstrate that AST and ASP have the ability to identify a richer set of control paths with fewer perturbations than ASI, which will greatly facilitate practical applications.
Predicting biological systems’ behaviors requires taking into account many molecular and genetic elements for which limited information is available past a global knowledge of their pairwise interactions. Logical modeling, notably with Boolean Networks (BNs), is a well-established approach that enables reasoning on the qualitative dynamics of networks. Several dynamical interpretations of BNs have been proposed. The synchronous and (fully) asynchronous ones are the most prominent, where the value of either all or only one component can change at each step. Here we prove that, besides being costly to analyze, these usual interpretations can preclude the prediction of certain behaviors observed in quantitative systems. We introduce an execution paradigm, the Most Permissive Boolean Networks (MPBNs), which offers the formal guarantee not to miss any behavior achievable by a quantitative model following the same logic. Moreover, MPBNs significantly reduce the complexity of dynamical analysis, enabling to model genome-scale networks.
Co‐occurring and mutually exclusive gene alteration events are helpful for understanding carcinogenesis but systematic screening for such events is quite limited. We conducted pairwise‐screening tests to identify “hit pairs” in colorectal cancer (CRC) by utilizing the cross‐omics data from the Cancer Genome Atlas (TCGA). Numerous hit pairs involving somatic mutations, CNVs and DNA methylation were found to occur non‐randomly in CRC, such as KRAS and HOXB6 , SMAD4 and PMEPA1 . Based on these hit pairs, we identified 32 synthetic lethal pairs and 7,527 co‐occurring pairs relating to drug response. Our further biological experiments showed that the co‐occurrence of mutant FCGBP and NUDT12 silencing (or mutant TMC3 and RPS6KA6 silencing) with siRNA reduced cell viability. Moreover, novel hit pairs could influence the prognosis. The patients who carried concurrent mutations of IRF5 and NEFH , SYNE1 and TTN , or MUC16 and NEFH had worse survival outcomes. Particularly, the presence of mutant SYNE1 and TTN pair not only affects prognosis, but also is related to CRC patients' response to drug treatment. Our “hit‐pair” genes may provide insights into colorectal carcinogenesis and help open new avenues for CRC therapy.
This article is protected by copyright. All rights reserved.
Gastric cancer is a leading cause of cancer deaths, but analysis of its molecular and clinical characteristics has been complicated by histological and aetiological heterogeneity. Here we describe a comprehensive molecular evaluation of 295 primary gastric adenocarcinomas as part of The Cancer Genome Atlas (TCGA) project. We propose a molecular classification dividing gastric cancer into four subtypes: tumours positive for Epstein–Barr virus, which display recurrent PIK3CA mutations, extreme DNA hypermethylation, and amplification of JAK2, CD274 (also known as PD-L1) and PDCD1LG2 (also known as PD-L2); microsatellite unstable tumours, which show elevated mutation rates, including mutations of genes encoding targetable oncogenic signalling proteins; genomically stable tumours, which are enriched for the diffuse histological variant and mutations of RHOA or fusions involving RHO-family GTPase-activating proteins; and tumours with chromosomal instability, which show marked aneuploidy and focal amplification of receptor tyrosine kinases. Identification of these subtypes provides a roadmap for patient stratification and trials of targeted therapies.
Motivation: Models of discrete concurrent systems often lead to huge and
complex state transition graphs that represent their dynamics. This makes
difficult to analyse dynamical properties. In particular, for logical models of
biological regulatory networks, it is of real interest to study attractors and
their reachability from specific initial conditions, i.e. to assess the
potential asymptotical behaviours of the system. Beyond the identification of
the reachable attractors, we propose to quantify this reachability.
Results: Relying on the structure of the state transition graph, we estimate
the probability of each attractor reachable from a given initial condition or
from a portion of the state space. First, we present a quasi-exact solution
with an original algorithm called Firefront, based on the exhaustive
exploration of the reachable state space. Then, we introduce an adapted version
of Monte Carlo simulation algorithm, termed Avatar, better suited to larger
models. Firefront and Avatar methods are validated and compared to other
related approaches, using as test cases logical models of synthetic and
biological networks.
Availability: Both algorithms are implemented as Perl scripts that can be
freely downloaded from http://compbio.igc.gulbenkian.pt/nmd/node/59 along with
Supplementary Material.
Epithelial-to-mesenchymal transition (EMT) is a developmental process hijacked by cancer cells to leave the primary tumor site, invade surrounding tissue, and establish distant metastases. A hallmark of EMT is the loss of E-cadherin expression, and one major signal for the induction of EMT is transforming growth factor beta (TGFβ), which is dysregulated in up to 40% of hepatocellular carcinoma (HCC). We have constructed an EMT network of 70 nodes and 135 edges by integrating the signaling pathways involved in developmental EMT and known dysregulations in invasive HCC. We then used discrete dynamic modeling to understand the dynamics of the EMT network driven by TGFβ. Our network model recapitulates known dysregulations during the induction of EMT and predicts the activation of the Wnt and Sonic hedgehog (SHH) signaling pathways during this process. We show, across multiple murine (P2E and P2M) and human HCC cell lines (Huh7, PLC/PRF/5, HLE, and HLF), that the TGFβ signaling axis is a conserved driver of mesenchymal phenotype HCC and confirm that Wnt and SHH signaling are induced in these cell lines. Furthermore, we identify by network analysis eight regulatory feedback motifs that stabilize the EMT process and show that these motifs involve cross-talk among multiple major pathways. Our model will be useful in identifying potential therapeutic targets for the suppression of EMT, invasion and metastasis in HCC.
