ArticleLiterature Review

The shortest path is not the one you know: Application of biological network resources in precision oncology research

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Abstract

Several decades of molecular biology research have delivered a wealth of detailed descriptions of molecular interactions in normal and tumour cells. This knowledge has been functionally organised and assembled into dedicated biological pathway resources that serve as an invaluable tool, not only for structuring the information about molecular interactions but also for making it available for biological, clinical and computational studies. With the advent of high-throughput molecular profiling of tumours, close to complete molecular catalogues of mutations, gene expression and epigenetic modifications are available and require adequate interpretation. Taking into account the information about biological signalling machinery in cells may help to better interpret molecular profiles of tumours. Making sense out of these descriptions requires biological pathway resources for functional interpretation of the data. In this review, we describe the available biological pathway resources, their characteristics in terms of construction mode, focus, aims and paradigms of biological knowledge representation. We present a new resource that is focused on cancer-related signalling, the Atlas of Cancer Signalling Networks. We briefly discuss current approaches for data integration, visualisation and analysis, using biological networks, such as pathway scoring, guilt-by-association and network propagation. Finally, we illustrate with several examples the added value of data interpretation in the context of biological networks and demonstrate that it may help in analysis of high-throughput data like mutation, gene expression or small interfering RNA screening and can guide in patients stratification. Finally, we discuss perspectives for improving precision medicine using biological network resources and tools. Taking into account the information about biological signalling machinery in cells may help to better interpret molecular patterns of tumours and enable to put precision oncology into general clinical practice. © The Author 2015. Published by Oxford University Press on behalf of the UK Environmental Mutagen Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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... Edges connecting signaling network nodes are activating or inhibitory physical connections of participating proteins, microRNAs and DNA-sequences including enzymatic actions, like phosphorylation, or dephosphorylation [1][2][3][4][5]. ...
... There are several signaling network resources (such as the curated and multi-layered SignaLink database, http://signalink.org [2,5]), among which the Atlas of Cancer Signaling Networks [4] is primarily focused on signaling components important in cancer. Proteins with cancer-related mutations are often hubs of the signaling network, which are becoming enriched in positive regulatory loops during cancer development [6,7]. ...
... Incorporation of personalized data, such as mutation, single nucleotide polymorphism, transcriptional, proteome, signalome (e.g. phosphoproteome) and epigenetic profiles to signaling networks significantly enhance patient-and disease stage-specific drug targeting in anti-cancer therapies [1][2][3][4][5]8,9]. Patient specificity can differentiate network behavior in at least four different levels: A.) at the level of the genetic background (e.g., single-nucleotide polymorphisms and cancer-related mutations, copy-number changes or chromatin rearrangements); B.) at the level of gene expression and translational changes (caused by e.g. ...
Article
Cancer initiation and development are increasingly perceived as systems-level phenomena, where intra- and inter-cellular signaling networks of the ecosystem of cancer and stromal cells offer efficient methodologies for outcome prediction and intervention design. Within this framework, RAS emerges as a 'contextual signaling hub', i.e. the final result of RAS activation or inhibition is determined by the signaling network context. Current therapies often train' cancer cells shifting them to a novel attractor, which has increased metastatic potential and drug resistance. The few therapy-surviving cancer cells are surrounded by massive cell death triggering a primordial adaptive and reparative general wound healing response. Overall, dynamic analysis of patient- and disease-stage specific intracellular and intercellular signaling networks may open new areas of anticancer therapy using multitarget drugs, drugs combinations, edgetic drugs, as well as help design 'gentler', differentiation and maintenance therapies.
... Signatures and functional enrichment studies using pathways databases are suitable for stratifying cancers and understanding what molecular mechanisms are implicated in M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 5 various cancer types, but these approaches still do not provide the clues on mechanistic basis of the disease and do not address the question of signaling network rewiring during cancer initiation and development. The step forward is to use molecular information detailed in pathways and signaling network resources as Panther [23], Spike [24], Kegg Pathway [25], Reactome [26], ACSN [27] (Table 1). These resources provide a more global picture of cell signaling with sufficient granularity of molecular detail description, capturing crosstalks and feedback loops between molecular circuits. ...
... Defining a "field of influence", the distance in the vicinity of the deregulated node, where players are assumed to have the similar impact on the pathology, is a source of potential targets for interference. Exploiting the notion of "network distance" between proteins, namely if several affected proteins create a compact group when mapped on the signaling network and therefore can be related functionally, they may represent together a set for intervention [27]. ...
Article
Signaling pathways implicated in cancer create a complex network with numerous regulatory loops and redundant pathways. This complexity explains frequent failure of one-drug-one-target paradigm of treatment, resulting in drug resistance in patients. To overcome the robustness of cell signaling network, cancer treatment should be extended to a combination therapy approach. Integrating and analyzing patient high-throughput data together with the information about biological signaling machinery may help deciphering molecular patterns specific to each patient and finding the best combinations of candidates for therapeutic targeting. We review state of the art in the field of targeted cancer medicine from the computational systems biology perspective. We summarize major signaling network resources and describe their characteristics with respect to applicability for drug response prediction and intervention targets suggestion. Thus discuss methods for prediction of drug sensitivity and intervention combinations using signaling networks together with high-throughput data. Gradual integration of these approaches into clinical routine will improve prediction of response to standard treatments and adjustment of intervention schemes. Copyright © 2015. Published by Elsevier Inc.
... ( Figure 5). We present this map in the form of a web-based atlas which is hierarchical and interconnected collection of maps browsable online [50]. The atlas depicts molecular mechanisms of Cell Cycle [8], DNA Repair, Cell Survival, Apoptosis, Epithelial-to-Mesenchymal Transition and Cell Motility and beyond. ...
