Multi-omic data interpretation to repurpose subtype specific drug candidates for breast cancer

Beste Turanli, Kubra Karagoz, Gholamreza Bidkhori, Raghu Sinha, Michael L. Gatza, Mathias Uhlen, Adil Mardinoglu, Kazim Yalcin Arga

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

Triple-negative breast cancer (TNBC), which is largely synonymous with the basal-like molecular subtype, is the 5th leading cause of cancer deaths for women in the United States. The overall prognosis for TNBC patients remains poor given that few treatment options exist; including targeted therapies (not FDA approved), and multi-agent chemotherapy as standard-of-care treatment. TNBC like other complex diseases is governed by the perturbations of the complex interaction networks thereby elucidating the underlying molecular mechanisms of this disease in the context of network principles, which have the potential to identify targets for drug development. Here, we present an integrated “omics” approach based on the use of transcriptome and interactome data to identify dynamic/active protein-protein interaction networks (PPINs) in TNBC patients. We have identified three highly connected modules, EED, DHX9, and AURKA, which are extremely activated in TNBC tumors compared to both normal tissues and other breast cancer subtypes. Based on the functional analyses, we propose that these modules are potential drivers of proliferation and, as such, should be considered candidate molecular targets for drug development or drug repositioning in TNBC. Consistent with this argument, we repurposed steroids, anti-inflammatory agents, anti-infective agents, cardiovascular agents for patients with basal-like breast cancer. Finally, we have performed essential metabolite analysis on personalized genome-scale metabolic models and found that metabolites such as sphingosine-1phosphate and cholesterol-sulfate have utmost importance in TNBC tumor growth.

Original languageEnglish (US)
Article number420
JournalFrontiers in Genetics
Volume10
Issue numberMAY
DOIs
StatePublished - Jan 1 2019

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All Science Journal Classification (ASJC) codes

  • Molecular Medicine
  • Genetics
  • Genetics(clinical)

Cite this

Turanli, B., Karagoz, K., Bidkhori, G., Sinha, R., Gatza, M. L., Uhlen, M., Mardinoglu, A., & Arga, K. Y. (2019). Multi-omic data interpretation to repurpose subtype specific drug candidates for breast cancer. Frontiers in Genetics, 10(MAY), [420]. https://doi.org/10.3389/fgene.2019.00420