Systems biology approaches for advancing the discovery of effective drug combinations

doi:10.1186/S13321-015-0055-9

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Systems biology approaches for advancing the discovery of effective drug combinations

Journal Article•DOI•

Systems biology approaches for advancing the discovery of effective drug combinations

Karen A. Ryall¹, Aik Choon Tan¹, Aik Choon Tan²•Institutions (2)

Anschutz Medical Campus¹, Korea University²

26 Feb 2015-Journal of Cheminformatics (BioMed Central)-Vol. 7, Iss: 1, pp 7-7

TL;DR: This review focuses on the use of computational modeling, bioinformatics and high-throughput experimental methods for discovery of drug combinations, and highlights cutting-edge systems approaches, including large-scale modeling of cell signaling networks, network motif analysis, statistical association-based models, and identifying correlations in gene signatures, functional genomics.

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Abstract: 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.

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Journal Article•DOI•

Modeling polypharmacy side effects with graph convolutional networks.

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Marinka Zitnik¹, Monica Agrawal¹, Jure Leskovec¹•Institutions (1)

Stanford University¹

01 Jul 2018-Bioinformatics

TL;DR: Decagon is presented, an approach for modeling polypharmacy side effects that develops a new graph convolutional neural network for multirelational link prediction in multimodal networks and can predict the exact side effect, if any, through which a given drug combination manifests clinically.

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Abstract: Motivation The use of drug combinations, termed polypharmacy, is common to treat patients with complex diseases or co-existing conditions However, a major consequence of polypharmacy is a much higher risk of adverse side effects for the patient Polypharmacy side effects emerge because of drug-drug interactions, in which activity of one drug may change, favorably or unfavorably, if taken with another drug The knowledge of drug interactions is often limited because these complex relationships are rare, and are usually not observed in relatively small clinical testing Discovering polypharmacy side effects thus remains an important challenge with significant implications for patient mortality and morbidity Results Here, we present Decagon, an approach for modeling polypharmacy side effects The approach constructs a multimodal graph of protein-protein interactions, drug-protein target interactions and the polypharmacy side effects, which are represented as drug-drug interactions, where each side effect is an edge of a different type Decagon is developed specifically to handle such multimodal graphs with a large number of edge types Our approach develops a new graph convolutional neural network for multirelational link prediction in multimodal networks Unlike approaches limited to predicting simple drug-drug interaction values, Decagon can predict the exact side effect, if any, through which a given drug combination manifests clinically Decagon accurately predicts polypharmacy side effects, outperforming baselines by up to 69% We find that it automatically learns representations of side effects indicative of co-occurrence of polypharmacy in patients Furthermore, Decagon models particularly well polypharmacy side effects that have a strong molecular basis, while on predominantly non-molecular side effects, it achieves good performance because of effective sharing of model parameters across edge types Decagon opens up opportunities to use large pharmacogenomic and patient population data to flag and prioritize polypharmacy side effects for follow-up analysis via formal pharmacological studies Availability and implementation Source code and preprocessed datasets are at: http://snapstanfordedu/decagon

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850 citations

Cites background from "Systems biology approaches for adva..."

...In vitro experiments and clinical trials can be performed to identify drug-drug interactions (Li et al., 2015; Ryall and Tan, 2015), but systematic combinatorial screening of drug-drug interaction candidates remains challenging and expensive (Bansal et al....
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..., drug pairs that produce an exaggerated response over and beyond the additive response expected under no interaction (Ryall and Tan, 2015)....
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Journal Article•DOI•

Immuno-Oncology: Emerging Targets and Combination Therapies.

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Henry T. Marshall¹, Mustafa B. A. Djamgoz¹•Institutions (1)

Imperial College London¹

23 Aug 2018-Frontiers in Oncology

TL;DR: The breadth and quality of immunotherapeutic approaches and the types of cancers that can be treated will increase significantly in the foreseeable future.