... A screenshot of the cell cycle territory in the map of Atlas of Cancer Signalling Network (ACSN, http://acsn.curie.fr), with profiles of expression in several tumour samples shown on top of the protein icons[7,50]. ...
Article
The problem of dealing with complexity arises when we fail to achieve a desired behavior of biological systems (for example, in cancer treatment). In this review I formulate the problem of tackling biological complexity at the level of large-dimensional datasets and complex mathematical models of reaction networks. I show that in many cases the complexity can be reduced by using approximation by simpler objects (for example, using principal graphs for data dimension reduction, and using dominant systems for reducing complex models). Examples of dealing with complexity from various fields of molecular systems biology are used, in particular, from the analysis of cancer transcriptomes, mathematical modeling of protein synthesis and of cell fate decisions between death and life.
... This allows understanding the global picture and connectivity between processes that is very difficult to keep in mind just from reading multiple scientific papers. Once the processes are depicted together as diagrams, the relationship between molecular circuits in cells can be appreciated, which makes signaling network maps also didactic tools (1). ...
... From text to modelRepresentation of biochemical reactions from the following text from a molecular biology manuscript.Numbers correspond to the reactions in the diagram: « BRCA1 transcription(1) and translation(2)is positively regulated by E2F1/BRIT1* complex(3) and inhibited by p53(4). BRCA1 protein is transported into nucleus(5), where CHEK2 kinase activates it by spesific phosphorylation(6)and(7). ...
Chapter
Full-text available
Graphical representation of biological knowledge in the form of interactive diagrams became widely used in molecular and computational biology. It enables the scientific community to exchange and discuss information on cellular processes described in numerous scientific publications and to interpret high-throughput data. Constructing a signaling network map is a laborious process, therefore application of consistent procedures for representation of molecular processes and accurately organized annotation is essential for generation of a high-quality signaling network map that can be used by various computational tools. We summarize here the major aims and challenges of assembling information in a form of comprehensive maps of molecular interactions and suggest an optimized workflow. We share our experience gained while creating a biological network resource Atlas of Cancer Signaling Network (ACSN) that was successfully applied in several studies. We explain the map construction process. Then we address the problem of user interaction with large signaling maps and suggest to facilitate navigation by hierarchical organization of map structure and by application of semantic zooming principles. In addition, we describe a computational technology using Google Maps API to explore signaling networks in the manner similar to global geographical maps and provide the outline for preparing a biological network for this type of navigation. Nowadays the most demanded application of signaling maps is integration and functional interpretation of high-throughput data. We demonstrate several examples of cancer data visualization in the context of comprehensive signaling network maps.
... (page number not for citation purposes) capture the multiple cross-talks and interactions occurring between different cell processes (1). Analysis and visualization of omics data in the context of signalling network maps can help to detect patterns in the data projected onto the molecular mechanisms there represented. ...
... Transforming text to diagram: role of p53 and NOTCH in induction of EMT. The following statements were used for diagram construction: (1). Control of EMT program is performed by SNAIL and TWIST, the major transcription factors that can induce the executors EMT program (49). ...
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Generation and usage of high-quality molecular signalling network maps can be augmented by standardizing notations, establishing curation workflows and application of computational biology methods to exploit the knowledge contained in the maps. In this manuscript, we summarize the major aims and challenges of assembling information in the form of comprehensive maps of molecular interactions. Mainly, we share our experience gained while creating the Atlas of Cancer Signalling Network. In the step-by-step procedure, we describe the map construction process and suggest solutions for map complexity management by introducing a hierarchical modular map structure. In addition, we describe the NaviCell platform, a computational technology using Google Maps API to explore comprehensive molecular maps similar to geographical maps and explain the advantages of semantic zooming principles for map navigation. We also provide the outline to prepare signalling network maps for navigation using the NaviCell platform. Finally, several examples of cancer high-throughput data analysis and visualization in the context of comprehensive signalling maps are presented.
... Tthe shortest path algorithm. The shortest path algorithm, one of network link algorithm, is used to intelligently identify the shortest connection between two genes or proteins in a graphical model that represents a cellular network 100,101 . The algorithm is illustrated in Fig. 3 and Algorithm 1. ...
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Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.
... Cluster of Cluster Assignments (COCA) [137] in breast cancer [138] and iCluster [139] in application to prostate cancer [42] and hepatocellular carcinoma [140]. Alternatively, network-based approaches [141][142][143] to data analysis have the potential to integrate data from disparate sources, while providing clinically relevant results. Multidisciplinary initiatives such as molecular tumour boards [144,145], which bring together bioinformaticians, biologists and clinicians, can also help address the issue of translating complex data to be relevant to clinical care providers and patients. ...
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There has been an exponential growth in the performance and output of sequencing technologies (omics data) with full genome sequencing now producing gigabases of reads on a daily basis. These data may hold the promise of personalized medicine, leading to routinely available sequencing tests that can guide patient treatment decisions. In the era of high-throughput sequencing (HTS), computational considerations, data governance and clinical translation are the greatest rate-limiting steps. To ensure that the analysis, management and interpretation of such extensive omics data is exploited to its full potential, key factors, including sample sourcing, technology selection and computational expertise and resources, need to be considered, leading to an integrated set of high-performance tools and systems. This article provides an up-to-date overview of the evolution of HTS and the accompanying tools, infrastructure and data management approaches that are emerging in this space, which, if used within in a multidisciplinary context, may ultimately facilitate the development of personalized medicine.
... web service.html. In Supplementary Materials, we provide two case studies demonstrating visualization of ovary cancer data obtained from The Cancer Genome Atlas (21) on the large map of Atlas of Cancer Signalling Network (22) and an example of using the non-CellDesigner network map of the Ewing's sarcoma signalling network (23) for visualizing transcriptomic time series data. ...
