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J Am Med Inform Assoc. 2016 Jul;23(4):681-91. doi: 10.1093/jamia/ocw007. Epub 2016 Mar 28.

A network-based drug repositioning infrastructure for precision cancer medicine through targeting significantly mutated genes in the human cancer genomes.

Author information

1
Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37203, USA.
2
Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37203, USA Chemical and Physical Biology Program, Vanderbilt University School of Medicine, Nashville, TN 37232, USA.
3
Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37203, USA Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA Department of Psychiatry, Vanderbilt University School of Medicine, Nashville, TN 37212, USA Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA zhongming.zhao@uth.tmc.edu.

Abstract

OBJECTIVE:

Development of computational approaches and tools to effectively integrate multidomain data is urgently needed for the development of newly targeted cancer therapeutics.

METHODS:

We proposed an integrative network-based infrastructure to identify new druggable targets and anticancer indications for existing drugs through targeting significantly mutated genes (SMGs) discovered in the human cancer genomes. The underlying assumption is that a drug would have a high potential for anticancer indication if its up-/down-regulated genes from the Connectivity Map tended to be SMGs or their neighbors in the human protein interaction network.

RESULTS:

We assembled and curated 693 SMGs in 29 cancer types and found 121 proteins currently targeted by known anticancer or noncancer (repurposed) drugs. We found that the approved or experimental cancer drugs could potentially target these SMGs in 33.3% of the mutated cancer samples, and this number increased to 68.0% by drug repositioning through surveying exome-sequencing data in approximately 5000 normal-tumor pairs from The Cancer Genome Atlas. Furthermore, we identified 284 potential new indications connecting 28 cancer types and 48 existing drugs (adjusted P < .05), with a 66.7% success rate validated by literature data. Several existing drugs (e.g., niclosamide, valproic acid, captopril, and resveratrol) were predicted to have potential indications for multiple cancer types. Finally, we used integrative analysis to showcase a potential mechanism-of-action for resveratrol in breast and lung cancer treatment whereby it targets several SMGs (ARNTL, ASPM, CTTN, EIF4G1, FOXP1, and STIP1).

CONCLUSIONS:

In summary, we demonstrated that our integrative network-based infrastructure is a promising strategy to identify potential druggable targets and uncover new indications for existing drugs to speed up molecularly targeted cancer therapeutics.

KEYWORDS:

cancer genome; drug-gene signatures; network-based drug repositioning; precision cancer medicine; significantly mutated genes

PMID:
27026610
PMCID:
PMC6370253
DOI:
10.1093/jamia/ocw007
[Indexed for MEDLINE]
Free PMC Article

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