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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.

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... Les réseaux booléens sont aujourd'hui utilisés également pour la modélisation des voies de signalisation. Cette modélisation concerne divers systèmes biologiques (plantes [10,28], cellules mammifères [84,36,18,111,23], levures [59] 1. Principes d'application -des fonctionnalités cellulaires comme l'apoptose, la prolifération, la survie, la nécrose qui sont contrôlés par ces réseaux [84,36,18,59] ; -des types cellulaire spécialisés : cellules myéloïdes et lymphoïdes [23], lymphocytes T CD4 ou CD8 [88], par exemple. ...
... Les réseaux booléens sont aujourd'hui utilisés également pour la modélisation des voies de signalisation. Cette modélisation concerne divers systèmes biologiques (plantes [10,28], cellules mammifères [84,36,18,111,23], levures [59] 1. Principes d'application -des fonctionnalités cellulaires comme l'apoptose, la prolifération, la survie, la nécrose qui sont contrôlés par ces réseaux [84,36,18,59] ; -des types cellulaire spécialisés : cellules myéloïdes et lymphoïdes [23], lymphocytes T CD4 ou CD8 [88], par exemple. ...
... Le modèle sauvage du cancer de la vessie développé dans [84] {Proliferation → 1, Apoptosis → 0, Apoptosisλ2 → 0, GrowthArrest → 0}. ...
Thesis
Les maladies complexes comme leCancer et la maladie d’Alzheimer sont causéespar des perturbations moléculaires multiples responsablesd’un comportement cellulaire pathologique.Un enjeu majeur de la médecine de précisionest l’identification des perturbations moléculairesinduites par les maladies complexes et lesthérapies à partir de leurs conséquences sur lesphénotypes cellulaires.Nous définissons un modèle des maladies complexes,appelé la reprogrammation comportementale,assimilant les perturbations moléculairesà des altérations des fonctions dynamiqueslocales de systèmes dynamiques discrets induisantune reprogrammation de la dynamique globaledu réseau. Ce cadre de modélisation s’appuied’une part, sur les réseaux booléens contrôlés, quisont des réseaux booléens dans lesquels sont insérésdes paramètres de contrôle modélisant lesperturbations et, d’autre part, sur la définition demodes (Possibilité, Nécessité) permettant d’exprimerles objectifs de cette reprogrammation.À partir de ce cadre, nous démontrons que le calculdes noyaux, c’est-à-dire, des ensembles minimauxd’actions permettant la reprogrammationselon un mode s’exprime comme un problèmed’inférence abductive en logique propositionnelle.En nous appuyant sur les méthodes historiquesde calcul d’impliquants premiers des fonctionsbooléennes, nous développons deux méthodespermettant le calcul exhaustif des noyauxde la reprogrammation.Enfin, nous évaluons la pertinence du cadre demodélisation pour l’identification des perturbationsresponsables de la transformation d’unecellule saine en cellule cancéreuse et la découvertede cibles thérapeutiques sur un modèledu cancer du sein. Nous montrons notammentque les perturbations inférées par nos méthodessont compatibles avec la connaissance biologiqueen discriminant les oncogènes des gènes suppresseursde tumeurs et en récupérant la mutationdu gène BRCA1. De plus, la méthode récupèrele phénomène de létalité synthétique entrePARP1 et BRCA1, qui constitue un traitement anticancéreuxoptimal car il cible spécifiquement lescellules tumorales.
... parameters. Thus, they turn out to be well suited for modelling cellular differentiation processes and thereby predict perturbations for their control [1,5,6,7,18,24]. In Boolean networks, each gene or protein is modelled as a binary variable, which can only take 0 or 1 as its value: a value of 0 means that the gene or protein is inactive, whereas a value of 1 means that the gene or protein is active. ...
... Existing works focus on one-step reprogramming [5,7,10,16,18], or in rare instances, on sequential reprogramming, e.g., [12]. One-step reprogramming allows applying perturbations only once as shown in Fig. 1(a). ...
... In most methods on cellular reprogramming using Boolean networks [18,7,16], all perturbations are done at once, and the system is left to stabilize itself towards the desired target attractor. However, allowing perturbations to be performed at different points in time opens alternative reprogramming paths, possibly less costly. ...