Article
Data visualization is an essential element of biological research, required for obtaining insights and formulating new hypotheses on mechanisms of health and disease. NaviCell Web Service is a tool for network-based visualization of 'omics' data which implements several data visual representation methods and utilities for combining them together. NaviCell Web Service uses Google Maps and semantic zooming to browse large biological network maps, represented in various formats, together with different types of the molecular data mapped on top of them. For achieving this, the tool provides standard heatmaps, barplots and glyphs as well as the novel map staining technique for grasping large-scale trends in numerical values (such as whole transcriptome) projected onto a pathway map. The web service provides a server mode, which allows automating visualization tasks and retrieving data from maps via RESTful (standard HTTP) calls. Bindings to different programming languages are provided (Python and R). We illustrate the purpose of the tool with several case studies using pathway maps created by different research groups, in which data visualization provides new insights into molecular mechanisms involved in systemic diseases such as cancer and neurodegenerative diseases. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.
... In order to explain the 17 LST hi cases with neither BRCA1/2 nor RAD51C inactivation 317 genes involved in DNA damage signaling and repair were explored. 44,45 No deleterious mutation associated with LOH was found in the 17 LST hi unexplained cases. Strikingly, cases with bi-allelic inactivation of WRN (1 case) and ATM (six cases) belonged to the LST lo subgroup. ...
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Therapeutic strategies targeting Homologous Recombination Deficiency (HRD) in breast cancer requires patient stratification. The LST (Large-scale State Transitions) genomic signature previously validated for triple-negative breast carcinomas (TNBC) was evaluated as biomarker of HRD in luminal (hormone receptor positive) and HER2-overexpressing (HER2+) tumors. The LST genomic signature related to the number of large-scale chromosomal breakpoints in SNP-array tumor profile was applied to identify HRD in in-house and TCGA sets of breast tumors, in which the status of BRCA1/2 and other genes was also investigated. In the in-house dataset, HRD was predicted in 5% (20/385) of sporadic tumors luminal or HER2+ by the LST genomic signature and the inactivation of BRCA1, BRCA2 or RAD51C confirmed this prediction in 75% (12/16) of the tested cases. In 14% (6/43) of tumors occurring in BRCA1/2 mutant carriers, the corresponding wild-type allele was retained emphasizing the importance of determining the tumor status. In the TCGA luminal and HER2+ subtypes HRD incidence was estimated at 5% (18/329, 95%CI:5-8%) and 2% (1/59, 95%CI:2-9%), respectively. In TNBC cisplatin-based neo-adjuvant clinical trials, HRD is shown to be a necessary condition for cisplatin sensitivity. This analysis demonstrates the high performance of the LST genomic signature for HRD detection in breast cancers, which suggests its potential as a biomarker for genetic testing and patient stratification for clinical trials evaluating platinum salts and PARP inhibitors. This article is protected by copyright. All rights reserved. © 2014 Wiley Periodicals, Inc. © 2015 UICC.
... This problem can be addressed with a systems biology approach -how to interpret expression and mutation data and take them to a higher level of understanding. Here we will briefly describe basic applications of systems biology and data integration, while more details on this topic could be found in other publications [146][147][148]. ...
Article
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Nowadays, the personalized approach to health care and cancer care in particular is becoming more and more popular and is taking an important place in the translational medicine paradigm. In some cases, detection of the patient-specific individual mutations that point to a targeted therapy has already become a routine practice for clinical oncologists. Wider panels of genetic markers are also on the market which cover a greater number of possible oncogenes including those with lower reliability of resulting medical conclusions. In light of the large availability of high-throughput technologies, it is very tempting to use complete patient-specific New Generation Sequencing (NGS) or other "omics" data for cancer treatment guidance. However, there are still no gold standard methods and protocols to evaluate them. Here we will discuss the clinical utility of each of the data types and describe a systems biology approach adapted for single patient measurements. We will try to summarize the current state of the field focusing on the clinically relevant case-studies and practical aspects of data processing.
... A popular method to leverage this prior knowledge consists in using a diffusion process on the gene network. This technique first appeared for the analysis of gene expression and GWAS data [21][22][23][24][25], and has more recently been used for mutation profiles [26][27][28][29][30]. Diffusion processes allow smoothing binary vectors of somatic gene mutations into dense vectors where the mutation status of a gene is increased when it is close to mutated genes in the network. This approach led to state-of-the-art methods for the discovery of driver pathways or complexes [29] and for the stratification of patients into clinically relevant subtypes [30] using whole-exome mutation profiles. ...
Article
Author summary The transition from a normal cell to a cancer cell is driven by genetic alterations, such as mutations, that induce uncontrolled cell proliferation. With the advent of next-generation sequencing technologies (NGS) in the last decade, thousands of tumours have been sequenced and their mutation profiles determined. However, the statistical analysis of these mutation profiles remains challenging. Indeed, two patients usually do not share the same set of mutations and can even have none in common. Moreover, it is difficult to distinguish the few disease-causing mutations from the dozens, often hundreds of mutations observed in a tumour. To alleviate these challenges, it has been proposed to use gene-gene interaction networks as prior knowledge, with the idea that if a gene is mutated and non-functional, then its interacting neighbours might not be able to fulfil their function as well. Here we propose NetNorM, a method that transforms mutation data using gene networks so as to make mutation profiles more amenable to statistical learning. We show that NetNorM significantly improves the prognostic power of mutation data compared to previous approaches, and allows defining meaningful groups of patients based on their mutation profiles.
