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Network-based approaches for drug response prediction and targeted therapy development in cancer
Abstract
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.
... Furthermore, data analysis in the context of signalling networks can help to detect patterns in the data projected onto the molecular mechanisms represented in the signalling maps, verifying enriched functional modules ('hot' deregulated areas), key players, and 'bottleneck' points [3] [5]. Correlating status of those network variables with the phenotype, as drug resistance or patient survival, followed by clustering methods, allows stratifying patients according to their integrated network-based molecular portraits and to design appropriate therapeutic intervention schemes [6]. ...
... This partnership aims at applying similar approaches as described in this review, that will lead to identification of emerging disease hallmarks. This will help to study diseases comorbidity, predict response to standard treatments and to suggest improved individual intervention schemes based on drug repositioning [6]. ...
Initiation and progression of cancer involve multiple molecular mechanisms. The knowledge on these mechanisms is expanding and should be converted into guidelines for tackling the disease. We discuss here formalization of biological knowledge into a comprehensive resource Atlas of Cancer Signalling Network (ACSN) and Google Maps-based tool NaviCell that supports map navigation. The application of maps for omics data visualisation in the context of signalling maps is possible using NaviCell Web Service module and NaviCom tool for generation of network-based molecular portraits of cancer using multi-level omics data. We review how these resources and tools are applied for cancer pre-clinical studies among others for rationalizing synergistic effect of drugs and designing complex disease stage-specific druggable interventions following structural analysis of the maps together with omics data. Modules and maps of ACSN as signatures of biological functions, can help in cancer data analysis and interpretation. In addition, they can also be used to find association between perturbations in particular molecular mechanisms to the risk of a specific cancer type development. These approaches and beyond help to study interplay between molecular mechanisms of cancer, deciphering how gene interactions govern hallmarks of cancer in specific context. We discuss a perspective to develop a flexible methodology and a pipeline to enable systematic omics data analysis in the context of signalling network maps, for stratifying patients and suggesting interventions points and drug repositioning in cancer and other human diseases.
... Correlating the status of those network variables with the phenotype, as drug resistance or patient survival, can be followed by clustering approaches to allow us stratifying patients according to their network-based molecular portraits. Moreover, these approaches can help in suggesting intervention points and in designing appropriate therapeutic schemes [6]. ...
... This partnership aims at applying similar approaches as described in this review that will lead to identifying emerging disease hallmarks of various disorders, as CNS-related Alzheimer's [54] and Parkinson diseases [56], influenza [57] and others. This will help in studying disease comorbidities, predict response to standard treatments and to suggest improved individual intervention schemes based on drug repositioning [6]. In addition, there is an ongoing effort to include ACSN content into aggregated pathway databases, such as Pathway Commons [58] and WikiPathways [59] such that it can be used in many projects through standard interfaces as Cytoscape [60]. ...
Cancer initiation and progression are associated with multiple molecular mechanisms. The knowledge of these mechanisms is expanding and should be converted into guidelines for tackling the disease. Here, we discuss the formalization of biological knowledge into a comprehensive resource: the Atlas of Cancer Signalling Network (ACSN) and the Google Maps-based tool NaviCell, which supports map navigation. The application of ACSN for omics data visualization, in the context of signalling maps, is possible via the NaviCell Web Service module and through the NaviCom tool. It allows generation of network-based molecular portraits of cancer using multilevel omics data. We review how these resources and tools are applied for cancer preclinical studies. Structural analysis of the maps together with omics data helps to rationalize the synergistic effects of drugs and allows design of complex disease stage-specific druggable interventions. The use of ACSN modules and maps as signatures of biological functions can help in cancer data analysis and interpretation. In addition, they empowered finding of associations between perturbations in particular molecular mechanisms and the risk to develop a specific type of cancer. These approaches are helpful, among others, to study the interplay between molecular mechanisms of cancer. It opens an opportunity to decipher how gene interactions govern the hallmarks of cancer in specific contexts. We discuss a perspective to develop a flexible methodology and a pipeline to enable systematic omics data analysis in the context of signalling network maps, for stratifying patients and suggesting interventions points and drug repositioning in cancer and other diseases.
... Ramos and Bentires-Alj [9] explained that the plasticity of tumour cells leads to the development of drug resistance by distinct mechanisms, including the following: (i) mutations in the target, (ii) reactivation of the targeted pathway, (iii) hyperactivation of alternative pathways, and (iv) cross-talk with the microenvironment. Dorel et al. [10] stated that signalling pathways implicated in cancer create a complex network with numerous regulatory loops and redundant pathways and that this complexity also explains the frequent failure of the one-drug-one-target paradigm of treatment, resulting in drug resistance in patients. These authors proposed that cancer treatment should be extended to a combination of therapeutic approaches to overcome the robustness of the cell signalling network [10]. ...
... Dorel et al. [10] stated that signalling pathways implicated in cancer create a complex network with numerous regulatory loops and redundant pathways and that this complexity also explains the frequent failure of the one-drug-one-target paradigm of treatment, resulting in drug resistance in patients. These authors proposed that cancer treatment should be extended to a combination of therapeutic approaches to overcome the robustness of the cell signalling network [10]. As highlighted in the following section, the consumption of certain types of food components/diet regimens can help as an added weapon to combat certain types of cancer, in addition to conventional treatments. ...
This review is part of a special issue entitled “Role of dietary pattern, foods, nutrients and nutraceuticals in supporting cancer prevention and treatment” and describes a pharmacological strategy to determine the potential contribution of food-related components as anticancer agents against established cancer. Therefore, this review does not relate to chemoprevention, which is analysed in several other reviews in the current special issue, but rather focuses on the following: i) the biological events that currently represent barriers against the treatment of certain types of cancers, primarily metastatic cancers; ii) the in vitro and in vivo pharmacological pre-clinical tests that can be used to analyse the potential anticancer effects of food-related components; and iii) several examples of food-related components with anticancer effects. This review does not represent a catalogue-based listing of food-related components with more or less anticancer activity. By contrast, this review proposes an original pharmacological strategy that researchers can use to analyse the potential anticancer activity of any food-related component—e.g., by considering the crucial characteristics of cancer biological aggressiveness. This review also highlights that cancer patients undergoing chemotherapy should restrict the use of “food complements” without supervision by a medical nutritionist. By contrast, an equilibrated diet that includes the food-related components listed herein would be beneficial for cancer patients who are not undergoing chemotherapy.
... Furthermore, data analysis in the context of signaling networks can help to detect data distribution patterns across molecular mechanisms on the signaling maps, verifying network variables as enriched functional modules ('hot' deregulated areas), key players, 'bottleneck' points (Wang et al., 2015). Correlating those network variables with the phenotype, as drug resistance or patient survival, followed by clustering methods allows to stratify patients according to their integrated network-based molecular portraits and to suggest appropriate intervention scheme (Dorel et al., 2015). Application of signaling network to explain mechanism of drug action in cancer is shown in Structural analysis and modeling of different scenarios (e.g. ...
... This approach helps to understand interplay between molecular mechanisms in cancer, deciphering how gene interactions govern hallmarks of cancer (Hanahan and Weinberg, 2011) in specific context and use this knowledge to stratify patients accordingly. This will lead to new therapeutic strategies, rationalizing the use of targeted inhibitors (Dorel et al., 2015). An advantage of representing the biological processes in a graphical form is demonstrating collectively multiple cross-talks between components of different cell signaling processes. ...
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.
... Furthermore, data analysis in the context of signaling networks can help to detect data distribution patterns across molecular mechanisms on the signaling maps, verifying network variables as enriched functional modules ('hot' deregulated areas), key players, 'bottleneck' points ( Wang et al., 2015). Correlating those network variables with the phenotype, as drug resistance or patient survival, followed by clustering methods allows to stratify patients according to their integrated network-based molecular portraits and to suggest appropriate intervention scheme (Dorel et al., 2015). Application of signaling network to explain mechanism of drug action in cancer is shown in Chapter 3.1. ...
... This approach helps to understand interplay between molecular mechanisms in cancer, deciphering how gene interactions govern hallmarks of cancer ( Hanahan and Weinberg, 2011) in specific context and use this knowledge to stratify patients accordingly. This will lead to new therapeutic strategies, rationalizing the use of targeted inhibitors (Dorel et al., 2015). An advantage of representing the biological processes in a graphical form is demonstrating collectively multiple cross-talks between components of different cell signaling processes. ...
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
... In addition, nodes with similar domain contexts in the protein interaction network may share similar functions and behaviours [11]. Prediction of drug response based on protein interaction network may help to better interpret the patterns observed in highthroughput data of diseases [12]. This approach offers an advantage over existing targeted therapeutic strategies that are based on identification of abnormal molecular expression of a given disease which narrow down the spectrum of putative targets. ...
Corynebacterium pseudotuberculosis is a Gram-positive pathogen that commonly causes caseous lymphadenitis which occurs in sheep, goats, cattle, buffalo and horses. This disease has long been shown to be a major cause of economic loss on sheep industries. Dimethyl sulfoxide (DMSO) is known to be effective against a wide spectrum of pathogens however, its efficacy against C. pseudotuberculosis biofilm remains uncertain. The objective of this study was to predict the antibiofilm potential of DMSO against C. pseudotuberculosis using in silico protein interaction network analysis and experimentally determine the antibiofilm activity using standard microplate assay system. As compared to the protein interaction network of S. typhimurium biofilm that had previously been shown to be inhibited by DMSO, the protein interaction network of C. pseudotuberculosis showed similar nodes, hub proteins and functional linkages between glycolytic enzymes, suggesting a network similarity and the same inhibitory effect. Further experimental validation revealed that the treatment with DMSO significantly (p<0.05) inhibited C. pseudotuberculosis biofilm at all tested concentrations (1.56%-50%). The findings from the present study suggest the potential application of DMSO in controlling caseous lymphadenitis in ruminants.
