Optimal control nodes in disease-perturbed networks as targets for combination therapy

Most combination therapies are developed based on targets of existing drugs, which only represent a small portion of the human proteome. We introduce a network controllability-based method, OptiCon, for de novo identification of synergistic regulators as candidates for combination therapy. These reg...

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Vydáno v:Nature communications Ročník 10; číslo 1; s. 2180 - 14
Hlavní autoři: Hu, Yuxuan, Chen, Chia-hui, Ding, Yang-yang, Wen, Xiao, Wang, Bingbo, Gao, Lin, Tan, Kai
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 16.05.2019
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A549 Cells
Algorithms
Antineoplastic Combined Chemotherapy Protocols - pharmacology
Antineoplastic Combined Chemotherapy Protocols - therapeutic use
Cancer
Cancer therapies
Computational Biology - methods
Controllability
Datasets as Topic
Deregulation
Disease
Disease control
Drug resistance
Drug Synergism
Drug Therapy, Combination - methods
Female
Gene expression
Gene Expression Profiling
Gene Expression Regulation, Neoplastic - drug effects
Gene Regulatory Networks - drug effects
Gene Regulatory Networks - genetics
Genes
HEK293 Cells
Humanities and Social Sciences
Humans
MCF-7 Cells
Models, Genetic
Molecular Targeted Therapy - methods
multidisciplinary
Mutation
Neoplasms - drug therapy
Neoplasms - genetics
Neoplasms - pathology
Optimal control
Protein Interaction Maps - drug effects
Protein Interaction Maps - genetics
Proteins
Proteomes
Regulators
Science
Science (multidisciplinary)
Side effects
Signal Transduction - drug effects
Signal Transduction - genetics
Sparsity
Stability
Therapy
ISSN:2041-1723, 2041-1723
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Abstract Most combination therapies are developed based on targets of existing drugs, which only represent a small portion of the human proteome. We introduce a network controllability-based method, OptiCon, for de novo identification of synergistic regulators as candidates for combination therapy. These regulators jointly exert maximal control over deregulated genes but minimal control over unperturbed genes in a disease. Using data from three cancer types, we show that 68% of predicted regulators are either known drug targets or have a critical role in cancer development. Predicted regulators are depleted for known proteins associated with side effects. Predicted synergy is supported by disease-specific and clinically relevant synthetic lethal interactions and experimental validation. A significant portion of genes regulated by synergistic regulators participate in dense interactions between co-regulated subnetworks and contribute to therapy resistance. OptiCon represents a general framework for systemic and de novo identification of synergistic regulators underlying a cellular state transition. Synergistic interactions may arise between regulators in complex molecular networks. Here, the authors develop OptiCon, a computational method for de novo identification of synergistic key regulators and investigate their potential roles as candidate targets for combination therapy.
AbstractList Most combination therapies are developed based on targets of existing drugs, which only represent a small portion of the human proteome. We introduce a network controllability-based method, OptiCon, for de novo identification of synergistic regulators as candidates for combination therapy. These regulators jointly exert maximal control over deregulated genes but minimal control over unperturbed genes in a disease. Using data from three cancer types, we show that 68% of predicted regulators are either known drug targets or have a critical role in cancer development. Predicted regulators are depleted for known proteins associated with side effects. Predicted synergy is supported by disease-specific and clinically relevant synthetic lethal interactions and experimental validation. A significant portion of genes regulated by synergistic regulators participate in dense interactions between co-regulated subnetworks and contribute to therapy resistance. OptiCon represents a general framework for systemic and de novo identification of synergistic regulators underlying a cellular state transition. Synergistic interactions may arise between regulators in complex molecular networks. Here, the authors develop OptiCon, a computational method for de novo identification of synergistic key regulators and investigate their potential roles as candidate targets for combination therapy.
Most combination therapies are developed based on targets of existing drugs, which only represent a small portion of the human proteome. We introduce a network controllability-based method, OptiCon, for de novo identification of synergistic regulators as candidates for combination therapy. These regulators jointly exert maximal control over deregulated genes but minimal control over unperturbed genes in a disease. Using data from three cancer types, we show that 68% of predicted regulators are either known drug targets or have a critical role in cancer development. Predicted regulators are depleted for known proteins associated with side effects. Predicted synergy is supported by disease-specific and clinically relevant synthetic lethal interactions and experimental validation. A significant portion of genes regulated by synergistic regulators participate in dense interactions between co-regulated subnetworks and contribute to therapy resistance. OptiCon represents a general framework for systemic and de novo identification of synergistic regulators underlying a cellular state transition.
