A Humanized Yeast Phenomic Model of Deoxycytidine Kinase to Predict Genetic Buffering of Nucleoside Analog Cytotoxicity.

Santos Santos,Icyuz Icyuz,Pound Pound,Rodgers Rodgers,Niederweis Niederweis,Iv Iv,Mckinney Mckinney,William William,Guo Guo

doi:10.3390/genes10100770

Santos Santos, Icyuz Icyuz + Show 7 more

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https://doi.org/10.3390/genes10100770

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Journal: Genes	Publication Date: Sep 30, 2019
Citations: 4	License type: CC BY 4.0

Affiliation: University of Alabama at Birmingham, University of Tulsa

Abstract

Knowledge about synthetic lethality can be applied to enhance the efficacy of anticancer therapies in individual patients harboring genetic alterations in their cancer that specifically render it vulnerable. We investigated the potential for high-resolution phenomic analysis in yeast to predict such genetic vulnerabilities by systematic, comprehensive, and quantitative assessment of drug–gene interaction for gemcitabine and cytarabine, substrates of deoxycytidine kinase that have similar molecular structures yet distinct antitumor efficacy. Human deoxycytidine kinase (dCK) was conditionally expressed in the Saccharomyces cerevisiae genomic library of knockout and knockdown (YKO/KD) strains, to globally and quantitatively characterize differential drug–gene interaction for gemcitabine and cytarabine. Pathway enrichment analysis revealed that autophagy, histone modification, chromatin remodeling, and apoptosis-related processes influence gemcitabine specifically, while drug–gene interaction specific to cytarabine was less enriched in gene ontology. Processes having influence over both drugs were DNA repair and integrity checkpoints and vesicle transport and fusion. Non-gene ontology (GO)-enriched genes were also informative. Yeast phenomic and cancer cell line pharmacogenomics data were integrated to identify yeast–human homologs with correlated differential gene expression and drug efficacy, thus providing a unique resource to predict whether differential gene expression observed in cancer genetic profiles are causal in tumor-specific responses to cytotoxic agents.

Highlights

Genomics has enabled targeted therapy aimed at cancer driver genes and oncogenic addiction [1], yet traditional cytotoxic chemotherapeutic agents remain among the most widely used and efficacious anticancer therapies [2]
To apply it for analysis of deoxycytidine kinase (dCK) substrates, a tetracycline-inducible human dCK allele was introduced into the complete yeast knockout and knockdown (YKO/KD) library by the synthetic genetic array method [29,48] (Figure 1B)
The dependence of gemcitabine and cytarabine toxicity on dCK expression was demonstrated for the reference strain (Figure 2A–F), as the two nucleosides exerted cytotoxicity only if dCK was induced by the addition of doxycycline

Summary

Introduction

Genomics has enabled targeted therapy aimed at cancer driver genes and oncogenic addiction [1], yet traditional cytotoxic chemotherapeutic agents remain among the most widely used and efficacious anticancer therapies [2]. Changes in the genetic network underlying cancer can produce vulnerabilities to cytotoxic chemotherapy that further influence the therapeutic window and provide additional insight into their mechanisms of action [3,4]. Genes 2019, 10, 770 gene expression, while classic targeted therapies are directed primarily at driver genes (Figure 1A). Quantitative high throughput cell array phenotyping of the yeast knockout and knockdown libraries provides a phenomic means for systems level, high-resolution modeling of gene interaction [5,6,7,8,9], which is applied here to predict cancer-relevant drug–gene interaction through integration with cancer pharmacogenomics resources (Figure 1B). The anticancer agents have tissue-specific efficacy ranging from solid tumors to leukemias, yet details about how these agents confer differential activity are unknown [10,11]

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