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A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
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Predicting chemotherapeutic drug combinations through gene network profiling

Thi Thuy Trang Nguyen1,2, Jacqueline Kia Kee Chua1,3, Kwi Shan Seah1,2

  • 1Department of Biochemistry, National University of Singapore, Singapore.

Scientific Reports
|January 22, 2016
PubMed

Insights

This study uses fission yeast to identify effective chemotherapy drug combinations. Combining doxorubicin with cisplatin and suberoylanilide hydroxamic acid shows promise for treating gastric cancer.

Area of Science:

  • Molecular Biology
  • Cancer Research
  • Yeast Genetics

Background:

  • Chemotherapy often uses drug combinations, but selecting optimal agents is challenging.
  • Understanding drug resistance gene networks is crucial for effective combination therapy.

Purpose of the Study:

  • To develop a targeted approach for selecting chemotherapeutic drug combinations.
  • To identify synergistic drug combinations for cancer treatment using a model organism.

Main Methods:

  • Utilized fission yeast (Schizosaccharomyces pombe) as a model for proliferating cells.
  • Employed a synthetic lethality workflow to delineate drug resistance gene networks.
  • Assessed genetic overlap between doxorubicin and six other drugs with varied mechanisms.

Main Results:

  • Identified drug-specific ontological sub-classifications via relative hypersensitivity computations.
  • Demonstrated that human gastric adenocarcinoma cells are sensitized to doxorubicin by cisplatin and suberoylanilide hydroxamic acid.
  • Validated the utility of fission yeast in modeling conserved gene interaction networks.

Conclusions:

  • Fission yeast serves as an effective model for screening chemotherapeutic drug combinations.
  • Targeting conserved gene interaction networks can lead to successful drug combinations for human cancers.
  • Concomitant treatment with cisplatin and suberoylanilide hydroxamic acid enhances doxorubicin efficacy.

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