Conference Paper
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We address the sequential reprogramming of gene regulatory networks modelled as Boolean networks. We develop an attractor-based sequential reprogramming method to compute all sequential reprogramming paths from a source attractor to a target attractor, where only attractors of the network are used as intermediates. Our method is more practical than existing reprogramming methods as it incorporates several practical constraints: (1) only biologically observable states, viz. attractors, can act as intermediates; (2) certain attractors, such as apoptosis, can be avoided as intermediates; (3) certain nodes can be avoided to perturb as they may be essential for cell survival or difficult to perturb with biomolecular techniques; and (4) given a threshold k, all sequential reprogramming paths with no more than k perturbations are computed. We compare our method with the minimal one-step reprogramming and the minimal sequential reprogramming on a variety of biological networks. The results show that our method can greatly reduce the number of perturbations compared to the one-step reprogramming, while having comparable results with the minimal sequential reprogramming. Moreover, our implementation is scalable for networks of more than 60 nodes.
... Given a mathematical model of an intracellular regulatory network, one commonly associates the possible phenotypes of the cell with the attractors of the model, an idea that can be traced back to Waddington [46,38] and Kauffman [20,18]. For example, the steady states in [33], discussed in more detail below, correspond to proliferative, apoptotic, or growth-arrest phenotypes of a cancer cell. In [3], the steady states of the model correspond to the observed altered iron metabolism phenotypes in a breast epithelial cell with and without a certain mutation in the RAS gene. ...
... Discrete networks defined in this way can be represented in the richer mathematical framework of polynomial dynamical systems (PDSs) [44,16], as can models in other common frameworks, such as Boolean networks [1], logical regulatory graphs [2], or multistate networks [33,3,10,41]. For instance, the mathematical tools associated with PDS allow for the computation of all steady states and cycles up to a certain length of a system as the solutions to a system of polynomial equations (in a suitably chosen finite field) without explicit simulation of the entire state space. ...
... The method was applied to a mathematical model of cellular response to DNA damage [4] and a model of large granular lymphocyte apoptosis escape [53]. However, an increasing number of discrete mathematical models in this context are not Boolean, e.g., [33,3,10,41], so that a more general method is desirable, and such a generalization is the focus of this paper. ...
Preprint
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Many problems in biology and medicine have a control component. Often, the goal might be to modify intracellular networks, such as gene regulatory networks or signaling networks, in order for cells to achieve a certain phenotype, such as happens in cancer. If the network is represented by a mathematical model for which mathematical control approaches are available, such as systems of ordinary differential equations, then this problem might be solved systematically. Such approaches are available for some other model types, such as Boolean networks, where structure-based approaches have been developed, as well as stable motif techniques. However, increasingly many published discrete models are mixed-state or multistate, that is, some or all variables have more than two states, and thus the development of control strategies for multistate networks is needed. This paper presents a control approach broadly applicable to general multistate models based on encoding them as polynomial dynamical systems over a finite algebraic state set, and using computational algebra for finding appropriate intervention strategies. To demonstrate the feasibility and applicability of this method, we apply it to a recently developed multistate intracellular model of E2F-mediated bladder cancerous growth, and to a model linking intracellular iron metabolism and oncogenic pathways. The control strategies identified for these published models are novel in some cases and represent new hypotheses, or are supported by the literature in others as potential drug targets. Our Macaulay2 scripts to find control strategies are publicly available through GitHub at https://github.com/luissv7/multistatepdscontrol .
... al. 2011), as can models in other common frameworks, such as Boolean networks (Albert and Thakar 2014), logical regulatory graphs (Chaouiya et al. 2006), or multistate networks (Remy et al. 2015;Chifman et al. 2017;Espinosa-Soto et al. 2004;Thieffry and Thomas 1995). For instance, the mathematical tools associated with PDSs allow for the computation of all steady states and cycles up to a certain length of a system as the solutions to a system of polynomial equations (in a suitably chosen finite field) without explicit simulation of the entire state space. ...
