Reference - PMID:26791325 - Predicting chemotherapeutic drug combinations through gene network profiling.
Reference summary
- PubMed ID
- PMID:26791325
- Title
- Predicting chemotherapeutic drug combinations through gene network profiling.
- Authors
- Nguyen TT, Chua JK, Seah KS, Koo SH, Yee JY, Yang EG, Lim KK, Pang SY, Yuen A, Zhang L, Ang WH, Dymock B, Lee EJ, Chen ES
- Citation
- Sci Rep 2016 Jan 21;6:18658
- Publication year
- 2016
- Abstract
- Contemporary chemotherapeutic treatments incorporate the use of several agents in combination. However, selecting the most appropriate drugs for such therapy is not necessarily an easy or straightforward task. Here, we describe a targeted approach that can facilitate the reliable selection of chemotherapeutic drug combinations through the interrogation of drug-resistance gene networks. Our method employed single-cell eukaryote fission yeast (Schizosaccharomyces pombe) as a model of proliferating cells to delineate a drug resistance gene network using a synthetic lethality workflow. Using the results of a previous unbiased screen, we assessed the genetic overlap of doxorubicin with six other drugs harboring varied mechanisms of action. Using this fission yeast model, drug-specific ontological sub-classifications were identified through the computation of relative hypersensitivities. We found that human gastric adenocarcinoma cells can be sensitized to doxorubicin by concomitant treatment with cisplatin, an intra-DNA strand crosslinking agent, and suberoylanilide hydroxamic acid, a histone deacetylase inhibitor. Our findings point to the utility of fission yeast as a model and the differential targeting of a conserved gene interaction network when screening for successful chemotherapeutic drug combinations for human cells.