... We have summarized some network-based methods in Table 3. Due to the increase in drug development costs and the decrease in the number of newly approved drugs, it is necessary to determine the new value of existing drugs. Some network-based methods help design unique drug target combinations and combined drugs therapies (68), and improve the treatment of specific patients through powerful channels (69). ...
Drug repositioning is a new way of applying the existing therapeutics to new disease indications. Due to the exorbitant cost and high failure rate in developing new drugs, the continued use of existing drugs for treatment, especially anti-tumor drugs, has become a widespread practice. With the assistance of high-throughput sequencing techniques, many efficient methods have been proposed and applied in drug repositioning and individualized tumor treatment. Current computational methods for repositioning drugs and chemical compounds can be divided into four categories: (i) feature-based methods, (ii) matrix decomposition-based methods, (iii) network-based methods, and (iv) reverse transcriptome-based methods. In this article, we comprehensively review the widely used methods in the above four categories. Finally, we summarize the advantages and disadvantages of these methods and indicate future directions for more sensitive computational drug repositioning methods and individualized tumor treatment, which are critical for further experimental validation.
... Specifically, on disease like cancer, therapeutics approach of targeting single gene retain the connection through feedback, compensatory and redundant loops. Understanding of biological signal machinery gives effective intervention schemes (Dorel et al., 2015;Peng and Schork, 2014). ...
... Specifically, on disease like cancer, therapeutics approach of targeting single gene retain the connection through feedback, compensatory and redundant loops. Understanding of biological signal machinery gives effective intervention schemes (Dorel et al., 2015;Peng and Schork, 2014). ...
Computational side-effect prediction tools have been used in rational drug design to decrease the late-stage failure of the drugs under trial. Irrational selection of cancer drug targets in the deregulated MAPK pathways causes more side effects. Quantitative data on the network centralities and biological features degree, radiality, eccentricity, closeness, bridging, stress, pagerank centralities, essentiality, pathway-specific proteins, disease-causing proteins, protein domains and the other functional features exploited. We trained an artificial neural network with 15 selected features for the binary classification of side effects causing and less side-effect causing drug targets among the non-targeted proteins. Inter-relationship among the node centralities revealed three clusters with positive correlations. Among three clusters of centralities, the top centrality nodes overlap within the clusters playing multiple roles in the complex networks. Top-ranked proteins among the degree, eccentricity, betweenness centralities, possessing GO-based molecular function, involved in more than one biocarta pathways, domain content is prone to cause a number of side effects than other centralities and functional features. We predicted the following 15 less side effect causing cancer drug targets - Shc, Rap 1a, Mos, Tpl-2, PAC1, 4EBP1, GAB1, LAD, MEF2, ZAK, GADD45, TAB2, TAB1, ELK1 and SRF.
... And most of the important methods were summarized in the literature (5)(6)(7). One popular method to identify key nodes as therapeutic targets was network construction combined with gene expression profiles (8)(9)(10). Some of such methods mainly used PPI networks to search for disease-related genes based on the principle that protein interactions played critical roles in biological processes such as cell cycle control, signal transduction, cell location and apoptosis (11). ...
... To address these challenges, a systems biology approach is needed 22 . Formalization of biological knowledge in a form of comprehensive signaling maps, both at the intra-and intercellular levels, helps to integrate information from multiple research papers 23 . There are numerous public databases containing signaling pathways related to innate-immune response such as KEGG 24 and REACTOME 25 , which are quiet comprehensive, but contain mostly generic mechanisms. ...
The lack of integrated resources depicting the complexity of the innate immune response in cancer represents a bottleneck for high-throughput data interpretation. To address this challenge, we perform a systematic manual literature mining of molecular mechanisms governing the innate immune response in cancer and represent it as a signalling network map. The cell-type specific signalling maps of macrophages, dendritic cells, myeloid-derived suppressor cells and natural killers are constructed and integrated into a comprehensive meta map of the innate immune response in cancer. The meta-map contains 1466 chemical species as nodes connected by 1084 biochemical reactions, and it is supported by information from 820 articles. The resource helps to interpret single cell RNA-Seq data from macrophages and natural killer cells in metastatic melanoma that reveal different anti- or pro-tumor sub-populations within each cell type. Here, we report a new open source analytic platform that supports data visualisation and interpretation of tumour microenvironment activity in cancer.
... And most of the important methods were summarized in the literature (5)(6)(7). One popular method to identify key nodes as therapeutic targets was network construction combined with gene expression profiles (8)(9)(10). Some of such methods mainly used PPI networks to search for disease-related genes based on the principle that protein interactions played critical roles in biological processes such as cell cycle control, signal transduction, cell location and apoptosis (11). ...
Prioritization of cancer-related genes from gene expression profiles and proteomic data is vital to improve the targeted therapies research. Although computational approaches have been complementing high-throughput biological experiments on the understanding of human diseases, it still remains a big challenge to accurately discover cancer-related proteins/genes via automatic learning from large-scale protein/gene expression data and protein–protein interaction data. Most of the existing methods are based on network construction combined with gene expression profiles, which ignore the diversity between normal samples and disease cell lines. In this study, we introduced a deep learning model based on a sparse auto-encoder to learn the specific characteristics of protein interactions in cancer cell lines integrated with protein expression data. The model showed learning ability to identify cancer-related proteins/genes from the input of different protein expression profiles by extracting the characteristics of protein interaction information, which could also predict cancer-related protein combinations. Comparing with other reported methods including differential expression and network-based methods, our model got the highest area under the curve value (>0.8) in predicting cancer-related genes. Our study prioritized ~500 high-confidence cancer-related genes; among these genes, 211 already known cancer drug targets were found, which supported the accuracy of our method. The above results indicated that the proposed auto-encoder model could computationally prioritize candidate proteins/genes involved in cancer and improve the targeted therapies research.
... Understanding the biomolecular interaction within the cellular environment can solve pathological consequences. (Dorel et al. 2015, Csermely, Korcsmáros, Kiss, London and Nussinov 2013, Kibble et al. 2015, Martínez-Jiménez and Marti-Renom 2016 Network biology along with the computational tools opens up a lot of opportunities to explore drug targets. Identification of potential and efficient molecular target can save from the late stage failure in clinical trials. ...
MAPK pathways are conserved from yeast to mammal and contribute towards the
cellular functional activities. Dysregulation of MAPK pathways causes uncontrolled
proliferation and cell death leading to play the vital role in several cancer types. Each
of the MAPK pathways is explored for drug targets in cancer over two decades. Drug
target identification is challenging due to an intertwined biological process of the targets
and the side effects caused. In this thesis, I aim to build the network based methods to
identify efficient cancer drug targets with fewer side effects.
I constructed the network of MAPK pathways by considering cross-talks among the
four MAPK pathways ERK1/2, ERK5, JNK and p38 pathways. By considering the
information flow in the network, I used directed, undirected and weighted graph representation
to reveal the hidden patterns / organizational principles in the network. Node
level analysis of the network is carried out by considering protein structural and functional
properties like domain, GO annotations, essentiality, protein types, subcellular
localization, cancerous/non-cancerous nature, targeted / non-targeted status, and inter
and intra-pathway properties to identify novel drug targets.
Network topological features like degree distribution, centrality and motifs substructure
are explored to identify less side effect causing drug targets. Specifically, I
developed an alternate centrality measure to identify drug targets in the nonlinear and
nonhierarchical signaling network. A computational algorithm is developed and implemented
to identify the cluster of nodes which are not involved in alternate activation
mechanism. Furthermore, lung and breast cancer-specific data is used to identify
disease-specific drug targets.
A novel set of overlapping drug targets identified by using six different methods are
4EBP1, RNPK, MLTKa/b, MLK3, and eEF2K. I tried to identify the targets which are
effective in cancer and less involved in cellular functional activities.
... Integrating highthroughput patient data with information about the underlying machinery has the potential to reveal molecular patterns specific to disease subtypes and inform combinatorial diagnostics or therapeutics. 49 This enables the identification of a set of interactions, whose joint alteration can shift the state of the network from unfavourable toward the desired outcome. For instance, analysing disease map's perturbations can help predicting sensitivity to drugs based on network topology and choosing a patient-specific combination of drug targets. ...
The development of computational approaches in systems biology has reached a state of maturity that allows their transition to systems medicine. Despite this progress, intuitive visualisation and context-dependent knowledge representation still present a major bottleneck. In this paper, we describe the Disease Maps Project, an effort towards a community-driven computationally readable comprehensive representation of disease mechanisms. We outline the key principles and the framework required for the success of this initiative, including use of best practices, standards and protocols. We apply a modular approach to ensure efficient sharing and reuse of resources for projects dedicated to specific diseases. Community-wide use of disease maps will accelerate the conduct of biomedical research and lead to new disease ontologies defined from mechanism-based disease endotypes rather than phenotypes.
... Understanding of biological signal machinery gives effective intervention schemes. [10,11] In the literature, several centrality measure of the networks are individually link side effects like Degree [12], Betweeness [12], Radiality [13,14], Eccentricity [13,15,16], Closeness [13,15], Bridging [17], Stress [13,14] and Pagerank [18]. Drug targets involved in more number of functional features also individually linked to side effects like Biocarta pathway [19,20], Biological process [21], Cellular component [22], Molecular functions [21], Interpro domain [23], OMIM diseases and Essentiality. ...
Computational side-effect prediction tools have been used in rational
drug design to decrease the late-stage failure of the drugs under trial. Irrational
selection of cancer drug targets in the deregulated MAPK pathways causes more
side effects. Quantitative data on the network centralities and biological features
- degree, radiality, eccentricity, closeness, bridging, stress, pagerank centralities,
essentiality, pathway-specific proteins, disease-causing proteins, protein domains
and the other functional features exploited.We trained an artificial neural network
with 15 selected features for the binary classification of side effects causing
and less side-effect causing drug targets among the non-targeted proteins. Interrelationship
among the node centralities revealed three clusters with positive
correlations. Among three clusters of centralities, the top centrality nodes overlap
within the clusters playing multiple roles in the complex networks. Top ranked
proteins among the degree, eccentricity, betweenness centralities, possessing GObased
molecular function, involved in more than one biocarta pathways, domain
content are prone to cause a number of side effects than other centralities and
functional features.We predicted the following 15 less side effect causing cancer
drug targets - Shc, Rap 1a, Mos, Tpl-2, PAC1, 4EBP1, GAB1, LAD, MEF2, ZAK,
GADD45, TAB2, TAB1, ELK1 and SRF.