... The method was applied to a mathematical model of cellular response to DNA damage (Choi et al. 2012) and a model of large granular lymphocyte apoptosis escape (Zhang et al. 2008). However, an increasing number of discrete mathematical models in this context are not Boolean, e.g., (Remy et al. 2015;Chifman et al. 2017;Espinosa-Soto et al. 2004;Thieffry and Thomas 1995), so that a more general method is desirable, and such a generalization is the focus of this paper. ...
... As our method uses polynomial algebra over a finite field, all network nodes need to take values in a common finite field, in particular, all nodes need to have the same number of possible values. In many published models, however, different nodes take on different numbers of states, and this number generally does not allow the imposition of a finite field structure (for which the number is required to be a power of a prime number); see, e.g., (Remy et al. 2015;. As part of the algorithm in this manuscript, we present a method to convert models with a general number of mixed discrete states into a model that satisfies the computational algebra requirements, without changing the model's steady states, and which is not equivalent to the well-known reduction to a Boolean network that adds new nodes to the network, as done in . ...
Article
Many problems in biology and medicine have a control component. Often, the goal might be to modify intracellular networks, such as gene regulatory networks or signaling networks, in order for cells to achieve a certain phenotype, what happens in cancer. If the network is represented by a mathematical model for which mathematical control approaches are available, such as systems of ordinary differential equations, then this problem might be solved systematically. Such approaches are available for some other model types, such as Boolean networks, where structure-based approaches have been developed, as well as stable motif techniques. However, increasingly many published discrete models are mixed-state or multistate, that is, some or all variables have more than two states, and thus the development of control strategies for multistate networks is needed. This paper presents a control approach broadly applicable to general multistate models based on encoding them as polynomial dynamical systems over a finite algebraic state set, and using computational algebra for finding appropriate intervention strategies. To demonstrate the feasibility and applicability of this method, we apply it to a recently developed multistate intracellular model of E2F-mediated bladder cancerous growth and to a model linking intracellular iron metabolism and oncogenic pathways. The control strategies identified for these published models are novel in some cases and represent new hypotheses, or are supported by the literature in others as potential drug targets. Our Macaulay2 scripts to find control strategies are publicly available through GitHub at https://github.com/luissv7/multistatepdscontrol.
... 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. ...
Preprint
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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 .
... 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. ...
Thesis
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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.
... 8 9 To study these observed differences in drug response in various cancers, some approaches based 10 on mathematical modelling were developed to explore the complexity of differential drug 11 sensitivities. A number of machine learning-based methods for predicting sensitivities have been 12 proposed [3], either without particular constraints or with varying degrees of prior knowledge; but 13 they do not provide a mechanistic understanding of the response. Some other approaches focused 14 on the description of the processes that might influence the response by integrating knowledge of facilitates the interpretation of mechanisms and drug response [9,10] and despite its simplicity, 35 semi-quantitative analyses have already been performed on complex systems [11] for both cancer 36 applications [9,12] and drug response studies [13,14], and have proved their efficacy [15,16]. ...
... A number of machine learning-based methods for predicting sensitivities have been 12 proposed [3], either without particular constraints or with varying degrees of prior knowledge; but 13 they do not provide a mechanistic understanding of the response. Some other approaches focused 14 on the description of the processes that might influence the response by integrating knowledge of facilitates the interpretation of mechanisms and drug response [9,10] and despite its simplicity, 35 semi-quantitative analyses have already been performed on complex systems [11] for both cancer 36 applications [9,12] and drug response studies [13,14], and have proved their efficacy [15,16]. 37 38 The nature of this formalism has shown its relevance in cases where the model is not 39 automatically trained on data but simply constructed from literature or pathway databases and 40 where biological experiments focus on a particular cell line [17]. ...