Most combination therapies are developed based on targets of existing drugs, which only represent a small portion of the human proteome. We introduce a network controllability-based method, OptiCon, for de novo identification of synergistic regulators as candidates for combination therapy. These regulators jointly exert maximal control over deregulated genes but minimal control over unperturbed genes in a disease. Using data from three cancer types, we show that 68% of predicted regulators are either known drug targets or have a critical role in cancer development. Predicted regulators are depleted for known proteins associated with side effects. Predicted synergy is supported by disease-specific and clinically relevant synthetic lethal interactions and experimental validation. A significant portion of genes regulated by synergistic regulators participate in dense interactions between co-regulated subnetworks and contribute to therapy resistance. OptiCon represents a general framework for systemic and de novo identification of synergistic regulators underlying a cellular state transition. Synergistic interactions may arise between regulators in complex molecular networks. Here, the authors develop OptiCon, a computational method for de novo identification of synergistic key regulators and investigate their potential roles as candidate targets for combination therapy.
Most combination therapies are developed based on targets of existing drugs, which only represent a small portion of the human proteome. We introduce a network controllability-based method, OptiCon, for de novo identification of synergistic regulators as candidates for combination therapy. These regulators jointly exert maximal control over deregulated genes but minimal control over unperturbed genes in a disease. Using data from three cancer types, we show that 68% of predicted regulators are either known drug targets or have a critical role in cancer development. Predicted regulators are depleted for known proteins associated with side effects. Predicted synergy is supported by disease-specific and clinically relevant synthetic lethal interactions and experimental validation. A significant portion of genes regulated by synergistic regulators participate in dense interactions between co-regulated subnetworks and contribute to therapy resistance. OptiCon represents a general framework for systemic and de novo identification of synergistic regulators underlying a cellular state transition.Most combination therapies are developed based on targets of existing drugs, which only represent a small portion of the human proteome. We introduce a network controllability-based method, OptiCon, for de novo identification of synergistic regulators as candidates for combination therapy. These regulators jointly exert maximal control over deregulated genes but minimal control over unperturbed genes in a disease. Using data from three cancer types, we show that 68% of predicted regulators are either known drug targets or have a critical role in cancer development. Predicted regulators are depleted for known proteins associated with side effects. Predicted synergy is supported by disease-specific and clinically relevant synthetic lethal interactions and experimental validation. A significant portion of genes regulated by synergistic regulators participate in dense interactions between co-regulated subnetworks and contribute to therapy resistance. OptiCon represents a general framework for systemic and de novo identification of synergistic regulators underlying a cellular state transition.
Synergistic interactions may arise between regulators in complex molecular networks. Here, the authors develop OptiCon, a computational method for de novo identification of synergistic key regulators and investigate their potential roles as candidate targets for combination therapy.
ArticleNumber 2180
Author Wang, Bingbo
Chen, Chia-hui
Tan, Kai
Wen, Xiao
Hu, Yuxuan
Ding, Yang-yang
Gao, Lin
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  surname: Wen
  fullname: Wen, Xiao
  organization: School of Computer Science and Technology, Xidian University
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  givenname: Bingbo
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  organization: School of Computer Science and Technology, Xidian University
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  givenname: Kai
  surname: Tan
  fullname: Tan, Kai
  email: tank1@email.chop.edu
  organization: Division of Oncology and Center for Childhood Cancer Research, 4004 CTRB, Children’s Hospital of Philadelphia, Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania
BackLink https://www.ncbi.nlm.nih.gov/pubmed/31097707$$D View this record in MEDLINE/PubMed
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Snippet Most combination therapies are developed based on targets of existing drugs, which only represent a small portion of the human proteome. We introduce a network...
Synergistic interactions may arise between regulators in complex molecular networks. Here, the authors develop OptiCon, a computational method for de novo...
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A549 Cells
Algorithms
Antineoplastic Combined Chemotherapy Protocols - pharmacology
Antineoplastic Combined Chemotherapy Protocols - therapeutic use
Cancer
Cancer therapies
Computational Biology - methods
Controllability
Datasets as Topic
Deregulation
Disease
Disease control
Drug resistance
Drug Synergism
Drug Therapy, Combination - methods
Female
Gene expression
Gene Expression Profiling
Gene Expression Regulation, Neoplastic - drug effects
Gene Regulatory Networks - drug effects
Gene Regulatory Networks - genetics
Genes
HEK293 Cells
Humanities and Social Sciences
Humans
MCF-7 Cells
Models, Genetic
Molecular Targeted Therapy - methods
multidisciplinary
Mutation
Neoplasms - drug therapy
Neoplasms - genetics
Neoplasms - pathology
Optimal control
Protein Interaction Maps - drug effects
Protein Interaction Maps - genetics
Proteins
Proteomes
Regulators
Science
Science (multidisciplinary)
Side effects
Signal Transduction - drug effects
Signal Transduction - genetics
Sparsity
Stability
Therapy
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Title Optimal control nodes in disease-perturbed networks as targets for combination therapy
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