Research Papers:

Differential prioritization of therapies to subtypes of triple negative breast cancer using a systems medicine method

Henri Wathieu, Naiem T. Issa, Aileen I. Fernandez, Manisha Mohandoss, Deanna M. Tiek, Jennifer L. Franke, Stephen W. Byers, Rebecca B. Riggins and Sivanesan Dakshanamurthy _

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Oncotarget. 2017; 8:92926-92942. https://doi.org/10.18632/oncotarget.21669

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Henri Wathieu1, Naiem T. Issa1,*, Aileen I. Fernandez1,*, Manisha Mohandoss2, Deanna M. Tiek1, Jennifer L. Franke1, Stephen W. Byers1,2, Rebecca B. Riggins1 and Sivanesan Dakshanamurthy1,2

1Georgetown-Lombardi Comprehensive Cancer Center, Department of Oncology, Georgetown University Medical Center, Washington, DC, 20057 USA

2Department of Biochemistry and Molecular Biology, Georgetown University, Washington, DC, 20057 USA

*These authors have contributed equally to this work

Correspondence to:

Sivanesan Dakshanamurthy, email: [email protected]

Keywords: triple negative breast cancer, systems biology, gene expression analysis, molecular subtyping, drug development

Received: April 02, 2017    Accepted: September 08, 2017    Published: October 09, 2017


Triple negative breast cancer (TNBC) is a group of cancers whose heterogeneity and shortage of effective drug therapies has prompted efforts to divide these cancers into molecular subtypes. Our computational platform, entitled GenEx-TNBC, applies concepts in systems biology and polypharmacology to prioritize thousands of approved and experimental drugs for therapeutic potential against each molecular subtype of TNBC. Using patient-based and cell line-based gene expression data, we constructed networks to describe the biological perturbation associated with each TNBC subtype at multiple levels of biological action. These networks were analyzed for statistical coincidence with drug action networks stemming from known drug-protein targets, while accounting for the direction of disease modulation for coinciding entities. GenEx-TNBC successfully designated drugs, and drug classes, that were previously shown to be broadly effective or subtype-specific against TNBC, as well as novel agents. We further performed biological validation of the platform by testing the relative sensitivities of three cell lines, representing three distinct TNBC subtypes, to several small molecules according to the degree of predicted biological coincidence with each subtype. GenEx-TNBC is the first computational platform to associate drugs to diseases based on inverse relationships with multi-scale disease mechanisms mapped from global gene expression of a disease. This method may be useful for directing current efforts in preclinical drug development surrounding TNBC, and may offer insights into the targetable mechanisms of each TNBC subtype.

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