It has been widely realized that pathways rather than individual genes govern the course of carcinogenesis. Therefore, discovering driver pathways is becoming an important step to understand the molecular mechanisms underlying cancer and design efficient treatments for cancer patients. Previous studies have focused mainly on observation of the alterations in cancer genomes at the individual gene or single pathway level. However, a great deal of evidence has indicated that multiple pathways often function cooperatively in carcinogenesis and other key biological processes.
In this study, an exact mathematical programming method was proposed to de novo identify co-occurring mutated driver pathways (CoMDP) in carcinogenesis without any prior information beyond mutation profiles. Two possible properties of mutations that occurred in cooperative pathways were exploited to achieve this: (1) each individual pathway has high coverage and high exclusivity; and (2) the mutations between the pair of pathways showed statistically significant co-occurrence. The efficiency of CoMDP was validated first by testing on simulated data and comparing it with a previous method. Then CoMDP was applied to several real biological data including glioblastoma, lung adenocarcinoma, and ovarian carcinoma datasets. The discovered co-occurring driver pathways were here found to be involved in several key biological processes, such as cell survival and protein synthesis. Moreover, CoMDP was modified to (1) identify an extra pathway co-occurring with a known pathway and (2) detect multiple significant co-occurring driver pathways for carcinogenesis.
The present method can be used to identify gene sets with more biological relevance than the ones currently used for the discovery of single driver pathways.
Muscle-invasive bladder carcinoma (MIBC) constitutes a heterogeneous group of tumors with a poor outcome. Molecular stratification of MIBC may identify clinically relevant tumor subgroups and help to provide effective targeted therapies. From seven series of large-scale transcriptomic data (383 tumors), we identified an MIBC subgroup accounting for 23.5% of MIBC, associated with shorter survival and displaying a basal-like phenotype, as shown by the expression of epithelial basal cell markers. Basal-like tumors presented an activation of the epidermal growth factor receptor (EGFR) pathway linked to frequent EGFR gains and activation of an EGFR autocrine loop. We used a 40-gene expression classifier derived from human tumors to identify human bladder cancer cell lines and a chemically induced mouse model of bladder cancer corresponding to human basal-like bladder cancer. We showed, in both models, that tumor cells were sensitive to anti-EGFR therapy. Our findings provide preclinical proof of concept that anti-EGFR therapy can be used to target a subset of particularly aggressive MIBC tumors expressing basal cell markers and provide diagnostic tools for identifying these tumors.
Urothelial carcinoma of the bladder is a common malignancy that causes approximately 150,000 deaths per year worldwide. So far, no molecularly targeted agents have been approved for treatment of the disease. As part of The Cancer Genome Atlas project, we report here an integrated analysis of 131 urothelial carcinomas to provide a comprehensive landscape of molecular alterations. There were statistically significant recurrent mutations in 32 genes, including multiple genes involved in cell-cycle regulation, chromatin regulation, and kinase signalling pathways, as well as 9 genes not previously reported as significantly mutated in any cancer. RNA sequencing revealed four expression subtypes, two of which (papillary-like and basal/squamous-like) were also evident in microRNA sequencing and protein data. Whole-genome and RNA sequencing identified recurrent in-frame activating FGFR3-TACC3 fusions and expression or integration of several viruses (including HPV16) that are associated with gene inactivation. Our analyses identified potential therapeutic targets in 69% of the tumours, including 42% with targets in the phosphatidylinositol-3-OH kinase/AKT/mTOR pathway and 45% with targets (including ERBB2) in the RTK/MAPK pathway. Chromatin regulatory genes were more frequently mutated in urothelial carcinoma than in any other common cancer studied so far, indicating the future possibility of targeted therapy for chromatin abnormalities.
In large collections of tumor samples, it has been observed that sets of genes that are commonly involved in the same cancer pathways tend not to occur mutated together in the same patient. Such gene sets form mutually exclusive patterns of gene alterations in cancer genomic data. Computational approaches that detect mutually exclusive gene sets, rank and test candidate alteration patterns by rewarding the number of samples the pattern covers and by punishing its impurity, i.e., additional alterations that violate strict mutual exclusivity. However, the extant approaches do not account for possible observation errors. In practice, false negatives and especially false positives can severely bias evaluation and ranking of alteration patterns. To address these limitations, we develop a fully probabilistic, generative model of mutual exclusivity, explicitly taking coverage, impurity, as well as error rates into account, and devise efficient algorithms for parameter estimation and pattern ranking. Based on this model, we derive a statistical test of mutual exclusivity by comparing its likelihood to the null model that assumes independent gene alterations. Using extensive simulations, the new test is shown to be more powerful than a permutation test applied previously. When applied to detect mutual exclusivity patterns in glioblastoma and in pan-cancer data from twelve tumor types, we identify several significant patterns that are biologically relevant, most of which would not be detected by previous approaches. Our statistical modeling framework of mutual exclusivity provides increased flexibility and power to detect cancer pathways from genomic alteration data in the presence of noise. A summary of this paper appears in the proceedings of the RECOMB 2014 conference, April 2-5.