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Abstract: Host immunity recognizes and eliminates most early tumor cells, yet immunological checkpoints, exemplified by CTLA-4, PD-1, and PD-L1, pose a significant obstacle to effective antitumor immune responses. T-lymphocyte co-inhibitory pathways influence intensity, inflammation and duration of antitumor immunity. However, tumors and their immunosuppressive microenvironments exploit them to evade immune destruction. Recent PD-1 checkpoint inhibitors yielded unprecedented efficacies and durable responses across advanced-stage melanoma, showcasing potential to replace conventional radiotherapy regimens. Neverthless, many clinical problems remain in terms of efficacy, patient-to-patient variability, and undesirable outcomes and side effects. In this review, we evaluate recent advances in the immuno-oncology field and discuss ways forward. First, we give an overview of current immunotherapy modalities, involving mainy single agents, including inhibitor monoclonal antibodies (mAbs) targeting T-cell checkpoints of PD-1 and CTLA-4. However, neoantigen recognition alone cannot eliminate tumors effectively in vivo given their inherent complex micro-environment, heterogeneous nature and stemness. Then, based mainly upon CTLA-4 and PD-1 checkpoint inhibitors as a "backbone," we cover a range of emerging ("second-generation") therapies incorporating other immunotherapies or non-immune based strategies in synergistic combination. These include targeted therapies such as tyrosine kinase inhibitors, co-stimulatory mAbs, bifunctional agents, epigenetic modulators (such as inhibitors of histone deacetylases or DNA methyltransferase), vaccines, adoptive-T-cell therapy, nanoparticles, oncolytic viruses, and even synthetic "gene circuits." A number of novel immunotherapy co-targets in pre-clinical development are also introduced. The latter include metabolic components, exosomes and ion channels. We discuss in some detail of the personalization of immunotherapy essential for ultimate maximization of clinical outcomes. Finally, we outline possible future technical and conceptual developments including realistic in vitro and in vivo models and inputs from physics, engineering, and artificial intelligence. We conclude that the breadth and quality of immunotherapeutic approaches and the types of cancers that can be treated will increase significantly in the foreseeable future.

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229 citations

Journal Article•DOI•

Combine and conquer: challenges for targeted therapy combinations in early phase trials

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Juanita Lopez¹, Udai Banerji¹•Institutions (1)

The Royal Marsden NHS Foundation Trust¹

01 Jan 2017-Nature Reviews Clinical Oncology

TL;DR: The challenges in identifying the best drug combinations and the best combination strategies, as well as the complexities of delivering these treatments to patients are explored.

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Abstract: Our increasing understanding of cancer biology has led to the development of molecularly targeted anticancer drugs. The full potential of these agents has not, however, been realised, owing to the presence of de novo (intrinsic) resistance, often resulting from compensatory signalling pathways, or the development of acquired resistance in cancer cells via clonal evolution under the selective pressures of treatment. Combinations of targeted treatments can circumvent some mechanisms of resistance to yield a clinical benefit. We explore the challenges in identifying the best drug combinations and the best combination strategies, as well as the complexities of delivering these treatments to patients. Recognizing treatment-induced toxicity and the inability to use continuous pharmacodynamically effective doses of many targeted treatments necessitates creative intermittent scheduling. Serial tumour profiling and the use of parallel co-clinical trials can contribute to understanding mechanisms of resistance, and will guide the development of adaptive clinical trial designs that can accommodate hypothesis testing, in order to realize the full potential of combination therapies.

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224 citations

Journal Article•DOI•

Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities.

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Marinka Zitnik¹, Francis Nguyen², Francis Nguyen³, Bo Wang, Jure Leskovec¹, Anna Goldenberg², Michael M. Hoffman - Show less +3 more•Institutions (3)

Stanford University¹, University of Toronto², Princess Margaret Cancer Centre³

01 Oct 2019-Information Fusion

TL;DR: In this paper, the authors describe the principles of data integration and discuss current methods and available implementations, as well as current challenges in biomedical integrative methods and their perspective on the future development of the field.

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212 citations

Journal Article•DOI•

Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities

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Marinka Zitnik¹, Francis Nguyen², Francis Nguyen³, Bo Wang, Jure Leskovec¹, Anna Goldenberg², Michael M. Hoffman - Show less +3 more•Institutions (3)

Stanford University¹, University of Toronto², Princess Margaret Cancer Centre³

30 Jun 2018-arXiv: Quantitative Methods

TL;DR: The principles of data integration are described and current methods and available implementations are discussed and examples of successful data integration in biology and medicine are provided.

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Abstract: New technologies have enabled the investigation of biology and human health at an unprecedented scale and in multiple dimensions. These dimensions include a myriad of properties describing genome, epigenome, transcriptome, microbiome, phenotype, and lifestyle. No single data type, however, can capture the complexity of all the factors relevant to understanding a phenomenon such as a disease. Integrative methods that combine data from multiple technologies have thus emerged as critical statistical and computational approaches. The key challenge in developing such approaches is the identification of effective models to provide a comprehensive and relevant systems view. An ideal method can answer a biological or medical question, identifying important features and predicting outcomes, by harnessing heterogeneous data across several dimensions of biological variation. In this Review, we describe the principles of data integration and discuss current methods and available implementations. We provide examples of successful data integration in biology and medicine. Finally, we discuss current challenges in biomedical integrative methods and our perspective on the future development of the field.

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149 citations

Cites methods from "Systems biology approaches for adva..."

...To address this combinatorial explosion of candidate drug combinations, computational methods were developed to identify drug pairs that potentially interact [282]....
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References

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Journal Article•DOI•

Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks

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Paul Shannon¹, Andrew Markiel, Owen Ozier, Nitin S. Baliga, Jonathan T. Wang, Daniel Ramage, Nada Amin, Benno Schwikowski, Trey Ideker - Show less +5 more•Institutions (1)

Institute for Systems Biology¹

01 Nov 2003-Genome Research

TL;DR: Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.