... This complexity explains frequent failure of one-drug-one-target paradigm of treatment, resulting in drug resistance in patients. To overcome the robustness of the cellular signaling network, the treatment should be extended to a combination therapy scheme [68]. ...
The Disease Maps Project builds on a network of scientific and clinical groups that exchange best practices, share information and develop systems biomedicine tools. The project aims for an integrated, highly curated and user-friendly platform for disease-related knowledge. The primary focus of disease maps is on interconnected signaling, metabolic and gene regulatory network pathways represented in standard formats. The involvement of domain experts ensures that the key disease hallmarks are covered and relevant, up-to-date knowledge is adequately represented. Expert-curated and computer readable, disease maps may serve as a compendium of knowledge, allow for data-supported hypothesis generation or serve as a scaffold for the generation of predictive mathematical models. This article summarizes the 2nd Disease Maps Community meeting, highlighting its important topics and outcomes. We outline milestones on the roadmap for the future development of disease maps, including creating and maintaining standardized disease maps; sharing parts of maps that encode common human disease mechanisms; providing technical solutions for complexity management of maps; and Web tools for in-depth exploration of such maps. A dedicated discussion was focused on mathematical modeling approaches, as one of the main goals of disease map development is the generation of mathematically interpretable representations to predict disease comorbidity or drug response and to suggest drug repositioning, altogether supporting clinical decisions.
... For instance, identification of deregulated mechanisms and key players in human diseases have a direct clinical application (2,3). Moreover, correlating the status of those deregulated mechanisms with patient survival helps for patient stratification according to their networkbased signatures (4). Due to the complexity of mechanisms simultaneously involved in diseases, targeting combinations of molecular players is now the trend in treatment of complex diseases. ...
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.
... In the light of the scope of this article, we believe that the study by Vitali and collaborators is an excellent example on how to integrate gene expression data to network analysis to repurpose drugs for specific subtypes of a given condition. In line with the emerging paradigm of using multi-target therapies to improve efficacy in the treatment of complex disorders and reduce drug resistance rates [74,75], this type of networkbased approximations are being increasingly applied in oncology to select drug combinations that may cope with robustness conferred by abundant regulatory loops and redundant pathways in cancer [76]. Whereas initial computational tools to detect effective drug combinations such as Combinatorial Drug Assembler [77] or DrugPairSeeker [78] were simply based on calculating connectivity scores of drug pairs that maximized the reversal of disease-associated gene signatures (see next section for further details), more recent tools such as DrugComboRanker [79] or SynGeNet [80] combine transcriptomics with network-mining algorithms. ...
http://www.tandfonline.com/eprint/n2ctySRjNmJCaEm3VY7h/full
(free download)
Introduction: Drug repositioning implies finding new medical uses for existing drugs. It represents a cost-efficient approach, since the new indications are built on the basis of available information on pharmacokinetics, safety and manufacturing. Whereas most of the pioneering drug repurposing stories arose from serendipitous observations and clever exploitation of side effects, the drug discovery community has lately addressed repurposing initiatives in a more systematic manner. Today, in the middle of the omics era, we have the tools to explore drug repurposing opportunities in a tailored, personalized manner.
Areas covered: After a brief discussion on modern approaches to drug repurposing, the author connects the philosophies of drug repurposing and personalized medicine through the well-known and extended practice of off-label prescription. The author also discusses which, among current systematic repurposing approaches, are more appropriate to be integrated with the field of precision medicine.
Expert commentary: Personalized drug repurposing is not a new concept at all: for years, it has been known as off-label prescription, a practice widely accepted especially in some branches of medicine. Whereas in the past such approach was in many cases supported by empiric knowledge, today omics technologies allow us to face novel personalized drug repurposing options in a systematic manner.
... provide information in a more systematic manner at a higher level than single molecules. In recent years, network-based approaches [105][106][107] have been developed to study the mechanisms underlying various cancers and to predict therapy responses [108]. The construction of biomolecular networks together with analysis using signaling pathways databases have been used to uncover network mechanisms of drug resistance and to suggest more complex treatments schemes. ...
Diverse forms of resistance to anticancer drugs can lead to the failure of chemotherapy. Drug resistance is one of the most intractable issues for successfully treating cancer in current clinical practice. Effective clinical approaches that could counter drug resistance by restoring the sensitivity of tumors to the targeted agents are urgently needed. As numerous experimental results on resistance mechanisms have been obtained and a mass of high-throughput data has been accumulated, mathematical modeling and computational predictions using systematic and quantitative approaches have become increasingly important, as they can potentially provide deeper insights into resistance mechanisms, generate novel hypotheses or suggest promising treatment strategies for future testing. In this review, we first briefly summarize the current progress of experimentally revealed resistance mechanisms of targeted therapy, including genetic mechanisms, epigenetic mechanisms, posttranslational mechanisms, cellular mechanisms, microenvironmental mechanisms and pharmacokinetic mechanisms. Subsequently, we list several currently available databases and Web-based tools related to drug sensitivity and resistance. Then, we focus primarily on introducing some state-of-the-art computational methods used in drug resistance studies, including mechanism-based mathematical modeling approaches (e.g. molecular dynamics simulation, kinetic model of molecular networks, ordinary differential equation model of cellular dynamics, stochastic model, partial differential equation model, agent-based model, pharmacokinetic-pharmacodynamic model, etc.) and data-driven prediction methods (e.g. omics data-based conventional screening approach for node biomarkers, static network approach for edge biomarkers and module biomarkers, dynamic network approach for dynamic network biomarkers and dynamic module network biomarkers, etc.). Finally, we discuss several further questions and future directions for the use of computational methods for studying drug resistance, including inferring drug-induced signaling networks, multiscale modeling, drug combinations and precision medicine.
... However, multiple challenges, including organ-specific transcription factor (TF) identification, tracking sequencespecific TF-binding sites and understanding of transcriptional biocircuits in health will require tremendous long-term basic research efforts. Innovative drugs disrupting deregulated nonlinear transcriptional biocircuits [105,106] will overcome one of the greatest challenges faced by biomedical research in the fields of driver structural and functional genome and transcriptome changes, as well as the comprehensive set of dynamic regulatory networks. ...
The unmet clinical needs of high relapse and cancer-related death rates are reflected by the poor understanding of the genome-wide mutational landscape and molecular mechanisms orchestrating therapeutic resistance. Emerging potential solutions to this challenge include the exploration of cancer genome dynamic evolution in time and space. Breakthrough next-generation sequencing (NGS) applications including multiregional NGS for intratumor heterogeneity identification, repeated cell-free DNA/circulating tumor DNA-NGS for detecting circulating genomic subclones and their comparison to reveal intrapatient heterogeneity (IPH) could identify the dynamic emergence of resistant subclones in the neoadjuvant, adjuvant and metastatic setting. Based on genome–phenotype mapping[SB1] , and potential promising findings, rigorous evaluation of IPH spatiotemporal evolution and early drug development concepts in innovative clinical trials could dramatically speed up the translational process to achieve clinical precision oncology.
... Data visualization is a possible solution to obtain an integrated overview of the data and understanding their characteristic patterns. Making biological sense out of the molecular data requires visualizing them in the context of cell signalling processes (5,6). A lot of information about molecular mechanisms is available in the scientific literature, and is also integrated into signalling pathway databases. ...
Human diseases such as cancer are routinely characterized by high-throughput molecular technologies, and multi-level omics data are accumulated in public databases at increasing rate. Retrieval and visualization of these data in the context of molecular network maps can provide insights into the pattern of regulation of molecular functions reflected by an omics profile. In order to make this task easy, we developed NaviCom, a Python package and web platform for visualization of multi-level omics data on top of biological network maps. NaviCom is bridging the gap between cBioPortal, the most used resource of large-scale cancer omics data and NaviCell, a data visualization web service that contains several molecular network map collections. NaviCom proposes several standardized modes of data display on top of molecular network maps, allowing addressing specific biological questions. We illustrate how users can easily create interactive network-based cancer molecular portraits via NaviCom web interface using the maps of Atlas of Cancer Signalling Network (ACSN) and other maps. Analysis of these molecular portraits can help in formulating a scientific hypothesis on the molecular mechanisms deregulated in the studied disease.
Database URL: NaviCom is available at https://navicom.curie.fr
... However, although many methods perform their analysis at the molecular level, several approaches work at a larger scale by investigating how the activation of an entire biological pathway is affected by the addition of drugs. Indeed, signaling pathways form a network with many crosstalks that are responsible for drug adverse effects, cancer resistance [58], or common activation of pathways under a given perturbation [59]. Pathway-based approaches for drug repurposing provide as an output a prioritized list of drug-induced pathways that can be assembled as a database for further analysis. ...
Here, we provide a comprehensive overview of the current status of in silico repurposing methods by establishing links between current technological trends, data availability and characteristics of the algorithms used in these methods. Using the case of the computational repurposing of fasudil as an alternative autophagy enhancer, we suggest a generic modular organization of a repurposing workflow. We also review 3D structure-based, similarity-based, inference-based and machine learning (ML)-based methods. We summarize the advantages and disadvantages of these methods to emphasize three current technical challenges. We finish by discussing current directions of research, including possibilities offered by new methods, such as deep learning.
Rainfall is one of the major climatic aspects of India. It has both pros and
cons. Rainfall prediction is a regression problem, and if we predict the amount of
rainfall in advance, we can assess the impact of it and decrease the risk of loss. One
of the major problems today to predict rainfall using machine learning is accuracy.