The Mitogen-Activated Protein Kinase (MAPK) network consists of tightly interconnected signalling pathways involved in diverse cellular processes, such as cell cycle, survival, apoptosis and differentiation. Although several studies reported the involvement of these signalling cascades in cancer deregulations, the precise mechanisms underlying their influence on the balance between cell proliferation and cell death (cell fate decision) in pathological circumstances remain elusive. Based on an extensive analysis of published data, we have built a comprehensive and generic reaction map for the MAPK signalling network, using CellDesigner software. In order to explore the MAPK responses to different stimuli and better understand their contributions to cell fate decision, we have considered the most crucial components and interactions and encoded them into a logical model, using the software GINsim. Our logical model analysis particularly focuses on urinary bladder cancer, where MAPK network deregulations have often been associated with specific phenotypes. To cope with the combinatorial explosion of the number of states, we have applied novel algorithms for model reduction and for the compression of state transition graphs, both implemented into the software GINsim. The results of systematic simulations for different signal combinations and network perturbations were found globally coherent with published data. In silico experiments further enabled us to delineate the roles of specific components, cross-talks and regulatory feedbacks in cell fate decision. Finally, tentative proliferative or anti-proliferative mechanisms can be connected with established bladder cancer deregulations, namely Epidermal Growth Factor Receptor (EGFR) over-expression and Fibroblast Growth Factor Receptor 3 (FGFR3) activating mutations.
Reactome (http://www.reactome.org) is a manually curated open-source open-data resource of human pathways and reactions. The current version 46 describes 7088
human proteins (34% of the predicted human proteome), participating in 6744 reactions based on data extracted from 15 107
research publications with PubMed links. The Reactome Web site and analysis tool set have been completely redesigned to increase
speed, flexibility and user friendliness. The data model has been extended to support annotation of disease processes due
to infectious agents and to mutation.
Background:
Preclinical studies have shown that PTEN loss enhances sensitivity to mammalian target of Rapamycin (mTOR) inhibitors because of facilitated PI3K (phosphatidylinositol-3 kinase)/Akt activation and consecutive stimulation of the mTOR pathway. In patients with advanced transitional cell carcinoma (TCC) treated with the mTOR inhibitor everolimus, PTEN loss was, however, associated with resistance to treatment.
Methods:
Transitional cell carcinoma specimens, human bladder cancer cells and derived mouse xenografts were used to evaluate how the PTEN status influences the activity of mTOR inhibitors.
Results:
Transitional cell carcinoma patients with a shorter progression-free survival under everolimus exhibited PTEN deficiency and increased Akt activation. Moreover, PTEN-deficient bladder cancer cells were less sensitive to rapamycin than cells expressing wild-type PTEN, and rapamycin strikingly induced Akt activation in the absence of functional PTEN. Inhibition of Akt activation by the PI3K inhibitor wortmannin interrupted this rapamycin-induced feedback loop, thereby enhancing the antiproliferative effects of the mTOR inhibitor both in vitro and in vivo.
Conclusion:
Facilitation of Akt activation upon PTEN loss can have a more prominent role in driving the feedback loop in response to mTOR inhibition than in promoting the mTOR pathway. These data support the use of both PI3K and mTOR inhibitors to treat urothelial carcinoma, in particular in the absence of functional PTEN.
PURPOSEWe sought to define the prevalence and co-occurrence of actionable genomic alterations in patients with high-grade bladder cancer to serve as a platform for therapeutic drug discovery. PATIENTS AND METHODS
An integrative analysis of 97 high-grade bladder tumors was conducted to identify actionable drug targets, which are defined as genomic alterations that have been clinically validated in another cancer type (eg, BRAF mutation) or alterations for which a selective inhibitor of the target or pathway is under clinical investigation. DNA copy number alterations (CNAs) were defined by using array comparative genomic hybridization. Mutation profiling was performed by using both mass spectroscopy-based genotyping and Sanger sequencing.ResultsSixty-one percent of tumors harbored potentially actionable genomic alterations. A core pathway analysis of the integrated data set revealed a nonoverlapping pattern of mutations in the RTK-RAS-RAF and phosphoinositide 3-kinase/AKT/mammalian target of rapamycin pathways and regulators of G1-S cell cycle progression. Unsupervised clustering of CNAs defined two distinct classes of bladder tumors that differed in the degree of their CNA burden. Integration of mutation and copy number analyses revealed that mutations in TP53 and RB1 were significantly more common in tumors with a high CNA burden (P < .001 and P < .003, respectively). CONCLUSION
High-grade bladder cancer possesses substantial genomic heterogeneity. The majority of tumors harbor potentially tractable genomic alterations that may predict for response to target-selective agents. Given the genomic diversity of bladder cancers, optimal development of target-specific agents will require pretreatment genomic characterization.