... The shortest path problem is a classical problem [1], [2], but is also a tough issue especially in large-scale network [3], [4]. It appears in many practical applications, such as transportation networks [5], [6], [7], isometric feature mapping [8], biological networks analysis [9], subgraph similarity matching [10], pattern mining [11], [12], [13], RDF clustering [14], and social networks [15]. Motivated by these applications, a variety of shortest path problems have been investigated. ...
Article
The A-star algorithm is an efficient classical algorithm for solving the shortest path problem. The efficiency of the algorithm depends on the evaluation function, which is used to estimate the heuristic value of the shortest path from the current vertex to the target. When the vertex coordinates are known, the heuristic value of the shortest path is usually generated by the distance. In this paper, we present an Index-Based A-Star algorithm, IBAS, which aims to solve the shortest path problem in a weighted directed acyclic graph with unknown vertex coordinates. This paper constructs three indexes for each vertex, i.e., the earliest arrival index, reverse earliest arrival index, and latest arrival index. We can compute the lower bound and the upper bound of the shortest distance from the source vertex to the target based on the three indexes and prune the intermediate vertice which are not in shortest path according to the lower and upper bounds. The IBAS algorithm not only makes use of the earliest arrival index to construct the evaluation function of the A-star algorithm, but also utilizes the three indexes to prune useless vertices, so as to improve the performance of the algorithm. Compared with the A-star algorithm, the additional time complexity and space complexity of the IBAS algorithm are O(|V| + |E|) and O(|V|), respectively. A real road network and benchmark datasets with large-scale network are selected to verify the performance of IBAS. Experimental results verify the effectiveness of the proposed algorithm.
... To our knowledge, the Google matrix approach or related ideas have been applied before in systems biology mostly to undirected networks, with few exceptions (e.g., [20]), in order to find activated network modules or to "smooth" high-throughput data [21][22][23], to establish connection of genes to diseases [24,25], to improve interpretability of genome-wide analyses [26][27][28] and to compute network-based cancer biomarkers [29,30]. PageRank approach has been used to quantify the functional proximity in undirected protein-protein interactions networks [31]. ...
Article
Full-text available
Signaling pathways represent parts of the global biological molecular network which connects them into a seamless whole through complex direct and indirect (hidden) crosstalk whose structure can change during development or in pathological conditions. We suggest a novel methodology, called Googlomics, for the structural analysis of directed biological networks using spectral analysis of their Google matrices, using parallels with quantum scattering theory, developed for nuclear and mesoscopic physics and quantum chaos. We introduce analytical “reduced Google matrix” method for the analysis of biological network structure. The method allows inferring hidden causal relations between the members of a signaling pathway or a functionally related group of genes. We investigate how the structure of hidden causal relations can be reprogrammed as a result of changes in the transcriptional network layer during cancerogenesis. The suggested Googlomics approach rigorously characterizes complex systemic changes in the wiring of large causal biological networks in a computationally efficient way.
... A popular method to leverage this prior knowledge consists in using a diffusion process on the gene network. This technique first appeared for the analysis of gene expression and GWAS data [Köhler et al., 2008;Kuperstein et al., 2015;Qian et al., 2014;Rapaport et al., 2007;Vanunu et al., 2010], and has more recently been used for mutation profiles [Babaei et al., 2013;Hofree et al., 2013;Hou and Ma, 2014;Jia and Zhao, 2014;Vandin et al., 2011]. Network diffusion processes allow smoothing binary vectors of somatic gene mutations into non-negative real-valued vectors of mutational statuses, where the mutational status of a gene increases when it is close to mutated genes in the network. ...
Thesis
Since the first sequencing of the human genome in the early 2000s, large endeavours have set out to map the genetic variability among individuals, or DNA alterations in cancer cells. They have laid foundations for the emergence of precision medicine, which aims at integrating the genetic specificities of an individual with its conventional medical record to adapt treatment, or prevention strategies.Translating DNA variations and alterations into phenotypic predictions is however a difficult problem. DNA sequencers and microarrays measure more variables than there are samples, which poses statistical issues. The data is also subject to technical biases and noise inherent in these technologies. Finally, the vast and intricate networks of interactions among proteins obscure the impact of DNA variations on the cell behaviour, prompting the need for predictive models that are able to capture a certain degree of complexity. This thesis presents novel methodological contributions to address these challenges. First, we define a novel representation for tumour mutation profiles that exploits prior knowledge on protein-protein interaction networks. For certain cancers, this representation allows improving survival predictions from mutation data as well as stratifying patients into meaningful subgroups. Second, we present a new learning framework to jointly handle data normalisation with the estimation of a linear model. Our experiments show that it improves prediction performances compared to handling these tasks sequentially. Finally, we propose a new algorithm to scale up sparse linear models estimation with two-way interactions. The obtained speed-up makes this estimation possible and efficient for datasets with hundreds of thousands of main effects, thereby extending the scope of such models to the data from genome-wide association studies.
... To understand the functional basis of network merging, relationships between drug-target and host-pathogen components were investigated on the basis of the shortest path parameter (a proxy to a 'functional distance' between proteins ( Kuperstein et al., 2015)) connecting them within DPI network. The shortest paths were calculated using Dijkstra algorithm (Dijkstra, 1959), and it was observed that these followed a normal distribution as indicated by the Shapiro-Wilk test (p-value ¼ 0.01). ...