To increase that accuracy, we can first use data preprocessing techniques and select
the best attributes and then use all well-known ML models and select the accurate
model from the existing one. We propose the process to select model for predicting
rainfall using machine learning techniques in India. Here, we will perform the visual
examination of distinct data variables to uncover all the patterns using data exploration techniques. To analyze rainfall trends, scatter plots, histograms, bar charts, box
plots, and heatmaps are used. Multiple linear regression, support vector regression,
random forest regression, lasso regression, and ridge regression are five regression
models that can be used to model data. Finally, the selected model predicts rainfall
with highest accuracy. Different machine learning regression models for prediction
were developed, and after comparative analysis, based on performance evaluation
metrics, a best model for predicting rainfall for a subdivision is implemented. Also,
the rainfall trends in India can be studied using exploratory data analysis and data
visualization.
Predicting medication (drug) adverse reactions is a crucial aspect of drug development. Using simulation techniques to customize medication response predictions to identify the optimal medication holds enormous potential for improving a patient’s probability of successful treatment. Unfortunately, the statistical effort of forecasting medication reaction is extremely difficult, partly due to dataset limitations and also due to algorithms flaws. Medications are organic molecules that are ingested by humans as well as cause a response in the body through engaging to target proteins. These medications can reduce positive or negative effects inside the organisms. The undesirable modifications in medications cause adverse reactions inside the human organism generally referred to as medication side effects. Such adverse effects might vary from mild occurrences like a migraine to more significant ones involving heart failure, malignancy, or perhaps even mortality. The medications are put through a series of tests in the laboratories to see if they have any negative health effects. Such tests, unfortunately, are both expensive and time-consuming. Numerical techniques can be used as a replacement for experimental research. Numerous computational strategies for detecting drug adverse reactions have recently been published. In this paper, several existing techniques of drug response prediction are surveyed. The Pearson correlation coefficient value of existing techniques is compared graphically and find that SRMF technique had attained the best results. The existing methods are examined with the help of various databases. The most essential databases for drug adverse reactions prediction are BIO-SNAP, SIDER, DART, etc. The sets of data associated with drug adverse reactions, as well as the parameters used to evaluate drug adverse events prediction techniques, have been described. For the evaluation of drug adverse reactions prediction techniques PCC, precision, accuracy, etc., parameters are used. For the prediction of drug side effects, several methods are used such as docking-based methods, network-based methods, machine learning-based methods, and various miscellaneous methods.KeywordsDrug response predictionDocking basedMachine learning-based prediction methods
Human diseases such as cancer are routinely characterized by high-throughput molecular technologies, and multi-level omics data are accumulated in public databases at increasing rate. Retrieval and visualization of these data in the context of molecular network maps can provide insights into the pattern of molecular functions encompassed by an omics profile. In order to make this task easy, we developed NaviCom, a Python package and web platform for visualization of multi-level omics data on top of biological network maps. NaviCom is bridging the gap between cBioPortal, the most used resource of large-scale cancer omics data and NaviCell, a data visualization web service that contains several molecular network map collections. NaviCom proposes several standardized modes of data display on top of molecular network maps, allowing to address specific biological questions. We illustrate how users can easily create interactive network-based cancer molecular portraits via NaviCom web interface using the maps of Atlas of Cancer Signaling Network (ACSN) and other maps. Analysis of these molecular portraits can help in formulating a scientific hypothesis on the molecular mechanisms deregulated in the studied disease.
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.
Anti-cancer drug combination is an effective solution to improve treatment efficacy and overcome resistance. Here we propose a network-based method (DComboNet) to prioritize the candidate drug combinations. The level one model is to predict generalized anti-cancer drug combination effectiveness and level two model is to predict personalized drug combinations. By integrating drugs, genes, pathways and their associations, DComboNet achieves better performance than previous methods, with high AUC value of around 0.8. The level two model performs better than level one model by introducing cancer sample specific transcriptome data into network construction. DComboNet is further applied on finding combinable drugs for sorafenib in hepatocellular cancer, and the results are verified with literatures and cell line experiments. More importantly, three potential mechanism modes of combinations were inferred based on network analysis. In summary, DComboNet is valuable for prioritizing drug combination and the network model may facilitate the understanding of the combination mechanisms.
Covering: 1957 to 2017 Algae constitute a heterogeneous group of eukaryotic photosynthetic organisms, mainly found in the marine environment. Algae produce numerous metabolites that help them cope with the harsh conditions of the marine environment. Because of their structural diversity and uniqueness, these molecules have recently gained a lot of interest for the identification of medicinally useful agents, including those with potential anticancer activities. In the current review, which is not a catalogue-based one, we first highlight the major biological events that lead to various types of cancer, including metastatic ones, to chemoresistance, thus to any types of current anticancer treatment relating to the use of chemotherapeutics. We then review algal metabolites for which scientific literature reports anticancer activity. Lastly, we focus on algal metabolites with promising anticancer activity based on their ability to target biological characteristics of cancer cells responsible for poor treatment outcomes. Thus, we highlight compounds that have, among others, one or more of the following characteristics: selectivity in reducing the proliferation of cancer cells over normal ones, potential for killing cancer cells through non-apoptotic signaling pathways, ability to circumvent MDR-related efflux pumps, and activity in vivo in relevant pre-clinical models.
In this review we address to what extent computational techniques can augment our ability to predict toxicity. The first section provides a brief history of empirical observations on toxicity dating back to the dawn of Sumerian civilization. Interestingly, the concept of dose emerged very early on, leading up to the modern emphasis on kinetic properties, which in turn encodes the insight that toxicity is not solely a property of a compound but instead depends on the interaction with the host organism. The next logical step is the current conception of evaluating drugs from a personalized medicine point of view. We review recent work on integrating what could be referred to as classical pharmacokinetic analysis with emerging systems biology approaches incorporating multiple omics data. These systems approaches employ advanced statistical analytical data processing complemented with machine learning techniques and use both pharmacokinetic and omics data. We find that such integrated approaches not only provide improved predictions of toxicity but also enable mechanistic interpretations of the molecular mechanisms underpinning toxicity and drug resistance. We conclude the chapter by discussing some of the main challenges, such as how to balance the inherent tension between the predicitive capacity of models, which in practice amounts to constraining the number of features in the models versus allowing for rich mechanistic interpretability, i.e., equipping models with numerous molecular features. This challenge also requires patient-specific predictions on toxicity, which in turn requires proper stratification of patients as regards how they respond, with or without adverse toxic effects. In summary, the transformation of the ancient concept of dose is currently successfully operationalized using rich integrative data encoded in patient-specific models.
Lung cancer has moved from the shadows of oncology to the forefront of drug development as it is now the cancer type for which the highest number of medicines are in development. Cancer genome testing is widely applied, and multiplexed analyses are performed because of the multitude of genetic aberrations which have targetable potential. Repeated tissue sampling is necessary to support treatment decisions upon resistance development, and liquid biopsies may prove valuable. All patients with adenocarcinoma or adenosquamous histology, as well as never-smokers of all histologies, should be tested for EGFR- and ALK-aberrations. KRAS-positivity may indicate the absence of other driver mutations and could be part of the initial diagnostic work-up in settings where multiplexed analyses are not yet available. Clinical trials with therapies targeting rare aberrations are abundant, and as many patients as possible should be offered therapy based on broad-spectrum cancer genome diagnostics.
The unprecedented potential of standard and new next-generation sequencing applications and methods to explore cancer genome evolution and tumor heterogeneity as well as transcription networks in time and space shapes the development of next-generation therapeutics. However, biomedical and pharmaceutical research for overcoming heterogeneity-based therapeutic resistance is at an important crossroads. Focus on linear transcription-based drug development targeting dynamics of simple intrapatient structured genome diversity represents a realistic medium-term goal. By contrast, the discovery of nonlinear transcription drugs for targeting structural and functional genome and transcriptome heterogeneity represents a long-term rational strategy. This review compares effectiveness, challenges and expectations between linear and nonlinear drugs targeting simple intrapatient variation and aberrant transcriptional biocircuits, respectively.
The recent advances in pharmacogenomics have made personalized medicine no longer a pipedream but a precise and powerful way to tailor individualized cancer treatment strategies. Cancer is a devastating disease, and contemporary chemotherapeutic strategies now integrate several agents in the treatment of some types of cancer, with the intent to block more than one target simultaneously. This constitutes the premise of synthetic lethality, an attractive therapeutic strategy already demonstrating clinical success in patients with breast and ovarian cancers. Synthetic lethal combinations offer the potential to also target the hitherto “undruggable” mutations that have challenged the cancer field for decades. However, synthetic lethality in clinical cancer therapy is very much still in its infancy, and selecting the most appropriate combinations—or synthetic lethal pairs—is not always an intuitive process. Here, we review some of the recent progress in identifying synthetic lethal combinations and their potential for therapy and highlight some of the tools through which synthetic lethal pairs are identified.
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.
The observation that disease associated proteins often interact with each other has fueled the development of network-based approaches to elucidate the molecular mechanisms of human disease. Such approaches build on the assumption that protein interaction networks can be viewed as maps in which diseases can be identified with localized perturbation within a certain neighborhood. The identification of these neighborhoods, or disease modules, is therefore a prerequisite of a detailed investigation of a particular pathophenotype. While numerous heuristic methods exist that successfully pinpoint disease associated modules, the basic underlying connectivity patterns remain largely unexplored. In this work we aim to fill this gap by analyzing the network properties of a comprehensive corpus of 70 complex diseases. We find that disease associated proteins do not reside within locally dense communities and instead identify connectivity significance as the most predictive quantity. This quantity inspires the design of a novel Disease Module Detection (DIAMOnD) algorithm to identify the full disease module around a set of known disease proteins. We study the performance of the algorithm using well-controlled synthetic data and systematically validate the identified neighborhoods for a large corpus of diseases.