It is poorly understood how driver mutations in cancer genes work together to promote tumor development. Renal cell carcinoma (RCC) offers a unique opportunity to study complex relationships among cancer genes. The four most commonly mutated genes in RCC of clear-cell type (the most common type) are two-hit tumor suppressor genes, and they cluster in a 43-Mb region on chromosome 3p that is deleted in approximately 90% of tumors: VHL (mutated in ∼80%), PBRM1 (∼50%), BAP1 (∼15%), and SETD2 (∼15%). Meta-analyses that we conducted show that mutations in PBRM1 and SETD2 co-occur in tumors at a frequency higher than expected by chance alone, indicating that these mutations may cooperate in tumorigenesis. In contrast, consistent with our previous results, mutations in PBRM1 and BAP1 tend to be mutually exclusive. Mutation exclusivity analyses (often confounded by lack of statistical power) raise the possibility of functional redundancy. However, mutation exclusivity may indicate negative genetic interactions, as proposed herein for PBRM1 and BAP1, and mutations in these genes define RCC with different pathologic features, gene expression profiles, and outcomes. Negative genetic interactions among cancer genes point toward broader context dependencies of cancer gene action beyond tissue dependencies. An enhanced understanding of cancer gene dependencies may help to unravel vulnerabilities that can be exploited therapeutically. Cancer Res; 73(14); 1-7. ©2013 AACR.
TP53 and FGFR3 mutations are the most common mutations in bladder cancers. FGFR3 mutations are most frequent in low-grade low-stage tumours, whereas TP53 mutations are most frequent in high-grade high-stage tumours. Several studies have reported FGFR3 and TP53 mutations to be mutually exclusive events, whereas others have reported them to be independent. We carried out a meta-analysis of published findings for FGFR3 and TP53 mutations in bladder cancer (535 tumours, 6 publications) and additional unpublished data for 382 tumours. TP53 and FGFR3 mutations were not independent events for all tumours considered together (OR = 0.25 [0.18-0.37], p = 0.0001) or for pT1 tumours alone (OR = 0.47 [0.28-0.79], p = 0.0009). However, if the analysis was restricted to pTa tumours or to muscle-invasive tumours alone, FGFR3 and TP53 mutations were independent events (OR = 0.56 [0.23-1.36] (p = 0.12) and OR = 0.99 [0.37-2.7] (p = 0.35), respectively). After stratification of the tumours by stage and grade, no dependence was detected in the five tumour groups considered (pTaG1 and pTaG2 together, pTaG3, pT1G2, pT1G3, pT2-4). These differences in findings can be attributed to the putative existence of two different pathways of tumour progression in bladder cancer: the CIS pathway, in which FGFR3 mutations are rare, and the Ta pathway, in which FGFR3 mutations are frequent. TP53 mutations occur at the earliest stage of the CIS pathway, whereas they occur would much later in the Ta pathway, at the T1G3 or muscle-invasive stage.
Unlabelled:
Mathematical modeling is used as a Systems Biology tool to answer biological questions, and more precisely, to validate a network that describes biological observations and predict the effect of perturbations. This article presents an algorithm for modeling biological networks in a discrete framework with continuous time.
Background:
There exist two major types of mathematical modeling approaches: (1) quantitative modeling, representing various chemical species concentrations by real numbers, mainly based on differential equations and chemical kinetics formalism; (2) and qualitative modeling, representing chemical species concentrations or activities by a finite set of discrete values. Both approaches answer particular (and often different) biological questions. Qualitative modeling approach permits a simple and less detailed description of the biological systems, efficiently describes stable state identification but remains inconvenient in describing the transient kinetics leading to these states. In this context, time is represented by discrete steps. Quantitative modeling, on the other hand, can describe more accurately the dynamical behavior of biological processes as it follows the evolution of concentration or activities of chemical species as a function of time, but requires an important amount of information on the parameters difficult to find in the literature.
Results:
Here, we propose a modeling framework based on a qualitative approach that is intrinsically continuous in time. The algorithm presented in this article fills the gap between qualitative and quantitative modeling. It is based on continuous time Markov process applied on a Boolean state space. In order to describe the temporal evolution of the biological process we wish to model, we explicitly specify the transition rates for each node. For that purpose, we built a language that can be seen as a generalization of Boolean equations. Mathematically, this approach can be translated in a set of ordinary differential equations on probability distributions. We developed a C++ software, MaBoSS, that is able to simulate such a system by applying Kinetic Monte-Carlo (or Gillespie algorithm) on the Boolean state space. This software, parallelized and optimized, computes the temporal evolution of probability distributions and estimates stationary distributions.
Conclusions:
Applications of the Boolean Kinetic Monte-Carlo are demonstrated for three qualitative models: a toy model, a published model of p53/Mdm2 interaction and a published model of the mammalian cell cycle. Our approach allows to describe kinetic phenomena which were difficult to handle in the original models. In particular, transient effects are represented by time dependent probability distributions, interpretable in terms of cell populations.