Article
Nipah Virus (NiV) is a newly emergent paramyxovirus that has caused various outbreaks in Asian countries. Despite its acute pathogenicity and lack of approved therapeutics for human use, there is an urgent need to determine inhibitors against NiV. Hence, this work includes prospection of potential entry inhibitors by implementing an integrative structure- and network-based drug discovery approach. FDA-approved drugs were screened against attachment glycoprotein (NiV-G, PDB: 2VSM), one of the prime targets to inhibit viral entry, using a molecular docking approach that was benchmarked both on CCDC/ASTEX and known NIV-G inhibitor set. The predicted small molecules were prioritized on the basis of topological analysis of the chemical-protein interaction network, which was inferred by integrating the drug-target network, NiV-human interaction network, and human protein-protein interaction network. A total of 17 drugs were predicted to be NiV-G inhibitors using molecular docking studies that were further prioritized to 3 novel leads − Nilotinib, Deslanoside and Acetyldigitoxin − on the basis of topological analysis of inferred chemical-protein interaction network. While Deslanoside and Acetyldigitoxin belong to an already known class of anti-NiV inhibitors, Nilotinib belongs to Benzenoids chemical class that has not been reported hitherto for developing anti-NiV inhibitors. These identified drugs are expected to be successful in further experimental evaluation and therefore could be used for anti-Nipah drug discovery. Apart, we also obtained various insights into the underlying chemical-protein interaction network, based on which several important network nodes were predicted. The applicability of our proposed approach was also demonstrated by prospecting for anti-NiV phytochemicals on an independent dataset. Communicated by Ramaswamy H. Sarma
... Detailed descriptions of disease mechanisms on the level of molecular processes have recently become available [1,2], with many examples of practical applications in the field of cancer research [3][4][5][6][7]. These disease maps are needed for integrating scattered knowledge and for advanced data interpretation and hypothesis generation [1,2]. ...
Article
Full-text available
Detailed maps of the molecular basis of the disease are powerful tools for interpreting data and building predictive models. Modularity and composability are considered necessary network features for large-scale collaborative efforts to build comprehensive molecular descriptions of disease mechanisms. An effective way to create and manage large systems is to compose multiple subsystems. Composable network components could effectively harness the contributions of many individuals and enable teams to seamlessly assemble many individual components into comprehensive maps. We examine manually built versions of the RAS–RAF–MEK–ERK cascade from the Atlas of Cancer Signalling Network, PANTHER and Reactome databases and review them in terms of their reusability and composability for assembling new disease models. We identify design principles for managing complex systems that could make it easier for investigators to share and reuse network components. We demonstrate the main challenges including incompatible levels of detail and ambiguous representation of complexes and highlight the need to address these challenges.
... For example, some cancers induce higher mortality in men [19], whereas other tumors have shown significant differences in response to treatment in female patients [20]. The advent of high-throughput gene expression technologies has increased understanding of molecular correlates of malignancy, providing novel ways to stratify patients, determine prognosis, and predict sensitivity to therapeutic treatments (reviewed in [21]). Molecular signatures associated to cancer have demonstrated that some types of cancers have sex-biased gene expression [22]. ...
Article
Full-text available
Sex differences in incidence, prognosis, and treatment response have been described for many cancers. In malignant pleural mesothelioma (MPM), a lethal disease associated with asbestos exposure, men outnumber women 4 to 1, but women consistently live longer than men following surgery-based therapy. This study investigated whether tumor expression of genes associated with estrogen signaling could potentially explain observed survival differences. Two microarray datasets of MPM tumors were analyzed to discover estrogen-related genes associated with survival. A validation cohort of MPM tumors was selected to balance the numbers of men and women and control for competing prognostic influences. The RAS like estrogen regulated growth inhibitor (RERG) gene was identified as the most differentially-expressed estrogen-related gene in these tumors and predicted prognosis in discovery datasets. In the sex-matched validation cohort, low RERG expression was significantly associated with increased risk of death among women. No association between RERG expression and survival was found among men, and no relationship between estrogen receptor protein or gene expression and survival was found for either sex. Additional investigations are needed to elucidate the molecular mechanisms underlying this association and its sex specificity.
... Detailed descriptions of disease mechanisms on the level of molecular processes have recently become available [1,2] , with many examples of practical applications in the field of cancer research [3][4][5][6][7] . These disease maps are needed for integrating scattered knowledge and for advanced data interpretation and hypothesis generation [1,2] . ...
Preprint
Full-text available
Detailed maps of the molecular basis of the disease are powerful tools for interpreting data and building predictive models. Modularity and composability are considered necessary network features for large-scale collaborative efforts to build comprehensive molecular descriptions of disease mechanisms. An effective way to create and manage large systems is to compose multiple subsystems. Composable network components could effectively harness the contributions of many individuals and enable teams to seamlessly assemble many individual components into comprehensive maps. We examine manually-built versions of the RAS-RAF-MEK-ERK cascade from the Atlas of Cancer Signalling Network, PANTHER and Reactome databases and review them in terms of their reusability and composability for assembling new disease models. We identify design principles for managing complex systems that could make it easier for investigators to share and reuse network components. We demonstrate the main challenges including incompatible levels of detail and ambiguous representation of complexes and highlight the need to address these challenges.
... Representation of biochemical reactions from the following statements. Numbers correspond to the reactions in the diagram: « BRCA1 transcription (1) and translation (2) is positively regulated by E2F1/BRIT1* complex (3) and inhibited by p53 (4). BRCA1 protein is transported into nucleus (5), where CHEK2 kinase activates it by specific phosphorylation (6) and (7). ...
Preprint
Full-text available
Generation and usage of high-quality molecular signalling network maps can be augmented by standardising notations, establishing curation workflows and application of computational biology methods to exploit the knowledge contained in the maps. In this manuscript, we summarize the major aims and challenges of assembling information in the form of comprehensive maps of molecular interactions. Mainly, we share our experience gained while creating the Atlas of Cancer Signalling Network. In the step-by-step procedure, we describe the map construction process and suggest solutions for map complexity management by introducing a hierarchical modular map structure. In addition, we describe the NaviCell platform, a computational technology using Google Maps API to explore comprehensive molecular maps similar to geographical maps, and explain the advantages of semantic zooming principles for map navigation. We also provide the outline to prepare signalling network maps for navigation using the NaviCell platform. Finally, several examples of cancer high-throughput data analysis and visualization in the context of comprehensive signalling maps are presented.