The study of biological pathways is key to a large number of systems analyses. However, many relevant tools consider a limited number of pathway sources, missing out on many genes and gene-to-gene connections. Simply pooling several pathways sources would result in redundancy and the lack of systematic pathway interrelations. To address this, we exercised a combination of hierarchical clustering and nearest neighbor graph representation, with judiciously selected cutoff values, thereby consolidating 3215 human pathways from 12 sources into a set of 1073 SuperPaths. Our unification algorithm finds a balance between reducing redundancy and optimizing the level of pathway-related informativeness for individual genes. We show a substantial enhancement of the SuperPaths' capacity to infer gene-to-gene relationships when compared with individual pathway sources, separately or taken together. Further, we demonstrate that the chosen 12 sources entail nearly exhaustive gene coverage. The computed SuperPaths are presented in a new online database, PathCards, showing each SuperPath, its constituent network of pathways, and its contained genes. This provides researchers with a rich, searchable systems analysis resource.Database URL: http://pathcards.genecards.org/
© The Author(s) 2015. Published by Oxford University Press.
Complex diseases like cancer are regulated by large, interconnected networks with many pathways affecting cell proliferation, invasion, and drug resistance. However, current cancer therapy predominantly relies on the reductionist approach of one gene-one disease. Combinations of drugs may overcome drug resistance by limiting mutations and induction of escape pathways, but given the enormous number of possible drug combinations, strategies to reduce the search space and prioritize experiments are needed. In this review, we focus on the use of computational modeling, bioinformatics and high-throughput experimental methods for discovery of drug combinations. We highlight cutting-edge systems approaches, including large-scale modeling of cell signaling networks, network motif analysis, statistical association-based models, identifying correlations in gene signatures, functional genomics, and high-throughput combination screens. We also present a list of publicly available data and resources to aid in discovery of drug combinations. Integration of these systems approaches will enable faster discovery and translation of clinically relevant drug combinations.
Graphical abstract
Spectrum of Systems Biology Approaches for Drug Combinations.
Cancers exhibit extensive mutational heterogeneity, and the resulting long-tail phenomenon complicates the discovery of genes and pathways that are significantly mutated in cancer. We perform a pan-cancer analysis of mutated networks in 3,281 samples from 12 cancer types from The Cancer Genome Atlas (TCGA) using HotNet2, a new algorithm to find mutated subnetworks that overcomes the limitations of existing single-gene, pathway and network approaches. We identify 16 significantly mutated subnetworks that comprise well-known cancer signaling pathways as well as subnetworks with less characterized roles in cancer, including cohesin, condensin and others. Many of these subnetworks exhibit co-occurring mutations across samples. These subnetworks contain dozens of genes with rare somatic mutations across multiple cancers; many of these genes have additional evidence supporting a role in cancer. By illuminating these rare combinations of mutations, pan-cancer network analyses provide a roadmap to investigate new diagnostic and therapeutic opportunities across cancer types.
High-throughput experiments are routinely performed in modern biological studies. However, extracting meaningful results from massive experimental data sets is a challenging task for biologists. Projecting data onto pathway and network contexts is a powerful way to unravel patterns embedded in seemingly scattered large data sets and assist knowledge discovery related to cancer and other complex diseases. We have developed a Cytoscape app called "ReactomeFIViz", which utilizes a highly reliable gene functional interaction network combined with human curated pathways derived from Reactome and other pathway databases. This app provides a suite of features to assist biologists in performing pathway- and network-based data analysis in a biologically intuitive and user-friendly way. Biologists can use this app to uncover network and pathway patterns related to their studies, search for gene signatures from gene expression data sets, reveal pathways significantly enriched by genes in a list, and integrate multiple genomic data types into a pathway context using probabilistic graphical models. We believe our app will give researchers substantial power to analyze intrinsically noisy high-throughput experimental data to find biologically relevant information.
The majority of melanomas have been shown to harbor somatic mutations in the RAS-RAF-MEK-MAPK and PI3K-AKT pathways, which play a major role in regulation of proliferation and survival. The prevalence of these mutations makes these kinase signal transduction pathways an attractive target for cancer therapy. However, tumors have generally shown adaptive resistance to treatment. This adaptation is achieved in melanoma through its ability to undergo neovascularization, migration and rearrangement of signaling pathways. To understand the dynamic, nonlinear behavior of signaling pathways in cancer, several computational modeling approaches have been suggested. Most of those models require that the pathway topology remains constant over the entire observation period. However, changes in topology might underlie adaptive behavior to drug treatment. To study signaling rearrangements, here we present a new approach based on Fuzzy Logic (FL) that predicts changes in network architecture over time. This adaptive modeling approach was used to investigate pathway dynamics in a newly acquired experimental dataset describing total and phosphorylated protein signaling over four days in A375 melanoma cell line exposed to different kinase inhibitors. First, a generalized strategy was established to implement a parameter-reduced FL model encoding non-linear activity of a signaling network in response to perturbation. Next, a literature-based topology was generated and parameters of the FL model were derived from the full experimental dataset. Subsequently, the temporal evolution of model performance was evaluated by leaving time-defined data points out of training. Emerging discrepancies between model predictions and experimental data at specific time points allowed the characterization of potential network rearrangement. We demonstrate that this adaptive FL modeling approach helps to enhance our mechanistic understanding of the molecular plasticity of melanoma.
Synthetic lethality occurs when the inhibition of two genes is lethal while the inhibition of each single gene is not. It can be harnessed to selectively treat cancer by identifying inactive genes in a given cancer and targeting their synthetic lethal (SL) partners. We present a data-driven computational pipeline for the genome-wide identification of SL interactions in cancer by analyzing large volumes of cancer genomic data. First, we show that the approach successfully captures known SL partners of tumor suppressors and oncogenes. We then validate SL predictions obtained for the tumor suppressor VHL. Next, we construct a genome-wide network of SL interactions in cancer and demonstrate its value in predicting gene essentiality and clinical prognosis. Finally, we identify synthetic lethality arising from gene overactivation and use it to predict drug efficacy. These results form a computational basis for exploiting synthetic lethality to uncover cancer-specific susceptibilities.
The human kinome is gaining importance through its promising cancer therapeutic targets, yet no general model to address the kinase inhibitor resistance has emerged. Here, we constructed a systems biology-based framework to catalogue the human kinome, including 538 kinase genes, in the broader context of the human interactome. Specifically, we constructed three networks: a kinase-substrate interaction network containing 7,346 pairs connecting 379 kinases to 36,576 phosphorylation sites in 1,961 substrates, a protein-protein interaction network (PPIN) containing 92,699 pairs, and an atomic resolution PPIN containing 4,278 pairs. We identified the conserved regulatory phosphorylation motifs (e.g., Ser/Thr-Pro) using a sequence logo analysis. We found the typical anticancer target selection strategy that uses network hubs as drug targets, might lead to a high adverse drug reaction risk. Furthermore, we found the distinct network centrality of kinases creates a high anticancer drug resistance risk by feedback or crosstalk mechanisms within cellular networks. This notion is supported by the systematic network and pathway analyses that anticancer drug resistance genes are significantly enriched as hubs and heavily participate in multiple signaling pathways. Collectively, this comprehensive human kinome interactome map sheds light on anticancer drug resistance mechanisms and provides an innovative resource for rational kinase inhibitor design.
The dynamic impact approach (DIA) represents an alternative to overrepresentation analysis (ORA) for functional analysis of time-course experiments or those involving multiple treatments. The DIA can be used to estimate the biological impact of the differentially expressed genes (DEGs) associated with particular biological functions, for example, as represented by the Kyoto encyclopedia of genes and genomes (KEGG) annotations. However, the DIA does not take into account the correlated dependence structure of the KEGG pathway hierarchy. We have developed herein a path analysis model (KEGG-PATH) to subdivide the total effect of each KEGG pathway into the direct effect and indirect effect by taking into account not only each KEGG pathway itself, but also the correlation with its related pathways. In addition, this work also attempts to preliminarily estimate the impact direction of each KEGG pathway by a gradient analysis method from principal component analysis (PCA). As a result, the advantage of the KEGG-PATH model is demonstrated through the functional analysis of the bovine mammary transcriptome during lactation.
Molecular profiling of tumors promises to advance the clinical management of cancer, but the benefits of integrating molecular data with traditional clinical variables have not been systematically studied. Here we retrospectively predict patient survival using diverse molecular data (somatic copy-number alteration, DNA methylation and mRNA, microRNA and protein expression) from 953 samples of four cancer types from The Cancer Genome Atlas project. We find that incorporating molecular data with clinical variables yields statistically significantly improved predictions (FDR < 0.05) for three cancers but those quantitative gains were limited (2.2-23.9%). Additional analyses revealed little predictive power across tumor types except for one case. In clinically relevant genes, we identified 10,281 somatic alterations across 12 cancer types in 2,928 of 3,277 patients (89.4%), many of which would not be revealed in single-tumor analyses. Our study provides a starting point and resources, including an open-access model evaluation platform, for building reliable prognostic and therapeutic strategies that incorporate molecular data.
Motivation:
Currently there are no curative anticancer drugs, and drug resistance is often acquired after drug treatment. One of the reasons is that cancers are complex diseases, regulated by multiple signaling pathways and cross talks among the pathways. It is expected that drug combinations can reduce drug resistance and improve patients' outcomes. In clinical practice, the ideal and feasible drug combinations are combinations of existing Food and Drug Administration-approved drugs or bioactive compounds that are already used on patients or have entered clinical trials and passed safety tests. These drug combinations could directly be used on patients with less concern of toxic effects. However, there is so far no effective computational approach to search effective drug combinations from the enormous number of possibilities.
Results:
In this study, we propose a novel systematic computational tool DRUGCOMBORANKER: to prioritize synergistic drug combinations and uncover their mechanisms of action. We first build a drug functional network based on their genomic profiles, and partition the network into numerous drug network communities by using a Bayesian non-negative matrix factorization approach. As drugs within overlapping community share common mechanisms of action, we next uncover potential targets of drugs by applying a recommendation system on drug communities. We meanwhile build disease-specific signaling networks based on patients' genomic profiles and interactome data. We then identify drug combinations by searching drugs whose targets are enriched in the complementary signaling modules of the disease signaling network. The novel method was evaluated on lung adenocarcinoma and endocrine receptor positive breast cancer, and compared with other drug combination approaches. These case studies discovered a set of effective drug combinations top ranked in our prediction list, and mapped the drug targets on the disease signaling network to highlight the mechanisms of action of the drug combinations.