Similar to other malignancies, urothelial carcinoma (UC) is characterized by specific recurrent chromosomal aberrations and gene mutations. However, the interconnection between specific genomic alterations, and how patterns of chromosomal alterations adhere to different molecular subgroups of UC, is less clear. We applied tiling resolution array CGH to 146 cases of UC and identified a number of regions harboring recurrent focal genomic amplifications and deletions. Several potential oncogenes were included in the amplified regions, including known oncogenes like E2F3, CCND1, and CCNE1, as well as new candidate genes, such as SETDB1 (1q21), and BCL2L1 (20q11). We next combined genome profiling with global gene expression, gene mutation, and protein expression data and identified two major genomic circuits operating in urothelial carcinoma. The first circuit was characterized by FGFR3 alterations, overexpression of CCND1, and 9q and CDKN2A deletions. The second circuit was defined by E3F3 amplifications and RB1 deletions, as well as gains of 5p, deletions at PTEN and 2q36, 16q, 20q, and elevated CDKN2A levels. TP53/MDM2 alterations were common for advanced tumors within the two circuits. Our data also suggest a possible RAS/RAF circuit. The tumors with worst prognosis showed a gene expression profile that indicated a keratinized phenotype. Taken together, our integrative approach revealed at least two separate networks of genomic alterations linked to the molecular diversity seen in UC, and that these circuits may reflect distinct pathways of tumor development.
Although individual tumors of the same clinical type have surprisingly diverse genomic alterations, these events tend to occur in a limited number of pathways, and alterations that affect the same pathway tend to not co-occur in the same patient. While pathway analysis has been a powerful tool in cancer genomics, our knowledge of oncogenic pathway modules is incomplete. To systematically identify such modules, we have developed a novel method, Mutual Exclusivity Modules in cancer (MEMo). The method uses correlation analysis and statistical tests to identify network modules by three criteria: (1) Member genes are recurrently altered across a set of tumor samples; (2) member genes are known to or are likely to participate in the same biological process; and (3) alteration events within the modules are mutually exclusive. Applied to data from the Cancer Genome Atlas (TCGA), the method identifies the principal known altered modules in glioblastoma (GBM) and highlights the striking mutual exclusivity of genomic alterations in the PI(3)K, p53, and Rb pathways. In serous ovarian cancer, we make the novel observation that inactivation of BRCA1 and BRCA2 is mutually exclusive of amplification of CCNE1 and inactivation of RB1, suggesting distinct alternative causes of genomic instability in this cancer type; and, we identify RBBP8 as a candidate oncogene involved in Rb-mediated cell cycle control. When applied to any cancer genomics data set, the algorithm can nominate oncogenic alterations that have a particularly strong selective effect and may also be useful in the design of therapeutic combinations in cases where mutual exclusivity reflects synthetic lethality.
Substantial effort in recent years has been devoted to constructing and analyzing large-scale gene and protein networks on the basis of "omic" data and literature mining. These interaction graphs provide valuable insight into the topologies of complex biological networks but are rarely context specific and cannot be used to predict the responses of cell signaling proteins to specific ligands or drugs. Conversely, traditional approaches to analyzing cell signaling are narrow in scope and cannot easily make use of network-level data. Here, we combine network analysis and functional experimentation by using a hybrid approach in which graphs are converted into simple mathematical models that can be trained against biochemical data. Specifically, we created Boolean logic models of immediate-early signaling in liver cells by training a literature-based prior knowledge network against biochemical data obtained from primary human hepatocytes and 4 hepatocellular carcinoma cell lines exposed to combinations of cytokines and small-molecule kinase inhibitors. Distinct families of models were recovered for each cell type, and these families clustered topologically into normal and diseased sets.
In the present investigation, we sought to refine the classification of urothelial carcinoma by combining information on gene expression, genomic, and gene mutation levels. For these purposes, we performed gene expression analysis of 144 carcinomas, and whole genome array-CGH analysis and mutation analyses of FGFR3, PIK3CA, KRAS, HRAS, NRAS, TP53, CDKN2A, and TSC1 in 103 of these cases. Hierarchical cluster analysis identified two intrinsic molecular subtypes, MS1 and MS2, which were validated and defined by the same set of genes in three independent bladder cancer data sets. The two subtypes differed with respect to gene expression and mutation profiles, as well as with the level of genomic instability. The data show that genomic instability was the most distinguishing genomic feature of MS2 tumors, and that this trait was not dependent on TP53/MDM2 alterations. By combining molecular and pathologic data, it was possible to distinguish two molecular subtypes of T(a) and T(1) tumors, respectively. In addition, we define gene signatures validated in two independent data sets that classify urothelial carcinoma into low-grade (G(1)/G(2)) and high-grade (G(3)) tumors as well as non-muscle and muscle-invasive tumors with high precisions and sensitivities, suggesting molecular grading as a relevant complement to standard pathologic grading. We also present a gene expression signature with independent prognostic effect on metastasis and disease-specific survival. We conclude that the combination of molecular and histopathologic classification systems might provide a strong improvement for bladder cancer classification and produce new insights into the development of this tumor type.
This study assessed the human epidermal growth factor receptor-2 (HER2) protein expression and its relationship with gene amplification in invasive bladder carcinoma, using the same criteria than for breast cancer.
In 1005 patients, paraffin-embedded tissues of transurethral resection or cystectomy were evaluated by immunohistochemistry (IHC), using antibodies against HER2. All samples with a 2+ or 3+ HER2 overexpression were evaluated by FISH.