... The distribution of gene weights from s k vectors can be projected on top of genome-wide biological network reconstructions where the network edges represent different types of interactions or regulations between genes and/or proteins. This can be further used for various types of network-based analyses, leading to the determination of biological network "hotspot" areas and eliminating the need of having a reference gene set collection [50]. The s k vectors (resulting from the analysis of transcriptomic or methylome data) can be projected onto genome and be a subject of peak-calling analysis, which can sometimes lead to associating a component to genomic alterations [33]. ...
Article
Full-text available
Independent component analysis (ICA) is a matrix factorization approach where the signals captured by each individual matrix factors are optimized to become as mutually independent as possible. Initially suggested for solving source blind separation problems in various fields, ICA was shown to be successful in analyzing functional magnetic resonance imaging (fMRI) and other types of biomedical data. In the last twenty years, ICA became a part of the standard machine learning toolbox, together with other matrix factorization methods such as principal component analysis (PCA) and non-negative matrix factorization (NMF). Here, we review a number of recent works where ICA was shown to be a useful tool for unraveling the complexity of cancer biology from the analysis of different types of omics data, mainly collected for tumoral samples. Such works highlight the use of ICA in dimensionality reduction, deconvolution, data pre-processing, meta-analysis, and others applied to different data types (transcriptome, methylome, proteome, single-cell data). We particularly focus on the technical aspects of ICA application in omics studies such as using different protocols, determining the optimal number of components, assessing and improving reproducibility of the ICA results, and comparison with other popular matrix factorization techniques. We discuss the emerging ICA applications to the integrative analysis of multi-level omics datasets and introduce a conceptual view on ICA as a tool for defining functional subsystems of a complex biological system and their interactions under various conditions. Our review is accompanied by a Jupyter notebook which illustrates the discussed concepts and provides a practical tool for applying ICA to the analysis of cancer omics datasets.
... For example, VANTED tool [5] creates a classification tree according to the KEGG pathway hierarchy and shows a biological network with omics data as barplots or pie-charts attached to the nodes which allows to visualize more complex data than by simple node coloring. NaviCell [6] and related pathway database Atlas of Cancer Signalling Network (ACSN) together with standard heat maps and barplots provide more flexible data visualization tools such as glyphs (symbols with configurable shape, size and color) and map staining (using the network background for visualization) [7]. An interesting approach for data visualization using biological networks was developed in NetGestalt online tool [8] which uses a NetSAM R package to create modules by hierarchical ordering of the network in one dimension and visualizes high-throughput data accordingly to a chosen track as a combination of barplots and heat maps. ...
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The knowledge of cell molecular mechanisms implicated in human diseases is expanding and should be converted into guidelines for deciphering pathological cell signaling and suggesting appropriate treatment. The basic assumption is that during a pathological transformation, the cell does not create new signaling mechanisms, but rather it hijacks the existing molecular programs. This affects not only intracellular functions, but also a crosstalk between different cell types resulting in a new, yet pathological status of the system. There is a certain combination of molecular characteristics dictating specific cell signaling states that sustains the pathological disease status. Identifying and manipulating the key molecular players controlling these cell signaling states, and shifting the pathological status toward the desired healthy phenotype, are the major challenge for molecular biology of human diseases. http://arxiv.org/abs/1512.05234
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Many forms of cancer have multiple subtypes with different causes and clinical outcomes. Somatic tumor genome sequences provide a rich new source of data for uncovering these subtypes but have proven difficult to compare, as two tumors rarely share the same mutations. Here we introduce network-based stratification (NBS), a method to integrate somatic tumor genomes with gene networks. This approach allows for stratification of cancer into informative subtypes by clustering together patients with mutations in similar network regions. We demonstrate NBS in ovarian, uterine and lung cancer cohorts from The Cancer Genome Atlas. For each tissue, NBS identifies subtypes that are predictive of clinical outcomes such as patient survival, response to therapy or tumor histology. We identify network regions characteristic of each subtype and show how mutation-derived subtypes can be used to train an mRNA expression signature, which provides similar information in the absence of DNA sequence.
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Background Public repositories of biological pathways and networks have greatly expanded in recent years. Such databases contain many pathways that facilitate the analysis of high-throughput experimental work and the formulation of new biological hypotheses to be tested, a fundamental principle of the systems biology approach. However, large-scale molecular maps are not always easy to mine and interpret. Results We have developed BiNoM (Biological Network Manager), a Cytoscape plugin, which provides functions for the import-export of some standard systems biology file formats (import from CellDesigner, BioPAX Level 3 and CSML; export to SBML, CellDesigner and BioPAX Level 3), and a set of algorithms to analyze and reduce the complexity of biological networks. BiNoM can be used to import and analyze files created with the CellDesigner software. BiNoM provides a set of functions allowing to import BioPAX files, but also to search and edit their content. As such, BiNoM is able to efficiently manage large BioPAX files such as whole pathway databases (e.g. Reactome). BiNoM also implements a collection of powerful graph-based functions and algorithms such as path analysis, decomposition by involvement of an entity or cyclic decomposition, subnetworks clustering and decomposition of a large network in modules. Conclusions Here, we provide an in-depth overview of the BiNoM functions, and we also detail novel aspects such as the support of the BioPAX Level 3 format and the implementation of a new algorithm for the quantification of pathways for influence networks. At last, we illustrate some of the BiNoM functions on a detailed biological case study of a network representing the G1/S transition of the cell cycle, a crucial cellular process disturbed in most human tumors.