Availability and implementation:
The program is available on request.
Synthetic lethality (SL) is a novel strategy for anticancer therapies, whereby mutations of two genes will kill a cell but mutation of a single gene will not. Therefore, a cancer-specific mutation combined with a drug-induced mutation, if they have SL interactions, will selectively kill cancer cells. While numerous SL interactions have been identified in yeast, only a few have been
known in human. There is a pressing need to systematically discover and understand SL interactions specific to human cancer. In this paper, we present Syn-Lethality, the first integrative knowledge base of SL that is dedicated to human cancer. It integrates
experimentally discovered and verified human SL gene pairs into a network, associated with annotations of gene function, pathway, and molecular mechanisms. It also includes yeast SL genes from high-throughput screenings which are mapped to orthologous human genes. Such an integrative knowledge base, organized as a relational database with user interface for searching and network visualization, will greatly expedite the discovery of novel anticancer drug targets based on synthetic lethality interactions. The database can be downloaded as a stand-alone Java application.
Somatic homozygous deletions of chromosomal regions in cancer, while not necessarily oncogenic, may lead to therapeutic vulnerabilities specific to cancer cells compared to normal cells. A recently reported example is the loss of one of two isoenzymes in glioblastoma cancer cells such that use of a specific inhibitor selectively inhibited growth of the cancer cells, which had become fully dependent on the second isoenzyme. We have now made use of the unprecedented conjunction of large scale cancer genomics profiling of tumor samples in The Cancer Genome Atlas, and of tumor-derived cell lines in the Cancer Cell Line Encyclopedia, as well as the availability of integrated pathway information systems, such as Pathway Commons, to systematically search for a comprehensive set of such epistatic vulnerabilities.
Based on homozygous deletions affecting metabolic enzymes in 15 cancer studies and 972 cancer cell lines, we identified 4104 candidate metabolic vulnerabilities present in 1019 tumor samples and 482 cell lines. Up to 44% of these vulnerabilities can be targeted with at least one FDA-approved drug. We suggest focused experiments to test these vulnerabilities and clinical trials based on personalized genomic profiles of those that pass pre-clinical filters. We conclude that genomic profiling will in the future provide a promising basis for network pharmacology of epistatic vulnerabilities as a promising therapeutic strategy.
Supplementary web site is available at http://cbio.mskcc.org/cancergenomics/statius.
statius@cbio.mskcc.org.
Gene set enrichment analysis (GSEA) is an important approach to the analysis of coordinate expression changes at a pathway level. Although many statistical and computational methods have been proposed for GSEA, the issue of a concordant integrative GSEA of multiple expression data sets has not been well addressed. Among different related data sets collected for the same or similar study purposes, it is important to identify pathways or gene sets with concordant enrichment.
We categorize the underlying true states of differential expression into three representative categories: no change, positive change and negative change. Due to data noise, what we observe from experiments may not indicate the underlying truth. Although these categories are not observed in practice, they can be considered in a mixture model framework. Then, we define the mathematical concept of concordant gene set enrichment and calculate its related probability based on a three-component multivariate normal mixture model. The related false discovery rate can be calculated and used to rank different gene sets.
We used three published lung cancer microarray gene expression data sets to illustrate our proposed method. One analysis based on the first two data sets was conducted to compare our result with a previous published result based on a GSEA conducted separately for each individual data set. This comparison illustrates the advantage of our proposed concordant integrative gene set enrichment analysis. Then, with a relatively new and larger pathway collection, we used our method to conduct an integrative analysis of the first two data sets and also all three data sets. Both results showed that many gene sets could be identified with low false discovery rates. A consistency between both results was also observed. A further exploration based on the KEGG cancer pathway collection showed that a majority of these pathways could be identified by our proposed method.
This study illustrates that we can improve detection power and discovery consistency through a concordant integrative analysis of multiple large-scale two-sample gene expression data sets.
Abstract Precision or personalized treatment can be defined as using the biological characteristics of a patient's disease in order to administer the most effective therapy at the optimum dose. The aim of this article is to discuss the use of prognostic and predictive markers to aid precision treatment in patients with cancer. Prognostic markers help to differentiate between indolent and life-threatening disease and thereby identify who should or should not receive adjuvant systemic therapy following surgical removal of a primary tumor. Predictive markers, on the other hand, help to identify upfront those patients who are likely to be responsive or resistant to a specific therapy. The use of prognostic and predictive markers can thus help to match each patient to the most effective and least toxic therapy and as a result avoid preventable toxicity and unnecessary costs.
Network-based drug discovery aims at harnessing the power of networks to investigate the mechanism of action of existing drugs, or new molecules, in order to identify innovative therapeutic treatments. In this review, we describe some of the most recent advances in the field of network pharmacology, starting with approaches relying on computational models of transcriptional networks, then moving to protein and signaling network models and concluding with "drug networks". These networks are derived from different sources of experimental data, or literature-based analysis, and provide a complementary view of drug mode of action. Molecular and drug networks are powerful integrated computational and experimental approaches that will likely speed up and improve the drug discovery process, once fully integrated into the academic and industrial drug discovery pipeline.
STITCH is a database of protein-chemical interactions that integrates many sources of experimental and manually curated evidence with text-mining information and interaction predictions. Available at http://stitch.embl.de, the resulting interaction network includes 390 000 chemicals and 3.6 million proteins from 1133 organisms. Compared with the previous version, the number of high-confidence protein-chemical interactions in human has increased by 45%, to 367 000. In this version, we added features for users to upload their own data to STITCH in the form of internal identifiers, chemical structures or quantitative data. For example, a user can now upload a spreadsheet with screening hits to easily check which interactions are already known. To increase the coverage of STITCH, we expanded the text mining to include full-text articles and added a prediction method based on chemical structures. We further changed our scheme for transferring interactions between species to rely on orthology rather than protein similarity. This improves the performance within protein families, where scores are now transferred only to orthologous proteins, but not to paralogous proteins. STITCH can be accessed with a web-interface, an API and downloadable files.
Motivation: Within Flux Balance Analysis, the investigation of complex subtasks, such as finding the optimalperturbation of the network or finding an optimal combination of drugs, often requires to set up abilevel optimization problem. In order to keep the linearity and convexity of these nestedoptimization problems, an ON/OFF description of the effect of the perturbation (i.e. Booleanvariable) is normally used. This restriction may not be realistic when one wants, for instance, todescribe the partial inhibition of a reaction induced by a drug.
In this paper we present a formulation of the bilevel optimization which overcomes theoversimplified ON/OFF modeling while preserving the linear nature of the problem. A case study isconsidered: the search of the best multi-drug treatment which modulates an objective reaction andhas the minimal perturbation on the whole network. The drug inhibition is described and modulatedthrough a convex combination of a fixed number of Boolean variables. The results obtained from theapplication of the algorithm to the core metabolism of E.coli highlight the possibility of finding abroader spectrum of drug combinations compared to a simple ON/OFF modeling.
The method we have presented is capable of treating partial inhibition inside a bilevel optimization,without loosing the linearity property, and with reasonable computational performances also on largemetabolic networks. The more fine-graded representation of the perturbation allows to enlarge therepertoire of synergistic combination of drugs for tasks such as selective perturbation of cellularmetabolism. This may encourage the use of the approach also for other cases in which a morerealistic modeling is required.
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.
Cancer has been increasingly recognized as a systems biology disease since many investigators have demonstrated that this malignant phenotype emerges from abnormal protein-protein, regulatory and metabolic interactions induced by simultaneous structural and regulatory changes in multiple genes and pathways. Therefore, the identification of oncogenic interactions and cancer-related signaling networks is crucial for better understanding cancer. As experimental techniques for determining such interactions and signaling networks are labor-intensive and time-consuming, the development of a computational approach capable to accomplish this task would be of great value. For this purpose, we present here a novel computational approach based on network topology and machine learning capable to predict oncogenic interactions and extract relevant cancer-related signaling subnetworks from an integrated network of human genes interactions (INHGI). This approach, called graph2sig, is twofold: first, it assigns oncogenic scores to all interactions in the INHGI and then these oncogenic scores are used as edge weights to extract oncogenic signaling subnetworks from INHGI. Regarding the prediction of oncogenic interactions, we showed that graph2sig is able to recover 89% of known oncogenic interactions with a precision of 77%. Moreover, the interactions that received high oncogenic scores are enriched in genes for which mutations have been causally implicated in cancer. We also demonstrated that graph2sig is potentially useful in extracting oncogenic signaling subnetworks: more than 80% of constructed subnetworks contain more than 50% of original interactions in their corresponding oncogenic linear pathways present in the KEGG PATHWAY database. In addition, the potential oncogenic signaling subnetworks discovered by graph2sig are supported by experimental evidence. Taken together, these results suggest that graph2sig can be a useful tool for investigators involved in cancer research interested in detecting signaling networks most prone to contribute with the emergence of malignant phenotype.
DrugBank (http://www.drugbank.ca) is a comprehensive online database containing extensive biochemical and pharmacological information about drugs, their mechanisms and their targets. Since it was first described in 2006, DrugBank has rapidly evolved, both in response to user requests and in response to changing trends in drug research and development. Previous versions of DrugBank have been widely used to facilitate drug and in silico drug target discovery. The latest update, DrugBank 4.0, has been further expanded to contain data on drug metabolism, absorption, distribution, metabolism, excretion and toxicity (ADMET) and other kinds of quantitative structure activity relationships (QSAR) information. These enhancements are intended to facilitate research in xenobiotic metabolism (both prediction and characterization), pharmacokinetics, pharmacodynamics and drug design/discovery. For this release, >1200 drug metabolites (including their structures, names, activity, abundance and other detailed data) have been added along with >1300 drug metabolism reactions (including metabolizing enzymes and reaction types) and dozens of drug metabolism pathways. Another 30 predicted or measured ADMET parameters have been added to each DrugCard, bringing the average number of quantitative ADMET values for Food and Drug Administration-approved drugs close to 40. Referential nuclear magnetic resonance and MS spectra have been added for almost 400 drugs as well as spectral and mass matching tools to facilitate compound identification. This expanded collection of drug information is complemented by a number of new or improved search tools, including one that provides a simple analyses of drug-target, -enzyme and -transporter associations to provide insight on drug-drug interactions.
