A genome-scale screen reveals context-dependent ovarian cancer sensitivity to miRNA overexpression

Benjamin B. Shields, Chad V. Pecot, Hua Gao, Elizabeth McMillan, Malia Potts, Christa Nagel, Scott Purinton, Ying Wang, Cristina Ivan, Hyun Seok Kim, Robert J. Borkowski, Shaheen Khan, Cristian Rodriguez-Aguayo, Gabriel Lopez-Berestein, Jayanthi Lea, Adi Gazdar, Keith A. Baggerly, Anil K. Sood, Michael A. White

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Large-scale molecular annotation of epithelial ovarian cancer (EOC) indicates remarkable heterogeneity in the etiology of that disease. This diversity presents a significant obstacle against intervention target discovery. However, inactivation of miRNA biogenesis is commonly associated with advanced disease. Thus, restoration of miRNA activity may represent a common vulnerability among diverse EOC oncogenotypes. To test this, we employed genome-scale, gain-of-function, miRNA mimic toxicity screens in a large, diverse spectrum of EOC cell lines. We found that all cell lines responded to at least some miRNA mimics, but that the nature of the miRNA mimics provoking a response was highly selective within the panel. These selective toxicity profiles were leveraged to define modes of action and molecular response indicators for miRNA mimics with tumor-suppressive characteristics in vivo. A mechanistic principle emerging from this analysis was sensitivity of EOC to miRNA-mediated release of cell fate specification programs, loss of which may be a prerequisite for development of this disease.

Original languageEnglish (US)
Pages (from-to)1-17
Number of pages17
JournalMolecular Systems Biology
Volume11
Issue number12
DOIs
StatePublished - Dec 1 2015

Keywords

  • cancer
  • cancer genetics
  • miRNA
  • microRNA
  • ovarian cancer

ASJC Scopus subject areas

  • General Agricultural and Biological Sciences
  • Information Systems
  • Applied Mathematics
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • Computational Theory and Mathematics

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