Preprint
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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 signalling 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 behaviours 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. Author summary We constructed a logical model to study, from a dynamical perspective, the differences between melanomas and colorectal cancers that share the same BRAF mutations but exhibit different sensitivities to anti-BRAF treatments. The model was built from the literature and completed from existing pathway databases. The model encompasses the key proteins of the MAPK pathway and was made specific to each cancer cell line (100 melanoma and colorectal cell lines from public database) using available omics data, including mutations and RNAseq data. It can simulate the effect of drugs and show high correlation with experimental results. Moreover, the structure of the network confirms both the importance of the reactivation of the MAPK pathway through CRAF and the involvement of PI3K/AKT pathway in the mechanisms of resistance to BRAF inhibition. The study shows that, because of the low number of samples, the mechanistic approach that we propose provides different insights than powerful standard machine learning methodologies would, showing the complementarity between the two approaches. An important aspect to mention is that the mechanistic approach presented here does not rely on training datasets but directly interprets and maps data on the model to simulate drug responses.
... Besides the myeloid and Th cell differentiation networks, we also apply the three control methods to several other biological networks [4,21,16,2,20,1,3]. Here is a brief introduction of the networks. ...
... -The network of hematopoietic cell specification is constructed to capture the lymphoid and myeloid cell development [2]. -The network of bladder tumour is constructed to study mutually exclusivity and co-occurrence in genetic alterations [20]. -The pharmacodynamic model of bortezomib responses integrates major survival and apoptotic pathways in U266 cells to connect bortezomib exposure to multiple myeloma cellular proliferation [1]. ...
Preprint
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 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 paths and guarantee 100% 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.
... Computational models of molecular signaling [36][37][38][39][40][41] have the potential to improve drug discovery and development [32,[42][43][44]. Analyses of knockdown experiments [45] using mass spectrometry [46] and transcriptomics [47][48][49] are progressively refined and tuned towards specific physiological situations. While these studies have helped considerably to extend our understanding of tumor biology, they are still restricted to signaling pathways and do not integrate the metabolic pathways, which in some initial studies have been subjected to separate systems biology analysis. ...
... These distinctively expressed proteins were considered as overexpressed in the respective cell lines in comparison to the other. Compared quantitatively, 458 of the 528 common proteins were expressed at similar levels, 70 proteins were either up (45) or down (25) regulated in U87MGvIII compared to U87MG ( Fig 4A). Together, nearly half of the identified proteins (449) were differentially expressed, 268 down-regulated and 181 up-regulated in U87MGvIII versus U87MG (Fig 4B). ...
Article
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As malignant transformation requires synchronization of growth-driving signaling (S) and metabolic (M) pathways, defining cancer-specific S-M interconnected networks (SMINs) could lead to better understanding of oncogenic processes. In a systems-biology approach, we developed a mathematical model for SMINs in mutated EGF receptor (EGFRvIII) compared to wild-type EGF receptor (EGFRwt) expressing glioblastoma multiforme (GBM). Starting with experimentally validated human protein-protein interactome data for S-M pathways, and incorporating proteomic data for EGFRvIII and EGFRwt GBM cells and patient transcriptomic data, we designed a dynamic model for EGFR-driven GBM-specific information flow. Key nodes and paths identified by in silico perturbation were validated experimentally when inhibition of signaling pathway proteins altered expression of metabolic proteins as predicted by the model. This demonstrated capacity of the model to identify unknown connections between signaling and metabolic pathways, explain the robustness of oncogenic SMINs, predict drug escape, and assist identification of drug targets and the development of combination therapies.
... We apply these methods to 10 biological networks (Cohen et al., 2015;Conroy et al., 2014;Grieco et al., 2013;Kim et al., 2013;Naldi et al., 2010;Offermann et al., 2016;Remy et al., 2015;Saez-Rodriguez et al., 2007;Schlatter et al., 2009;Singh et al., 2012). Our methods for the computation of min-TC and min-FC are implemented as part of the software tool ASSA-PBN (Mizera et al., 2018). ...