The goal of pathway analysis is to identify the pathways significantly impacted in a given phenotype. Many current methods are based on algorithms that consider pathways as simple gene lists, dramatically under-utilizing the knowledge that such pathways are meant to capture. During the past few years, a plethora of methods claiming to incorporate various aspects of the pathway topology have been proposed. These topology-based methods, sometimes referred to as "third generation," have the potential to better model the phenomena described by pathways. Although there is now a large variety of approaches used for this purpose, no review is currently available to offer guidance for potential users and developers. This review covers 22 such topology-based pathway analysis methods published in the last decade. We compare these methods based on: type of pathways analyzed (e.g., signaling or metabolic), input (subset of genes, all genes, fold changes, gene p-values, etc.), mathematical models, pathway scoring approaches, output (one or more pathway scores, p-values, etc.) and implementation (web-based, standalone, etc.). We identify and discuss challenges, arising both in methodology and in pathway representation, including inconsistent terminology, different data formats, lack of meaningful benchmarks, and the lack of tissue and condition specificity.
Cancer is a complex disease resulting from alterations of multiple signaling networks. Cancer networks have been identified as scale-free networks and may contain a functionally important key player called a hub that is linked to a large number of interactors. Since a hub can serve as a biological marker in a given network, targeting the hub could be an effective strategy for enhancing the efficacy of cancer treatment. Chemotherapies and radiotherapies are generally used to treat tumors not amenable to resection, and target single or multiple molecules associated with hubs. However, these therapies may unexpectedly induce the resistance of cancer cells to drugs and radiation. Cancer cells can overcome therapy-induced damage via the activation of back-up signaling pathways and flexible modulation of affected networks. These activities are considered to be the main reasons for chemoresistance and radioresistance, and subsequent failure of cancer therapies. Much effort is required to identify the key molecules that control the modulation of signaling networks in response to drugs and radiation. Network-based therapy that affects network flexibility, including rewired network structures and hub molecules in these networks, could minimize the occurrence of side-effects and be a promising strategy for enhancing the therapeutic efficacy of cancer treatments. This review is intended to offer an overview of current research efforts including ones focused on cancer-associated complex networks, their modulation in response to cancer therapy, and further strategies targeting networks that may improve cancer treatment efficacy.
Glioblastoma is the most common, malignant adult primary tumor with dismal patient survival, yet the molecular determinants of patient survival are poorly characterized. Global methylation profile of GBM samples (our cohort; n=44) using high-resolution methylation microarrays was carried out. Cox regression analysis identified a 9-gene methylation signature that predicted survival in glioblastoma patients. A risk-score derived from methylation signature, predicted survival in univariate analysis in our and TCGA cohort. Multivariate analysis identified methylation risk-score as an independent survival predictor in TCGA cohort. Methylation risk-score stratified the patients into low-risk and high-risk groups with significant survival difference. Network analysis revealed an activated NFkB pathway association with high-risk group. NFkB inhibition reversed glioma chemoresistance and RNA interference studies identified IL6 and ICAM1 as key NFkB targets in imparting chemoresistance. Promoter hypermethylation of NPTX2, a risky methylated gene, was confirmed by bisulfite sequencing in GBMs. GBMs and glioma cell lines had low levels of NPTX2 transcripts which could be reversed upon methylation inhibitor treatment. NPTX2 overexpression induced apoptosis, inhibited proliferation and anchorage-independent growth and rendered glioma cells chemosensitive. Further, NPTX2 repressed NFkB activity by inhibiting AKT through a p53-PTEN dependent pathway, thus explaining the hypermethylation and downregulation of NPTX2 in NFkB activated high-risk GBMs. Taken together, a 9-gene methylation signature was identified as an independent GBM prognosticator and could be used for GBM risk stratification. Pro-survival NFkB pathway activation characterized high-risk patients with poor prognosis indicating it to be a therapeutic target.
All cancers are caused by somatic mutations; however, understanding of the biological processes generating these mutations is limited. The catalogue of somatic mutations from a cancer genome bears the signatures of the mutational processes that have been operative. Here we analysed 4,938,362 mutations from 7,042 cancers and extracted more than 20 distinct mutational signatures. Some are present in many cancer types, notably a signature attributed to the APOBEC family of cytidine deaminases, whereas others are confined to a single cancer class. Certain signatures are associated with age of the patient at cancer diagnosis, known mutagenic exposures or defects in DNA maintenance, but many are of cryptic origin. In addition to these genome-wide mutational signatures, hypermutation localized to small genomic regions, ‘kataegis’, is found in many cancer types. The results reveal the diversity of mutational processes underlying the development of cancer, with potential implications for understanding of cancer aetiology, prevention and therapy.
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.
Analysis of gene expression patterns in normal tissues and their perturbations in tumours can help to identify the functional roles of oncogenes or tumour suppressors and identify potential new therapeutic targets. Here, gene expression correlation networks were derived from 92 normal human lung samples and patient-matched adenocarcinomas. The networks from normal lung show that NKX2-1 is linked to the alveolar type 2 lineage, and identify PEBP4 as a novel marker expressed in alveolar type 2 cells. Differential correlation analysis shows that the NKX2-1 network in tumours includes pathways associated with glutamate metabolism, and identifies Vaccinia-related kinase (VRK1) as a potential drug target in a tumour-specific mitotic network. We show that VRK1 inhibition cooperates with inhibition of poly (ADP-ribose) polymerase signalling to inhibit growth of lung tumour cells. Targeting of genes that are recruited into tumour mitotic networks may provide a wider therapeutic window than that seen by inhibition of known mitotic genes.
Polypharmacology has emerged as novel means in drug discovery for improving treatment response in clinical use. However, to really capitalize on the polypharmacological effects of drugs, there is a critical need to better model and understand how the complex interactions between drugs and their cellular targets contribute to drug efficacy and possible side effects. Network graphs provide a convenient modeling framework for dealing with the fact that most drugs act on cellular systems through targeting multiple proteins both through on-target and off-target binding. Network pharmacology models aim at addressing questions such as how and where in the disease network should one target to inhibit disease phenotypes, such as cancer growth, ideally leading to therapies that are less vulnerable to drug resistance and side effects by means of attacking the disease network at the systems level through synergistic and synthetic lethal interactions. Since the exponentially increasing number of potential drug target combinations makes pure experimental approach quickly unfeasible, this review depicts a number of computational models and algorithms that can effectively reduce the search space for determining the most promising combinations for experimental evaluation. Such computational-experimental strategies are geared toward realizing the full potential of multi-target treatments in different disease phenotypes. Our specific focus in on system-level network approaches to polypharmacology designs in anticancer drug discovery, where we give representative examples of how network-centric modeling may offer systematic strategies toward better understanding and even predicting the phenotypic responses to multi-target therapies.
Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only gives a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central node/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.
The genome of a cancer cell carries somatic mutations that are the cumulative consequences of the DNA damage and repair processes operative during the cellular lineage between the fertilized egg and the cancer cell. Remarkably, these mutational processes are poorly characterized. Global sequencing initiatives are yielding catalogs of somatic mutations from thousands of cancers, thus providing the unique opportunity to decipher the signatures of mutational processes operative in human cancer. However, until now there have been no theoretical models describing the signatures of mutational processes operative in cancer genomes and no systematic computational approaches are available to decipher these mutational signatures. Here, by modeling mutational processes as a blind source separation problem, we introduce a computational framework that effectively addresses these questions. Our approach provides a basis for characterizing mutational signatures from cancer-derived somatic mutational catalogs, paving the way to insights into the pathogenetic mechanism underlying all cancers.
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.
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.
The elucidation of breast cancer subgroups and their molecular drivers requires integrated views of the genome and transcriptome from representative numbers of patients. We present an integrated analysis of copy number and gene expression in a discovery and validation set of 997 and 995 primary breast tumours, respectively, with long-term clinical follow-up. Inherited variants (copy number variants and single nucleotide polymorphisms) and acquired somatic copy number aberrations (CNAs) were associated with expression in ~40% of genes, with the landscape dominated by cis- and trans-acting CNAs. By delineating expression outlier genes driven in cis by CNAs, we identified putative cancer genes, including deletions in PPP2R2A, MTAP and MAP2K4. Unsupervised analysis of paired DNA--RNA profiles revealed novel subgroups with distinct clinical outcomes, which reproduced in the validation cohort. These include a high-risk, oestrogen-receptor-positive 11q13/14 cis-acting subgroup and a favourable prognosis subgroup devoid of CNAs. Trans-acting aberration hotspots were found to modulate subgroup-specific gene networks, including a TCR deletion-mediated adaptive immune response in the ‘CNA-devoid’ subgroup and a basal-specific chromosome 5 deletion-associated mitotic network. Our results provide a novel molecular stratification of the breast cancer population, derived from the impact of somatic CNAs on the transcriptome.
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.
Synthetic lethality refers to the requirement that two defects be present for cell death to occur. When an agent damages DNA, its effects on the cell can be amplified if the efforts of the cell to repair the damage are inhibited. Clinical application of this idea is just beginning.