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Abstract: Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.

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32,980 citations

"Systems biology approaches for adva..." refers methods in this paper

...Unlike Loewe additivity, calculating Bliss independence does not require determination of dose–response curves for the individual compounds to determine the theoretical Gene expression data Connectivity Map (CMap) Gene expression profiles from 1309 FDA approved small molecules tested in 5 human cell lines. www.broadinstitute.org/cmap/ Gene Expression Omnibus (GEO) Public repository of gene expression data. http://www.ncbi.nlm.nih.gov/geo/ Kinase inhibitors K-Map Web tool that identifies kinase inhibitors for a set of query kinases. http://tanlab.ucdenver.edu/kMap/ Pathways Reactome Pathway database with visual representation for 21 organisms, which includes over 1500 human pathways. www.reactome.org KEGG Pathways Large collection of manually drawn pathway maps of molecular interaction networks for various biological processes. www.genome.jp/kegg/pathway.html Network visualization Cytoscape Open source software platform for network analysis and visualization. www.cytoscape.org Computational modeling Netflux Modeling and simulation tool for construction of normalized-Hill models of signaling networks from user defined species interactions. http://code.google.com/p/netflux/ CellNOpt Free software for creating logic-based models of signaling networks. www.cellnopt.org BioModels Database Repository of computational models of biological processes....
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...Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks....
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...Networks exported into Cytoscape [43], a open source software platform for network visualization, can use the Netmatch plug-in [44] to quickly search for motifs of interest....
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Journal Article•DOI•

Gene Expression Omnibus: NCBI gene expression and hybridization array data repository

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Ron Edgar¹, Michael Domrachev¹, Alex E. Lash¹•Institutions (1)

National Institutes of Health¹

01 Jan 2002-Nucleic Acids Research

TL;DR: The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data and provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-power gene expression and genomic hybridization experiments.

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Abstract: The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data. GEO provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-throughput gene expression and genomic hybridization experiments. GEO is not intended to replace in house gene expression databases that benefit from coherent data sets, and which are constructed to facilitate a particular analytic method, but rather complement these by acting as a tertiary, central data distribution hub. The three central data entities of GEO are platforms, samples and series, and were designed with gene expression and genomic hybridization experiments in mind. A platform is, essentially, a list of probes that define what set of molecules may be detected. A sample describes the set of molecules that are being probed and references a single platform used to generate its molecular abundance data. A series organizes samples into the meaningful data sets which make up an experiment. The GEO repository is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.

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10,968 citations

"Systems biology approaches for adva..." refers methods in this paper

...Unlike Loewe additivity, calculating Bliss independence does not require determination of dose–response curves for the individual compounds to determine the theoretical Gene expression data Connectivity Map (CMap) Gene expression profiles from 1309 FDA approved small molecules tested in 5 human cell lines. www.broadinstitute.org/cmap/ Gene Expression Omnibus (GEO) Public repository of gene expression data. http://www.ncbi.nlm.nih.gov/geo/ Kinase inhibitors K-Map Web tool that identifies kinase inhibitors for a set of query kinases. http://tanlab.ucdenver.edu/kMap/ Pathways Reactome Pathway database with visual representation for 21 organisms, which includes over 1500 human pathways. www.reactome.org KEGG Pathways Large collection of manually drawn pathway maps of molecular interaction networks for various biological processes. www.genome.jp/kegg/pathway.html Network visualization Cytoscape Open source software platform for network analysis and visualization. www.cytoscape.org Computational modeling Netflux Modeling and simulation tool for construction of normalized-Hill models of signaling networks from user defined species interactions. http://code.google.com/p/netflux/ CellNOpt Free software for creating logic-based models of signaling networks. www.cellnopt.org BioModels Database Repository of computational models of biological processes....
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...An advantage of this approach is the ability to query CMap with publicly available gene expression data from sources such as Gene Expression Omnibus (GEO) [53], therefore facilitating rapid drug combinations prediction for experimental validation [54]....
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Journal Article•DOI•

Error and attack tolerance of complex networks

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Réka Albert¹, Hawoong Jeong¹, Albert-László Barabási¹•Institutions (1)

University of Notre Dame¹

27 Jul 2000-Nature

TL;DR: It is found that scale-free networks, which include the World-Wide Web, the Internet, social networks and cells, display an unexpected degree of robustness, the ability of their nodes to communicate being unaffected even by unrealistically high failure rates.