Article
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Motivation: The control of Boolean networks has traditionally focussed on strategies where the perturbations are applied to the nodes of the network for an extended period of time. In this work, we study if and how a Boolean network can be controlled by perturbing a minimal set of nodes for a single-step and letting the system evolve afterwards according to its original dynamics. More precisely, given a Boolean network (BN), we compute a minimal subset Cmin of the nodes such that BN can be driven from any initial state in an attractor to another 'desired' attractor by perturbing some or all of the nodes of Cmin for a single-step. Such kind of control is attractive for biological systems because they are less time consuming than the traditional strategies for control while also being financially more viable. However, due to the phenomenon of state-space explosion, computing such a minimal subset is computationally inefficient and an approach that deals with the entire network in one-go, does not scale well for large networks. Results: We develop a 'divide-and-conquer' approach by decomposing the network into smaller partitions, computing the minimal control on the projection of the attractors to these partitions and then composing the results to obtain Cmin for the whole network. We implement our method and test it on various real-life biological networks to demonstrate its applicability and efficiency. Supplementary information: Supplementary data are available at Bioinformatics online.
... NFE2L2, KDM6A and FGFR3, and ERBB3 and ERBB4, while others show patterns of mutual exclusivity in bladder cancer as found for P53 and RAS and RB1 and FGFR3[142]. ...
Conference Paper
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This thesis reports the development of several computational approaches to predict human disease proteins and to assess their value as drug targets, using in-house domain functional families (CATH FunFams). CATH-FunFams comprise evolutionary related protein domains with high structural and functional similarity. External resources were used to identify proteins associated with disease and their genetic variations. These were then mapped to the CATH-FunFams together with information on drugs bound to any relatives within the FunFam. A number of novel approaches were then used to predict the proteins likely to be driving disease and to assess whether drugs could be repurposed within the FunFams for targeting these putative driver proteins. The first work chapter of this thesis reports the mapping of drugs to CATHFunFams to identify druggable FunFams based on statistical overrepresentation of drug targets within the FunFam. 81 druggable CATH-FunFams were identified and the dispersion of their relatives on a human protein interaction network was analysed to assess their propensity to be associated with side effects. In the second work chapter, putative drug targets for bladder cancer were identified using a novel computational protocol that expands a set of known bladder cancer genes with genes highly expressed in bladder cancer and highly associated with known bladder cancer genes in a human protein interaction network. 35 new bladder cancer targets were identified in druggable FunFams, for some of which FDA approved drugs could be repurposed from other protein domains in the FunFam. In the final work chapter, protein kinases and kinase inhibitors were analysed. These are an important class of human drug targets. A novel classification protocol was applied to give a comprehensive classification of the kinases which was benchmarked and compared with other widely used kinase classifications. Druginformation from ChEMBL was mapped to the Kinase-FunFams and analyses of protein network characteristics of the kinase relatives in each FunFam used to identify those families likely to be associated with side effects.
... 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]). ...
Article
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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.
... 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). ...
Article
Epithelial-to-Mesenchymal Transition (EMT) is a natural and reversible process involved in embryogenesis, wound healing and thought to participate in the process of metastasis. Multiple signals from the microenvironment have been reported to drive EMT. However, the tight control of this process on physiological scenarios and how it is disrupted during cancer progression is not fully understood. Here, we analysed a regulatory network of EMT accounting for 10 key microenvironment signals focusing on the impact of two cell contact signals on the reversibility of EMT and the stability of resulting phenotypes. The analysis showed that the microenvironment is not enough for stabilizing Hybrid and Ameoboid-like phenotypes, requiring intracellular de-regulations as reported during cancer progression. Our simulations demonstrated that RPTP activation by cell contacts have the potential to inhibit the process of EMT and trigger its reversibility under tissue growth and chronic inflammation scenarios. Simulations also showed that hypoxia inhibits the capacity of RPTPs to control EMT. Our analysis further provided a theoretical explanation for the observed correlation between hypoxia and metastasis under chronic inflammation, and predicted that de-regulations in FAT4 signalling may promote Hybrid stabilization Taken together, we propose a natural control mechanism of EMT that supports the idea that EMT is tightly regulated by the microenvironment.
... Examples: Steinway et al. developed a Boolean model to explore the role of transforming growth factor beta (TGFb) signaling in hepatocellular carcinoma epithelial-to-mesenchymal transition, a process by which cancer cells lose their epithelial features to acquire a mesenchymal phenotype with metastatic capabilities. 64 In Remy et al., 65 logical model to uncover patterns of genetic alterations (co-occurrences and mutual exclusivities) in bladder tumors. Synergistic drug interactions were predicted by Floback and co-authors by analyzing a logical model of the network controlling cell fate decision in the AGS gastric cancer cell line. ...