Cancer heterogeneity, long recognized as an important clinical determinant of patient outcomes, was poorly understood at a molecular level. Genomic studies have significantly improved our understanding of heterogeneity, and have pointed to ways in which heterogeneity might be understood and defeated for therapeutic effect. Recent studies have evaluated intratumoral heterogeneity within the primary tumor, as well as heterogeneity observed between primary and metastasis. The existence of clonal heterogeneity in the primary and metastasis also affects response to therapy, since the Darwinian pressures of systemic therapy result in clonal selection for initially rare variants. Novel technologies (such as measurements of circulating tumor cells and circulating tumor DNA) may allow physicians to monitor the emergence of clonal subtypes and intervene at an early point to improve patient prognosis.
Revealing functional reorganization or module rewiring between modules at network levels during drug treatment is important to systematically understand therapies and drug responses. The present article proposed a novel model of module network rewiring to characterize functional reorganization of a complex biological system, and described a new framework named as module network rewiring-analysis (MNR) for systematically studying dynamical drug sensitivity and resistance during drug treatment. MNR was used to investigate functional reorganization or rewiring on the module network, rather than molecular network or individual molecules. Our experiments on expression data of patients with Hepatitis C virus infection receiving Interferon therapy demonstrated that consistent module genes derived by MNR could be directly used to reveal new genotypes relevant to drug sensitivity, unlike the other differential analyses of gene expressions. Our results showed that functional connections and reconnections among consistent modules bridged by biological paths were necessary for achieving effective responses of a drug. The hierarchical structures of the temporal module network can be considered as spatio-temporal biomarkers to monitor the efficacy, efficiency, toxicity, and resistance of the therapy. Our study indicates that MNR is a useful tool to identify module biomarkers and further predict dynamical drug sensitivity and resistance, characterize complex dynamic processes for therapy response, and provide biologically systematic clues for pharmacogenomic applications.
Disruptions in DNA repair pathways predispose cells to accumulating DNA damage. A growing body of evidence indicates that tumors accumulate progressively more mutations in DNA repair proteins as cancers progress. DNA repair mechanisms greatly affect the response to cytotoxic treatments, so understanding those mechanisms and finding ways to turn dysregulated repair processes against themselves to induce tumor death is the goal of all DNA repair inhibition efforts. Inhibition may be direct or indirect. This burgeoning field of research is replete with promise and challenge, as more intricacies of each repair pathway are discovered. In an era of increasing concern about healthcare costs, use of DNA repair inhibitors can prove to be highly effective stewardship of R&D resources and patient expenses.
Proteomic biomarker discovery has led to the identification of numerous potential candidates for disease diagnosis, prognosis and prediction of response to therapy. However, very few of these identified candidate biomarkers reach clinical validation and go on to be routinely used in clinical practice. One particular issue with biomarker discovery is the identification of significantly changing proteins in the initial discovery experiment that do not validate when subsequently tested on separate patient sample cohorts.Here, we seek to highlight some of the statistical challenges surrounding the analysis of liquid chromatography mass spectrometry (LC-MS) proteomic data for biomarker candidate discovery. We show that common statistical algorithms run on data with low sample sizes can over-fit and yield misleading misclassification rates and area under the curve (AUC) values. A common solution to this problem is to pre-filter variables (via e.g. ANOVA and or use of correction methods such as Bonferonni, or FDR) to give a smaller data set and reduce the size of the apparent statistical challenge. However, we show that this exacerbates the problem yielding even higher performance metrics whilst reducing the predictive accuracy of the biomarker panel.To illustrate some of these limitations we have run simulation analyses with known biomarkers. For our chosen algorithm (Random Forests) we show that the above problems are substantially reduced if sufficient numbers of samples are analysed and the data are not pre-filtered. Our view is that LC-MS proteomic biomarker discovery data should be analysed without pre-filtering and that increasing the sample size in biomarker discovery experiments should be a very high priority.This article is protected by copyright. All rights reserved
That each of us is truly biologically unique, extending to even monozygotic, "identical" twins, is not fully appreciated. Now that it is possible to perform a comprehensive "omic" assessment of an individual, including one's DNA and RNA sequence and at least some characterization of one's proteome, metabolome, microbiome, autoantibodies, and epigenome, it has become abundantly clear that each of us has truly one-of-a-kind biological content. Well beyond the allure of the matchless fingerprint or snowflake concept, these singular, individual data and information set up a remarkable and unprecedented opportunity to improve medical treatment and develop preventive strategies to preserve health.
The elucidation of breast cancer subgroups and their molecular drivers requires integrated views of the genome and transcriptome from representative numbers of patients. We present an integrated analysis of copy number and gene expression in a discovery and validation set of 997 and 995 primary breast tumours, respectively, with long-term clinical follow-up. Inherited variants (copy number variants and single nucleotide polymorphisms) and acquired somatic copy number aberrations (CNAs) were associated with expression in similar to 40% of genes, with the landscape dominated by cis-and trans-acting CNAs. By delineating expression outlier genes driven in cis by CNAs, we identified putative cancer genes, including deletions in PPP2R2A, MTAP and MAP2K4. Unsupervised analysis of paired DNA-RNA profiles revealed novel subgroups with distinct clinical outcomes, which reproduced in the validation cohort. These include a high-risk, oestrogen-receptor-positive 11q13/14 cis-acting subgroup and a favourable prognosis subgroup devoid of CNAs. Trans-acting aberration hotspots were found to modulate subgroup-specific gene networks, including a TCR deletion-mediated adaptive immune response in the 'CNA-devoid' subgroup and a basal-specific chromosome 5 deletion-associated mitotic network. Our results provide a novel molecular stratification of the breast cancer population, derived from the impact of somatic CNAs on the transcriptome.
Molecular biology knowledge can be formalized and systematically represented in a computer-readable form as a comprehensive map of molecular interactions. There exist an increasing number of maps of molecular interactions containing detailed and step-wise description of various cell mechanisms. It is difficult to explore these large maps, to organize discussion of their content and to maintain them. Several efforts were recently made to combine these capabilities together in one environment, and NaviCell represents one of them.
NaviCell is a web-based environment for exploiting large maps of molecular interactions, created in CellDesigner, allowing their easy exploration, curation and maintenance. It is characterized by a combination of three essential features: (1) efficient map browsing based on Google Maps engine; (2) semantic zooming for viewing different levels of details or of abstraction of the map and (3) integrated web-based blog for collecting the community feedback. NaviCell can be easily used by experts in the field of molecular biology for studying molecular entities of their interest in the context of signaling pathways and crosstalk between pathways within a global signaling network. NaviCell allows both exploration of detailed molecular mechanisms represented on the map and a more abstract view of the map up to a top-level modular representation. NaviCell greatly facilitates curation, maintenance and updating the comprehensive maps of molecular interactions in an interactive and user-friendly fashion due to an imbedded blogging system.
NaviCell provides a user-friendly exploration of large-scale maps of molecular interactions, thanks to Google Maps and WordPress interfaces, with which many users are already familiar. Semantic zooming which is used for navigating geographical maps is adopted for molecular maps in NaviCell, making any level of visualization readable. In addition, NaviCell provides the framework for community-based curation of maps.
The high rate of clinical response to protein-kinase-targeting drugs matched to cancer patients with specific genomic alterations has prompted efforts to use cancer cell line (CCL) profiling to identify additional biomarkers of small-molecule sensitivities. We have quantitatively measured the sensitivity of 242 genomically characterized CCLs to an Informer Set of 354 small molecules that target many nodes in cell circuitry, uncovering protein dependencies that: (1) associate with specific cancer-genomic alterations and (2) can be targeted by small molecules. We have created the Cancer Therapeutics Response Portal (http://www.broadinstitute.org/ctrp) to enable users to correlate genetic features to sensitivity in individual lineages and control for confounding factors of CCL profiling. We report a candidate dependency, associating activating mutations in the oncogene β-catenin with sensitivity to the Bcl-2 family antagonist, navitoclax. The resource can be used to develop novel therapeutic hypotheses and to accelerate discovery of drugs matched to patients by their cancer genotype and lineage.
Because of globalization of the semiconductor industry, the IC fabrication is increasingly outsourced. This poses a significant risk for integrated circuits (ICs) used for security critical applications. Attackers can maliciously alter the ICs during fabrication in untrusted foundries. In the case of ICs bought externally, they may have hidden functions that users would never know. These malicious alterations and hidden functions are also referred to as "Hardware Trojan". It is extremely difficult to discover such Trojan circuits using conventional testing strategies. In this paper, we propose a non- destructive, power analysis based Trojan detection approach which is able to detect Trojan circuits in the presence of large noise. The approach is validated using 90nm FPGA (Xilinx Spartan-3E) chips. Experimental results with a 64-bit Data Encryption Standard (DES) cipher circuit show that Trojans which are 2 orders of magnitude smaller than the DES circuit can be detected by using statistic signal processing techniques.
With the advent of molecularly targeted agents, treatment of metastatic renal cell carcinoma (mRCC) has improved significantly. Agents targeting the vascular endothelial growth factor receptor (VEGFR) and the mammalian target of rapamycin complex 1(mTORC1) are more effective and less toxic than previous standards of care involving cytotoxic and cytokine therapies. Unfortunately, many patients relapse following treatment with VEGFR and mTORC1 inhibitors as a result of acquired resistance mechanisms, which are thought to lead to the reestablishment of tumor vasculature. Specifically, the loss of negative feedback loops caused by inhibition of mTORC1 lead to upregulation of downstream effectors of thephosphoinositide 3-kinase(PI3K)/AKT/mTOR pathway and subsequent activation of hypoxia-inducible factor, an activator of angiogenesis. De novoresistance involving activated PI3K signaling has also been observed.These observations have led to the development of novel agents targeting PI3K, mTORC1/2, and PI3K/mTORC1/2, which have demonstrated antitumor activity in preclinical models of RCC. Several agents-BKM120, BEZ235, and GDC-0980-are being investigated in clinical trials in patients with metastatic/advanced RCC, and similar agents are being tested in patients with solid tumors. The future success of mRCCtreatment will likely involve a combination of agents targeting the multiple pathways involved in angiogenesis, including VEGFR, PI3K, and mTORC1/2. © 2013 Wiley Periodicals, Inc.