Finding melanoma drugs through a probabilistic knowledge graph

James P. McCusker, Michel Dumontier, Rui Yan, Sylvia He, Jonathan S. Dordick, Deborah L. McGuinness

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Metastatic cutaneous melanoma is an aggressive skin cancer with some progression-slowing treatments but no known cure. The omics data explosion has created many possible drug candidates; however, filtering criteria remain challenging, and systems biology approaches have become fragmented with many disconnected databases. Using drug, protein and disease interactions, we built an evidence-weighted knowledge graph of integrated interactions. Our knowledge graph-based system, ReDrugS, can be used via an application programming interface or web interface, and has generated 25 high-quality melanoma drug candidates. We show that probabilistic analysis of systems biology graphs increases drug candidate quality compared to non-probabilistic methods. Four of the 25 candidates are novel therapies, three of which have been tested with other cancers. All other candidates have current or completed clinical trials, or have been studied in in vivo or in vitro. This approach can be used to identify candidate therapies for use in research or personalized medicine.

Original languageEnglish
Article number106
Pages (from-to)e106
Number of pages20
JournalPeerJ Computer Science
Volume3
DOIs
Publication statusPublished - 13 Feb 2017
Externally publishedYes

Keywords

  • Melanoma
  • Knowledge graphs
  • Drug repositioning
  • Uncertainty reasoning
  • PROTEIN-INTERACTION DATABASE
  • PHASE-II TRIAL
  • MALIGNANT-MELANOMA
  • COMPREHENSIVE RESOURCE
  • GENE-EXPRESSION
  • CANCER-CELLS
  • LUNG-CANCER
  • DISCOVERY
  • APOPTOSIS
  • METASTASES

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