HER2 overexpression was observed in 93 (9.2%) tumors (2+: 42 tumors and 3+: 51 tumors). Using FISH, all HER2 3+ tumors had a gene amplification, whereas no amplification was found in 2+ tumors. Intratumoral heterogeneity was observed in 35% of cases. These tumors showed the same heterogeneous pattern, with adjacent 3+ positive and negative areas by both IHC and FISH.
This study showed that 5.1% of invasive bladder carcinomas had a HER2 gene amplification. These findings may have clinical implications for the management of patients with HER2-positive locally advanced or metastatic bladder cancer, as they could be potential candidates for targeted therapy.
Cancer is now appreciated as not only a highly heterogenous pathology with respect to cell type and tissue origin but also as a disease involving dysregulation of multiple pathways governing fundamental cell processes such as death, proliferation, differentiation and migration. Thus, the activities of molecular networks that execute metabolic or cytoskeletal processes, or regulate these by signal transduction, are altered in a complex manner by diverse genetic mutations in concert with the environmental context. A major challenge therefore is how to develop actionable understanding of this multivariate dysregulation, with respect both to how it arises from diverse genetic mutations and to how it may be ameliorated by prospective treatments. While high-throughput experimental platform technologies ranging from genomic sequencing to transcriptomic, proteomic and metabolomic profiling are now commonly used for molecular-level characterization of tumor cells and surrounding tissues, the resulting data sets defy straightforward intuitive interpretation with respect to potential therapeutic targets or the effects of perturbation. In this review article, we will discuss how significant advances can be obtained by applying computational modeling approaches to elucidate the pathways most critically involved in tumor formation and progression, impact of particular mutations on pathway operation, consequences of altered cell behavior in tissue environments and effects of molecular therapeutics.
Senescence is the process of cellular aging dependent on the normal physiological functions of non-immortalized cells. With increasing data being uncovered in this field, the complex molecular web regulating senescence is gradually being unraveled. Recent studies have suggested two main phases of senescence, the triggering of senescence and the maintenance of senescence. Each has been supported by data implying precise roles for DNA methyltransferases, reactive oxygen species and other factors. We will first summarize the data supporting these claims and then highlight the specific role that we hypothesize that p130/Rbl2 plays in the modulation of the senescence process.
In breast cancer, overexpression of the transmembrane tyrosine kinase ERBB2 is an adverse prognostic marker, and occurs in almost 30% of the patients. For therapeutic intervention, ERBB2 is targeted by monoclonal antibody trastuzumab in adjuvant settings; however, de novo resistance to this antibody is still a serious issue, requiring the identification of additional targets to overcome resistance. In this study, we have combined computational simulations, experimental testing of simulation results, and finally reverse engineering of a protein interaction network to define potential therapeutic strategies for de novo trastuzumab resistant breast cancer.
First, we employed Boolean logic to model regulatory interactions and simulated single and multiple protein loss-of-functions. Then, our simulation results were tested experimentally by producing single and double knockdowns of the network components and measuring their effects on G1/S transition during cell cycle progression. Combinatorial targeting of ERBB2 and EGFR did not affect the response to trastuzumab in de novo resistant cells, which might be due to decoupling of receptor activation and cell cycle progression. Furthermore, examination of c-MYC in resistant as well as in sensitive cell lines, using a specific chemical inhibitor of c-MYC (alone or in combination with trastuzumab), demonstrated that both trastuzumab sensitive and resistant cells responded to c-MYC perturbation.
In this study, we connected ERBB signaling with G1/S transition of the cell cycle via two major cell signaling pathways and two key transcription factors, to model an interaction network that allows for the identification of novel targets in the treatment of trastuzumab resistant breast cancer. Applying this new strategy, we found that, in contrast to trastuzumab sensitive breast cancer cells, combinatorial targeting of ERBB receptors or of key signaling intermediates does not have potential for treatment of de novo trastuzumab resistant cells. Instead, c-MYC was identified as a novel potential target protein in breast cancer cells.
Noninvasive transitional cell carcinomas of the bladder can have two distinct morphologies suggesting they contain different genetic alterations. Papillary transitional cell carcinomas (T(a) tumors) are often multifocal and only occasionally progress, whereas flat tumors (carcinomas in situ, CIS), frequently progress to invasive disease. We examined 216 bladder tumors of various stages and histopathologies for two genetic alterations previously described to be of importance in bladder tumorigenesis. Loss of heterozygosity of chromosome 9 was observed in 24 of 70 (34%) T(a) tumors but was present in only 3 of 24 (12%) CIS and dysplasia lesions (P = 0.04). In contrast, only 1 of 36 (3%) T(a) tumors contained a p53 gene mutation compared to 15 of 23 (65%) CIS and dysplasias (P < 0.001), a frequency comparable to that observed in muscle invasive tumors (25 of 49; 51%). The presence of p53 mutations in CIS and dysplasia could explain their propensities to progress since these mutations are known to destabilize the genome. Analysis of several tumor pairs involving a CIS and an invasive cancer provided evidence that the chromosome 9 alteration may in some cases be involved in the progression of CIS to more invasive tumors, in addition to its role in the initiation of T(a) tumors. However, the CIS and secondary tumor were found to contain different genetic alterations in some patients suggesting divergent progression pathways. Bladder carcinogenesis may therefore proceed through two distinct genetic alteration pathways responsible for generating superficial tumors with differing morphologies and pathologies.
