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Bioinformatics. 2019 Feb 15. pii: btz109. doi: 10.1093/bioinformatics/btz109. [Epub ahead of print]

Driver Network as a Biomarker: Systematic integration and network modeling of multi-omics data to derive driver signaling pathways for drug combination prediction.

Author information

1
Department of Systems Medicine and Bioengineering, Houston Methodist Research Institute; Weill Cornell Medicine of Cornell University, Houston, TX, USA.
2
Houston Methodist Cancer Center, Houston Methodist Hospital, Houston, TX, USA.
3
Center for Clinical and Translational Cancer Research, Texas A&M Health Sciences Center, Institute of Biosciences and Technology, Houston, TX, USA.
4
Immunobiology & Transplant Science Center, Houston Methodist Cancer Center, and Department of Urology, Houston Methodist Hospital, Houston, TX 77030.  Department of Medicine, Weill Cornell Medicine of Cornell University, New York, NY, USA.
5
Department of Genitourinary Medical Oncology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
6
Department of Medical Oncology, Thomas Jefferson University, Philadelphia, PA, USA.

Abstract

MOTIVATION:

Drug combinations that simultaneously suppress multiple cancer driver signaling pathways increase therapeutic options and may reduce drug resistance. We have developed a computational systems biology tool, DrugComboExplorer, to identify driver signaling pathways and predict synergistic drug combinations by integrating the knowledge embedded in vast amounts of available pharmacogenomics and omics data.

RESULTS:

This tool generates driver signaling networks by processing DNA sequencing, gene copy number, DNA methylation, and RNA-seq data from individual cancer patients using an integrated pipeline of algorithms, including bootstrap aggregating-based Markov random field, weighted co-expression network analysis, and supervised regulatory network learning. It uses a systems pharmacology approach to infer the combinatorial drug efficacies and synergy mechanisms through drug functional module-induced regulation of target expression analysis. Application of our tool on diffuse large B-cell lymphoma and prostate cancer demonstrated how synergistic drug combinations can be discovered to inhibit multiple driver signaling pathways. Compared to existing computational approaches, DrugComboExplorer had higher prediction accuracy based on in vitro experimental validation and probability concordance index. These results demonstrate that our network-based drug efficacy screening approach can reliably prioritize synergistic drug combinations for cancer and uncover potential mechanisms of drug synergy, warranting further studies in individual cancer patients to derive personalized treatment plans.

AVAILABILITY:

DrugComboExplorer is available at https://github.com/Roosevelt-PKU/drugcombinationprediction.

SUPPLEMENTARY INFORMATION:

Supplementary data will be available at Bioinformatics online.

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