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Large-scale cancer genome sequencing has uncovered thousands of gene mutations, but distinguishing tumor driver genes from functionally neutral passenger mutations is a major challenge. We analyzed 800 cancer genomes of eight types to find single-nucleotide variants (SNVs) that precisely target phosphorylation machinery, important in cancer development and drug targeting. Assuming that cancer-related biological systems involve unexpectedly frequent mutations, we used novel algorithms to identify genes with significant phosphorylation-associated SNVs (pSNVs), phospho-mutated pathways, kinase networks, drug targets, and clinically correlated signaling modules. We highlight increased survival of patients with TP53 pSNVs, hierarchically organized cancer kinase modules, a novel pSNV in EGFR, and an immune-related network of pSNVs that correlates with prolonged survival in ovarian cancer. Our findings include multiple actionable cancer gene candidates (FLNB, GRM1, POU2F1), protein complexes (HCF1, ASF1), and kinases (PRKCZ). This study demonstrates new ways of interpreting cancer genomes and presents new leads for cancer research.
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In mammalian cells more than 90% of double-strand breaks are repaired by NHEJ. Impairment of this pathway is associated with cell cycle arrest, cell death, genomic instability and cancer. Human diseases such as Nijmegen breakage syndrome, due to mutations in the NBS1 gene, produce defects in resection of double-strand breaks. NBS1 hypomorphic mutant mice are viable, and cells from these mice are defective in S phase and G2/M checkpoints. NBS1 polymorphisms have been associated with increased risk of breast cancer. We previously demonstrated that estradiol protected estrogen receptor (ER)-positive (+) breast cancer cell lines against double-strand breaks and cell death. We now demonstrate that protection from double-strand break damage in ER+ cells is mediated via regulation by c-myc, p53, CBP and SRC1 coactivators in intron 1 of the NBS1 gene. We concluded that NBS1 is responsible for estradiol-mediated protection from double-strand breaks in ER+ breast cancer cells.
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The Biological General Repository for Interaction Datasets (BioGRID: http//thebiogrid.org) is an open access archive of genetic and protein interactions that are curated from the primary biomedical literature for all major model organism species. As of September 2012, BioGRID houses more than 500 000 manually annotated interactions from more than 30 model organisms. BioGRID maintains complete curation coverage of the literature for the budding yeast Saccharomyces cerevisiae, the fission yeast Schizosaccharomyces pombe and the model plant Arabidopsis thaliana. A number of themed curation projects in areas of biomedical importance are also supported. BioGRID has established collaborations and/or shares data records for the annotation of interactions and phenotypes with most major model organism databases, including Saccharomyces Genome Database, PomBase, WormBase, FlyBase and The Arabidopsis Information Resource. BioGRID also actively engages with the text-mining community to benchmark and deploy automated tools to expedite curation workflows. BioGRID data are freely accessible through both a user-defined interactive interface and in batch downloads in a wide variety of formats, including PSI-MI2.5 and tab-delimited files. BioGRID records can also be interrogated and analyzed with a series of new bioinformatics tools, which include a post-translational modification viewer, a graphical viewer, a REST service and a Cytoscape plugin.
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Complete knowledge of all direct and indirect interactions between proteins in a given cell would represent an important milestone towards a comprehensive description of cellular mechanisms and functions. Although this goal is still elusive, considerable progress has been made—particularly for certain model organisms and functional systems. Currently, protein interactions and associations are annotated at various levels of detail in online resources, ranging from raw data repositories to highly formalized pathway databases. For many applications, a global view of all the available interaction data is desirable, including lower-quality data and/or computational predictions. The STRING database (http://string-db.org/) aims to provide such a global perspective for as many organisms as feasible. Known and predicted associations are scored and integrated, resulting in comprehensive protein networks covering >1100 organisms. Here, we describe the update to version 9.1 of STRING, introducing several improvements: (i) we extend the automated mining of scientific texts for interaction information, to now also include full-text articles; (ii) we entirely re-designed the algorithm for transferring interactions from one model organism to the other; and (iii) we provide users with statistical information on any functional enrichment observed in their networks.
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We analysed primary breast cancers by genomic DNA copy number arrays, DNA methylation, exome sequencing, messenger RNA arrays, microRNA sequencing and reverse-phase protein arrays. Our ability to integrate information across platforms provided key insights into previously defined gene expression subtypes and demonstrated the existence of four main breast cancer classes when combining data from five platforms, each of which shows significant molecular heterogeneity. Somatic mutations in only three genes (TP53, PIK3CA and GATA3) occurred at >10% incidence across all breast cancers; however, there were numerous subtype-associated and novel gene mutations including the enrichment of specific mutations in GATA3, PIK3CA and MAP3K1 with the luminal A subtype. We identified two novel protein-expression-defined subgroups, possibly produced by stromal/microenvironmental elements, and integrated analyses identified specific signalling pathways dominant in each molecular subtype including a HER2/phosphorylated HER2/EGFR/phosphorylated EGFR signature within the HER2-enriched expression subtype. Comparison of basal-like breast tumours with high-grade serous ovarian tumours showed many molecular commonalities, indicating a related aetiology and similar therapeutic opportunities. The biological finding of the four main breast cancer subtypes caused by different subsets of genetic and epigenetic abnormalities raises the hypothesis that much of the clinically observable plasticity and heterogeneity occurs within, and not across, these major biological subtypes of breast cancer.
