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Nat Commun. 2019 May 16;10(1):2180. doi: 10.1038/s41467-019-10215-y.

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

Hu Y1,2, Chen CH2, Ding YY2, Wen X1, Wang B1, Gao L3, Tan K4,5,6.

Author information

1
School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi, 710071, China.
2
Division of Oncology and Center for Childhood Cancer Research, 4004 CTRB, Children's Hospital of Philadelphia, 3501 Civic Center Boulevard, Philadelphia, PA, 19104, USA.
3
School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi, 710071, China. lgao@mail.xidian.edu.cn.
4
Division of Oncology and Center for Childhood Cancer Research, 4004 CTRB, Children's Hospital of Philadelphia, 3501 Civic Center Boulevard, Philadelphia, PA, 19104, USA. tank1@email.chop.edu.
5
Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA. tank1@email.chop.edu.
6
Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA. tank1@email.chop.edu.

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.

PMID:
31097707
PMCID:
PMC6522545
DOI:
10.1038/s41467-019-10215-y
[Indexed for MEDLINE]
Free PMC Article

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