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Drug research, therapy development, and other areas of pharmacology and medicine can benefit from simulations and optimization of mathematical models that contain a mathematical description of interactions between systems elements at the cellular, tissue, organ, body, and population level. This approach is the foundation of systems medicine and precision medicine. Here, simulated experiments are performed with computers (in silico) first, and they are then replicated through lab experiments (in vivo or in vitro) or clinical studies. In turn, these experiments and studies can be used to validate or improve the models. This iterative loop of dry and wet lab work is successful when biomedical researchers tightly collaborate with data scientists and modelers. From an educational point of view, the interdisciplinary research in systems biology can be sustained most effectively when specialists have been trained to have both a strong background in the disciplines of biology or modeling and strong communication skills, which make them able to communicate with other specialists. This overview addresses possible interdisciplinary communication gaps. Focusing our attention on biomedical researchers, we describe the reasons for using modeling and ways to collaborate with modelers, including their needs for specific biological expertise and data. This review includes an introduction to the principles of several widely used mechanistic modeling methods, focusing on their areas of applicability as well as their limitations. A potential complementary role of machine-learning methods in the development of mechanistic models is also discussed. The descriptions of the methods also include links to corresponding modeling software tools as well as practical examples of their application. Finally, we also explicitly address different aspects of multiscale modeling approaches that allow a more complete and holistic perspective of the human body.
... We observed a striking cooccurrence between mutations in NOTCH1 and a number of chromatin-remodeling genes ( Figure 3A). Cooccurrence between 2 alterations may imply possible biological synergy, whereby only dysfunction in both genes may lead to cancer (31,32). It is also pos- A final, previously unexplored aspect of ACC involves pathogenic germline alterations. ...
Article
BACKGROUND Adenoid cystic carcinoma (ACC) is a rare malignancy arising in salivary glands and other sites, characterized by high rates of relapse and distant spread. Recurrent/metastatic (R/M) ACCs are generally incurable, due to a lack of active systemic therapies. To improve outcomes, deeper understanding of genetic alterations and vulnerabilities in R/M tumors is needed.METHODS An integrated genomic analysis of 1,045 ACCs (177 primary, 868 R/M) was performed to identify alterations associated with advanced and metastatic tumors. Intratumoral genetic heterogeneity, germline mutations, and therapeutic actionability were assessed.RESULTSCompared with primary tumors, R/M tumors were enriched for alterations in key Notch (NOTCH1, 26.3% vs. 8.5%; NOTCH2, 4.6% vs. 2.3%; NOTCH3, 5.7% vs. 2.3%; NOTCH4, 3.6% vs. 0.6%) and chromatin-remodeling (KDM6A, 15.2% vs. 3.4%; KMT2C/MLL3, 14.3% vs. 4.0%; ARID1B, 14.1% vs. 4.0%) genes. TERT promoter mutations (13.1% of R/M cases) were mutually exclusive with both NOTCH1 mutations (q = 3.3 × 10-4) and MYB/MYBL1 fusions (q = 5.6 × 10-3), suggesting discrete, alternative mechanisms of tumorigenesis. This network of alterations defined 4 distinct ACC subgroups: MYB+NOTCH1+, MYB+/other, MYBWTNOTCH1+, and MYBWTTERT+. Despite low mutational load, we identified numerous samples with marked intratumoral genetic heterogeneity, including branching evolution across multiregion sequencing.CONCLUSION These observations collectively redefine the molecular underpinnings of ACC progression and identify further targets for precision therapies.FUNDINGAdenoid Cystic Carcinoma Research Foundation, Pershing Square Sohn Cancer Research grant, the PaineWebber Chair, Stand Up 2 Cancer, NIH R01 CA205426, the STARR Cancer Consortium, NCI R35 CA232097, the Frederick Adler Chair, Cycle for Survival, the Jayme Flowers Fund, The Sebastian Nativo Fund, NIH K08 DE024774 and R01 DE027738, and MSKCC through NIH/NCI Cancer Center Support Grant (P30 CA008748).