Cyclin D1 and p16INK4A are molecules with pivotal roles in cell cycle control and the development of diverse human cancers, and overexpression of cyclin D1 and loss of p16INK4A expression are common genetic events in head and neck squamous cell carcinoma. The prognostic significance of these molecular events at different sites within the head and neck, however, remains controversial. Thus, we sought to determine the relationship between cyclin D1 and/or p16INK4A expression and disease outcome in squamous cell carcinoma of the anterior tongue. Immunohistochemical detection of nuclear proteins cyclin D1, p53, and p16INK4A, and the Ki-67 labeling index was undertaken in tissue sections from 148 tongue cancers treated by surgical resection. Nuclear antigen status was analyzed in relation to pathological variables, tumor recurrence, and patient survival. Statistical significance was assessed using chi2 analysis for pathological variables and the Kaplan-Meier method, log rank test, and the Cox proportional hazards model for survival parameters. Overexpression of cyclin D1 occurred in 68% of tumors (100 of 147) and was associated with increased lymph node stage (P = 0.014), increased tumor grade (P = 0.003), and reduced disease-free (P = 0.006) and overall (P = 0.01) survival. Loss of p16INK4A expression was demonstrated in 55% of tumors (78 of 143) and was associated with reduced disease-free (P = 0.007) and overall (P = 0.014) survival. Multivariate analysis confirmed that in addition to pathological stage and regional lymph node status, cyclin D1 overexpression and loss of p16INK4A expression are independent predictors of death from tongue cancer. Loss of p16INK4A in the presence of cyclin D1 overexpression conferred a significantly worse disease-free (P = 0.011) and overall (P = 0.002) survival at 5 years. p53 nuclear accumulation and the Ki-67 labeling index were not prognostic. These data indicate that cyclin D1 overexpression and loss of p16INK4A expression predict early relapse and reduced survival in squamous cell carcinoma of the anterior tongue. Simultaneous assessment of cyclin D1 and p16INK4A protein levels define subgroups of patients at increased risk of relapse and may be of clinical utility in optimizing therapy.
Smoking is a major risk factor for urothelial cell carcinoma of the bladder (UCC). Mutations in the FGFR3 and TP53 genes have been shown to define two distinct pathways in superficial papillary and invasive UCC disease, respectively. We investigated the relationship between smoking and these mutations by means of denaturing high performance liquid chromatography and sequencing for 110 primary UCC of the bladder. This study included 48 current smokers, 31 ex-smokers and 31 non-smokers. Thirty-five of the tumors were stage pTa, 40 pT1 and 35 > or =pT2. Fourteen of the tumors were grade 1, 37 were grade 2 and 59 grade 3. Smoking was associated with high stage (P = 0.03) and high grade tumors (P = 0.006). Twenty-two of the 110 tumors studied harbored TP53 mutations (20%) and 43 harbored FGFR3 mutations (39%). Odds ratios (OR) were higher for TP53 mutations in current smokers [OR, 2.25; 95% confidence interval (95% CI), 0.65-7.75] and ex-smokers (OR, 1.62; 95% CI, 0.41-6.42) than in non-smokers. Double TP53 mutations and the A:T-->G:C TP53 mutation pattern was found only in current smokers. Patients with the FGFR3(wild-type)/TP53(mutated) genotype had significantly higher levels of tobacco consumption, as measured in pack-years (P = 0.01). Smoking influenced neither the frequency nor the pattern of FGFR3 mutations. Our results suggest that smoking is associated with invasive and high grade UCCs, at initial presentation, and influenced TP53 or the molecular pathway defined by these mutations. In contrast, FGFR3 mutations are not affected by smoking and probably result from endogenous alterations. These data have potential implications for clinical management and prevention strategies.
Fibroblast growth factor receptor 3 (FGFR3) mutations are frequent in superficial urothelial cell carcinoma (UCC). Ras gene mutations are also found in UCC. As oncogenic activation of both FGFR3 and Ras is predicted to result in stimulation of the mitogen-activated protein kinase (MAPK) pathway, we hypothesized that these might be mutually exclusive events. HRAS mutation has been widely studied in UCC, but all three Ras gene family members have not been screened for mutation in the same sample series. We screened 98 bladder tumours and 31 bladder cell lines for mutations in FGFR3, HRAS, NRAS and KRAS2. FGFR3 mutations were present in 54 tumours (55%) and three cell lines (10%), and Ras gene mutations in 13 tumours (13%) and four cell lines (13%). These included mutations in all three Ras genes; ten in HRAS, four in KRAS2 and four in NRAS and these were not associated with either tumour grade or stage. In no cases were Ras and FGFR3 mutation found together. This mutual exclusion suggests that FGFR3 and Ras gene mutation may represent alternative means to confer the same phenotype on UCC cells. If these events have biological equivalence, Ras mutant invasive UCC may represent a novel subgroup.