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Importance of the field: Post-genome drug development has been driven by the need to study biological perturbations at the molecular system level. Systems biology visualization tools can help researchers extract hidden patterns from complex and large Omics data sets, model disease molecular mechanisms, and identify drug targets and drugs with good pharmacological and toxicological profiles. Areas covered in this review: This review covers basic concepts in developing and applying information visualization tools to systems biology. We describe a framework and basic data representation schemes for visual data analysis in systems biology. We review major application areas of these visualization tools within drug discovery by focusing on early-stage drug discovery tasks such as disease biology modeling, target identifications and lead identification. We also show case studies and summarize our experience using visualization tools as lessons to our readers. What the reader will gain: The reader will understand what visualization tools are available for diverse types of systems biology studies in drug discovery and understand how these tools can help advance drug development. Take home message: In spite of the complexity inherent in systems biology, proper use of information visualization tools may reveal emerging properties hidden in the data and enhance chances of success for drug discovery.
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Many genome-scale studies in molecular biology deliver results in the form of a ranked list of gene names, accordingly to some scoring method. There is always the question how many top-ranked genes to consider for further analysis, for example, in order creating a diagnostic or predictive gene signature for a disease. This question is usually approached from a statistical point of view, without considering any biological properties of top-ranked genes or how they are related to each other functionally. Here we suggest a new method for selecting a number of genes in a ranked gene list such that this set forms the Optimally Functionally Enriched Network (OFTEN), formed by known physical interactions between genes or their products. The method allows associating a network with the gene list, providing easier interpretation of the results and classifying the genes or proteins accordingly to their position in the resulting network. We demonstrate the method on four breast cancer datasets and show that 1) the resulting gene signatures are more reproducible from one dataset to another compared to standard statistical procedures and 2) the overlap of these signatures has significant prognostic potential. The method is implemented in BiNoM Cytoscape plugin (http://binom.curie.fr).
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Knowledge environment (KE) describes the collection of electronic networking tools that have been and continue to be developed by AAAS and Stanford University libraries. Knowledge environments use practical, production-quality tools to systematize the consensus knowledge within a scientific domain and facilitate users' access to that knowledge. Science's Signal Transduction Knowledge Environment (STKE) is the first in this new concept in electronic publishing that combines the traditional, albeit electronic, publishing of articles, such as reviews, perspectives, and protocols, with tools for organizing and collating information in the cross-disciplinary field of signal transduction. One of the major tools developed for the STKE is the Connections Map database and the software (called CMADES [Connections Maps Authority Data Entry Software]) created to facilitate data entry by Pathway Authorities. The Connections Maps are a graphical representation of a database of information about the molecules involved in cellular signaling cascades. CMADES automates many of the functions involved in adding data into the Connections Maps database, such as references and descriptors, as well as allowing the Authorities to indicate the relationships between the components in the pathway through the use of a graphing tool. CMADES and the Connections Maps represent evolving tools that assist the Authorities in systemizing information regarding a particular system at the organism- and cell-specific level and the canonical level, as well as provide the STKE user with organized and expert-supplied information about signal transduction pathways.
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The loss of hormonal dependency of breast tumor cells is often accompanied with the appearance of epithelial-mesenchymal transition (EMT) features and increase in cell metastasis and invasiveness. The central role in the EMT belongs to transcription factors Snail responded for the decrease in E-cadherin expression and cell contacts, stimulation of cell mobility and invasiveness. Aim was to study the relationships between estrogen receptor machinery and Snail1 signaling, and mechanism of Snail1 regulation in hormone-resistant breast cancer cells. The experiments were performed on the estrogen-dependent MCF-7 breast cancer cells, estrogen-hyposensitive MCF-7/LS subline generated through long-term cultivation of the parental cells in steroid-free medium, and ER-negative estrogen-resistant HBL-100 cells. Snail1, estrogen receptor, p65 NF-κB, E-cadherin levels were analyzed by Western blot. We found that decrease in the estrogen dependency is correlated with increase in Snail1 expression and activity, we demonstrated the Snail1 involvement in the negative regulation of ER, and showed that Snail1 inhibition partially restores the sensitivity of the estrogen-hyposensitive cells to antiestrogen tamoxifen. Furthermore, NF-κB was found to serve as a positive regulator of Snail1 in breast cancer cells, and simultaneous inhibition of NF-κB and Snail1 resulted in additional increase in cell response to tamoxifen. In general, the results obtained demonstrate the phenomenon of Snail1 activation in the hormone-resistant breast cancer cells, and show that Snail1 and NF-κB may serve as an important targets in the treatment of breast cancer, both estrogen-dependent and estrogen-independent tumors.
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In this brief overview we discuss the association between Wnt signaling and colon cell biology and tumorigenesis. Our current understanding of the role of Apc in the β-catenin destruction complex is compared with potential roles for Apc in cell adhesion and migration. The requirement for phosphorylation in the proteasomal-mediated degradation of β-catenin is contrasted with roles for phospho-β-catenin in the activation of transcription, cell adhesion and migration. The synergy between Myb and β-catenin regulation of transcription in crypt stem cells during Wnt signaling is discussed. Finally, potential effects of growth factor regulatory systems, Apc or truncated-Apc on crypt morphogenesis, stem cell localization and crypt fission are considered.
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The hallmarks of cancer comprise six biological capabilities acquired during the multistep development of human tumors. The hallmarks constitute an organizing principle for rationalizing the complexities of neoplastic disease. They include sustaining proliferative signaling, evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis. Underlying these hallmarks are genome instability, which generates the genetic diversity that expedites their acquisition, and inflammation, which fosters multiple hallmark functions. Conceptual progress in the last decade has added two emerging hallmarks of potential generality to this list-reprogramming of energy metabolism and evading immune destruction. In addition to cancer cells, tumors exhibit another dimension of complexity: they contain a repertoire of recruited, ostensibly normal cells that contribute to the acquisition of hallmark traits by creating the "tumor microenvironment." Recognition of the widespread applicability of these concepts will increasingly affect the development of new means to treat human cancer.