... 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]. ...
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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.
... • 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]. ...
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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.
... 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). ...
Article
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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.
... 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. ...
Article
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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.
... In [11] we introduced methods for quantifying side effects in Boolean networks. However, many of the more recently published discrete dynamical models include variables that take on more than two states due to the need for capturing mechanisms that are not binary in nature [12,13,14,15,16]. Consequently, Boolean nested and partially nested canalizing functions were generalized to multistate [17,18,19] which enables the possibility of capturing more complex interactions among the genes in the network. ...
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Developing efficient computational methods to change the state of a cell from an undesirable condition, e.g. diseased, into a desirable, e.g. healthy, condition is an important goal of systems biology. The identification of potential interventions can be achieved through mathematical modeling of the state of a cell by finding appropriate input manipulations in the model that represent external interventions. This paper focuses on quantifying the unwanted or unplanned changes that come along with the application of an intervention to produce a desired effect, which we define as the \emph{side effects} 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 control targets can be represented by a set of nodes and edges that can be manipulated to produce a desired effect on the system. This paper presents practical tools along with applications for the analysis and control of multistate networks. The first result is a polynomial normal form representation for discrete functions that provides a partition of the inputs of the function into canalizing and non-canalizing variables and, within the canalizing ones, we categorize the input variables into layers of canalization. The second theoretical result is 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 stratification of the inputs of the target function where the number of changed transitions depends on the layer of canalization that includes the input to be deleted. Applications from using these formulas to estimate the number of changes in the state space and comparisons with the actual number of changes are also presented.
... 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. ...
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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.
... 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). ...
Article
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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.
... • The bladder cancer network of 35 nodes allows one to identify the deregulated pathways and their influence on bladder tumourigenesis [52]. ...
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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.
... 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. ...
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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.
... 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. ...
Article
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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. ...
Article
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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. ...
Article
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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]. ...
Preprint
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. ...
Preprint
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). ...
Article
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). ...
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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). ...
Preprint
Full-text available
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. ...
Article
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. ...
Preprint
Full-text available
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.
... 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]. ...
Article
Full-text available
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] . ...
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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.
Thesis
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.
Article
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.
Preprint
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.
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Predicting the behaviors of complex biological systems, underpinning processes such as cellular differentiation, 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 which enables reasoning on the qualitative dynamics of networks accounting for many species. Several dynamical approaches have been proposed to interpret the logic of the regulations encoded by the BNs. 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 are not adequate to represent quantitative systems, being able to both predict spurious behaviors and miss others. We introduce a new paradigm, the Most Permissive Boolean Networks (MPBNs), which offer 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.
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The two most fundamental processes describing change in biology, development, and evolution, occur over drastically different timescales. Development involves temporal sequences of cell states controlled by hierarchies of regulatory structures. It occurs over the lifetime of a single individual and is associated with gene expression level change of a given genotype. Evolution, by contrast, entails genotypic change through mutation, the acquisition/loss of genes and changes in the network topology of interactions among genes. It involves the emergence of new, environmentally selected phenotypes over the lifetimes of many individuals. We start by reviewing the most limiting aspects of the theoretical modeling of gene regulatory networks (GRNs) which prevent the study of both timescales in a common, mathematical language. We then consider the simple framework of Boolean network models of GRNs and point out its inadequacy to include evolutionary processes. As opposed to one‐to‐one maps to specific attractors, we adopt a many‐to‐one map which makes each phenotype correspond to multiple attractors. This definition no longer requires a fixed size for the genotype and opens the possibility for modeling the phenotypic change of a genotype, which is itself changing over evolutionary timescales. At the same time, we show that this generalized framework does not interfere with established numerical techniques for the identification of the kernel of controlling nodes responsible for cell differentiation through external signals. Research Highlights • 1. Formalism for evolutionary change in GRNs. • 2. Cell phenotype as collections of equivalent gene expression patterns. • 3. GRN dynamics with genotype‐changing mutations. • 4. Controllability of the revised formalism. • 5. Mathematical framework for GRN differences across related species.
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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.
Chapter
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.
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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.
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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.
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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
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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