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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Genet. | doi: 10.3389/fgene.2019.00420

Multi-Omic Data Interpretation to Repurpose Subtype Specific Drug Candidates for Breast Cancer

  • 1Department of Bioengineering, Faculty of Engineering, Marmara University, Turkey
  • 2Science for Life Laboratory (SciLifeLab), Sweden
  • 3Department of Bioengineering, Istanbul Medeniyet University, Turkey
  • 4Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, United States
  • 5Dental Institute, King's College London, United Kingdom
  • 6Department of Biochemistry and Molecular Biology, Pennsylvania State University, United States
  • 7Science for Life Laboratory, Royal Institute of Technology, Sweden
  • 8Department of Chemical and Biological Engineering, Chalmers University of Technology, Sweden
  • 9Department of Bioengineering, Marmara University, Turkey

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 USA. The overall prognosis for TNBC patients remains poor given that few treatment options exist, including no FDA approved targeted therapies, and standard-of-care treatment remains multi-agent chemotherapy. TNBC like other complex diseases is governed by the perturbations of the complex interaction networks, therefore, elucidating the underlying molecular mechanisms of this disease in the context of network principles has the potential to identify targets for drug development. Here, we presented an integrated “omics” approach based on the use of transcriptome and interactome data to identify dynamic/active protein-protein interaction networks in TNBC patients. We have identified three highly connected modules, EED, DHX9 and AURKA, which are highly activated in TNBC tumors compared to both normal tissues and other breast cancer subtypes. Based on functional analyses, we proposed that these modules are potential drivers of proliferation and, as such, should be considered as 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-1-phosphate and cholesterol-sulfate have utmost important in TNBC tumor growth.

Keywords: breast cancer, Drug Repositioning, non-cancer therapeutics, basal subtype, personalized metabolic models

Received: 23 Nov 2018; Accepted: 17 Apr 2019.

Edited by:

Junbai Wang, Oslo University Hospital, Norway

Reviewed by:

Woonyoung Choi, The Johns Hopkins Hospital, Johns Hopkins Medicine, United States
Diego Bonatto, Center of Biotechnology, Faculty of Veterinary Medicine, Federal University of Rio Grande do Sul, Brazil  

Copyright: © 2019 TURANLI, Karagoz, Bidkhori, Sinha, Gatza, Uhlen, Mardinoglu and Arga. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
PhD. Adil Mardinoglu, Science for Life Laboratory (SciLifeLab), Stockholm, Stockholm, Sweden,
PhD. Kazim Yalcin Arga, Marmara University, Department of Bioengineering, Kadıköy, 34722, Turkey,