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Abstract: Many complex systems display a surprising degree of tolerance against errors. For example, relatively simple organisms grow, persist and reproduce despite drastic pharmaceutical or environmental interventions, an error tolerance attributed to the robustness of the underlying metabolic network1. Complex communication networks2 display a surprising degree of robustness: although key components regularly malfunction, local failures rarely lead to the loss of the global information-carrying ability of the network. The stability of these and other complex systems is often attributed to the redundant wiring of the functional web defined by the systems' components. Here we demonstrate that error tolerance is not shared by all redundant systems: it is displayed only by a class of inhomogeneously wired networks, called scale-free networks, which include the World-Wide Web3,4,5, the Internet6, social networks7 and cells8. We find that such networks display an unexpected degree of robustness, the ability of their nodes to communicate being unaffected even by unrealistically high failure rates. However, error tolerance comes at a high price in that these networks are extremely vulnerable to attacks (that is, to the selection and removal of a few nodes that play a vital role in maintaining the network's connectivity). Such error tolerance and attack vulnerability are generic properties of communication networks.

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7,697 citations

"Systems biology approaches for adva..." refers background in this paper

...The redundancy and feedback in these networks allows for robustness of phenotype and maintenance of homeostasis [6,7]....
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Journal Article•DOI•

The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease

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Justin Lamb¹, Emily D. Crawford¹, David Peck¹, Joshua W. Modell¹, Irene C. Blat¹, Matthew J. Wrobel¹, Jim Lerner¹, Jean Philippe Brunet¹, Aravind Subramanian¹, Kenneth N. Ross¹, Michael Reich¹, Haley Hieronymus², Haley Hieronymus¹, Guo Wei¹, Guo Wei², Scott A. Armstrong², Scott A. Armstrong³, Stephen J. Haggarty¹, Stephen J. Haggarty², Paul A. Clemons¹, Ru Wei¹, Steven A. Carr¹, Eric S. Lander², Eric S. Lander¹, Todd R. Golub - Show less +21 more•Institutions (3)

Massachusetts Institute of Technology¹, Harvard University², Boston Children's Hospital³

29 Sep 2006-Science

TL;DR: The first installment of a reference collection of gene-expression profiles from cultured human cells treated with bioactive small molecules is created, and it is demonstrated that this “Connectivity Map” resource can be used to find connections among small molecules sharing a mechanism of action, chemicals and physiological processes, and diseases and drugs.

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Abstract: To pursue a systematic approach to the discovery of functional connections among diseases, genetic perturbation, and drug action, we have created the first installment of a reference collection of gene-expression profiles from cultured human cells treated with bioactive small molecules, together with pattern-matching software to mine these data. We demonstrate that this "Connectivity Map" resource can be used to find connections among small molecules sharing a mechanism of action, chemicals and physiological processes, and diseases and drugs. These results indicate the feasibility of the approach and suggest the value of a large-scale community Connectivity Map project.

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4,429 citations

Journal Article•DOI•

MET Amplification Leads to Gefitinib Resistance in Lung Cancer by Activating ERBB3 Signaling

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Jeffrey A. Engelman¹, Kreshnik Zejnullahu, Tetsuya Mitsudomi, Youngchul Song, Courtney Hyland, Joon Oh Park, Neal I. Lindeman, Christopher-Michael Gale, Xiaojun Zhao, James J. Christensen, Takayuki Kosaka, Alison J. Holmes, Andrew M. Rogers, Federico Cappuzzo, Tony Mok, Charles Lee, Bruce E. Johnson, Lewis C. Cantley, Pasi A. Jänne - Show less +15 more•Institutions (1)

Harvard University¹

18 May 2007-Science

TL;DR: It is proposed that MET amplification may promote drug resistance in other ERBB-driven cancers as well after it was found that amplification of MET causes gefitinib resistance by driving ERBB3 (HER3)–dependent activation of PI3K, a pathway thought to be specific to EGFR/ERBB family receptors.

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Abstract: The epidermal growth factor receptor (EGFR) kinase inhibitors gefitinib and erlotinib are effective treatments for lung cancers with EGFR activating mutations, but these tumors invariably develop drug resistance. Here, we describe a gefitinib-sensitive lung cancer cell line that developed resistance to gefitinib as a result of focal amplification of the MET proto-oncogene. inhibition of MET signaling in these cells restored their sensitivity to gefitinib. MET amplification was detected in 4 of 18 (22%) lung cancer specimens that had developed resistance to gefitinib or erlotinib. We find that amplification of MET causes gefitinib resistance by driving ERBB3 (HER3)–dependent activation of PI3K, a pathway thought to be specific to EGFR/ERBB family receptors. Thus, we propose that MET amplification may promote drug resistance in other ERBB-driven cancers as well.

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4,218 citations

"Systems biology approaches for adva..." refers background in this paper

...In cancer, drug resistance can occur through mutation of the drug target [11], amplification of an alternate pathway [12], or intrinsic resistance of a subset of the cancer cells [13]....
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