Mutations in the fibroblast growth factor receptor 3 (FGFR3) occur in 50% of primary bladder tumors. An FGFR3 mutation is associated with good prognosis, illustrated by significantly lower percentage of patients with progression and disease-specific mortality. FGFR3 mutations are especially prevalent in low grade/stage tumors, with pTa tumors harboring mutations in 85% of the cases. These tumors recur in 70% of patients. Efficient FGFR3 mutation detection for prognostic purposes and for detection of recurrences in urine is an important clinical issue. In this paper, we describe a simple assay for the simultaneous detection of nine different FGFR3 mutations.
The assay consists of one multiplex PCR, followed by extension of primers for each mutation with a labeled dideoxynucleotide. The extended primers are separated by capillary electrophoresis, and the identity of the incorporated nucleotide indicates the presence or absence of a mutation.
The assay was found to be more sensitive than single-strand conformation polymorphism analysis. Mutations could still be detected with an input of only 1 ng of genomic DNA and in a 20-fold excess of wild-type DNA. Moreover, in urine samples from patients with a mutant tumor, the sensitivity of mutation detection was 62%.
We have developed a fast, easy to use assay for the simultaneous detection of FGFR3 mutations, which can be of assistance in clinical decision-making and as an alternative for the follow-up of patients by invasive cystoscopy for the detection of recurrences in urine.
Bladder tumors constitute a very heterogeneous disease. Superficial tumors are characterized by a high prevalence of FGFR3 mutations and chromosome 9 alterations. High-grade and muscle-invasive tumors are characterized by Tp53 mutations and aneuploidy. We have analyzed the sequence of exons 9 and 20 of PIK3CA in a panel of bladder tumors covering the whole spectrum of the disease. DNA from formalin-fixed, paraffin-embedded tumor sections was amplified by PCR and products were sequenced. In an unselected panel of tumors representative of the disease, the PIK3CA mutation prevalence was 13% (11 of 87). Mutations occurred mainly at the previously identified hotspots (codons 542, 545, 1007, and 1047). The distribution according to stage was as follows: papillary urothelial neoplasms of uncertain malignant potential (PUNLMP; 11 of 43, 25.6%), T(a) (9 of 57, 16%), T(1) (2 of 10, 20%), and muscle-invasive tumors (0 of 20, 0%; P = 0.019). Mutations were associated with low-grade tumors: grade 1 (6 of 27, 22.2%), grade 2 (3 of 23, 13%), and grade 3 (2 of 37, 5.4%; P = 0.047). Overall, PIK3CA mutations were strongly associated with FGFR3 mutations: 18 of 69 (26%) FGFR3(mut) tumors were PIK3CA(mut), versus 4 of 58 (6.9%) FGFR3(wt) tumors (P = 0.005). Our findings indicate that PIK3CA mutations are a common event that can occur early in bladder carcinogenesis and support the notion that papillary and muscle-invasive tumors arise through different molecular pathways. PIK3CA may constitute a novel diagnostic and prognostic tool, as well as a therapeutic target, in bladder cancer.
Based upon observations on 48 cases of retinoblastoma and published reports, the hypothesis is developed that retinoblastoma is a cancer caused by two mutational events. In the dominantly inherited form, one mutation is inherited via the germinal cells and the second occurs in somatic cells. In the nonhereditary form, both mutations occur in somatic cells. The second mutation produces an average of three retinoblastomas per individual inheriting the first mutation. Using Poisson statistics, one can calculate that this number (three) can explain the occasional gene carrier who gets no tumor, those who develop only unilateral tumors, and those who develop bilateral tumors, as well as explaining instances of multiple tumors in one eye. This value for the mean number of tumors occurring in genetic carriers may be used to estimate the mutation rate for each mutation. The germinal and somatic rates for the first, and the somatic rate for the second, mutation, are approximately equal. The germinal mutation may arise in some instances from a delayed mutation.
Cancer is a complex and heterogeneous disease that exhibits high levels of robustness against various therapeutic interventions. It is a constellation of diverse and evolving disorders that are manifested by the uncontrolled proliferation of cells that may eventually lead to fatal dysfunction of the host system. Although some of the cancer subtypes can be cured by early diagnosis and specific treatment, no effective treatment is yet established for a significant portion of cancer subtypes.
In industrial countries where the average life expectancy is high, cancer is one of the major causes of death. Any contribution to an in-depth understanding of cancer shall eventually lead to better care and treatment for patients. Due to the complex, heterogeneous, and evolving nature of cancer, it is essential for a system-oriented view to be adopted for an in-depth understanding. The question is how to achieve an in-depth yet realistic understanding of cancer dynamics. Although large-scale experiments are now being deployed, there are practical limitations of how much they do to convey the reality of cancer pathology and progression within the patient’s body. Computational approaches with system-oriented thinking may complement the limitations of an experimental approach. Computational studies not only provide us with new insights from large-scale experimental data, but also enable us to perceive what are the conceivable characteristics of cancer under certain assumptions. It is an engine of thoughts and proving grounds of various hypotheses on how cancer may behave as well as how molecular mechanisms work within anomalous conditions. It is not just computing that helps us fight against cancer, but a computational approach has to be combined with a proper theoretical framework that enables us to perceive “cancer” as complex dynamical and evolvable systems that entail a robust yet fragile nature. This recognition shifts our attention from the magic bullet approach of anti-cancer drugs to a more systematic control of cancer as complex dynamical phenomena. T