RT Journal Article
SR Electronic
T1 A humanized yeast phenomic model of deoxycytidine kinase to predict genetic buffering of nucleoside analog cytotoxicity
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 700153
DO 10.1101/700153
A1 Sean M. Santos
A1 Mert Icyuz
A1 Ilya Pound
A1 Doreen William
A1 Jingyu Guo
A1 Brett A. McKinney
A1 Michael Niederweis
A1 John Rodgers
A1 John L. Hartman IV
YR 2019
UL http://biorxiv.org/content/early/2019/07/22/700153.abstract
AB Knowledge about synthetic lethality can be applied to enhance the efficacy of anti-cancer 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 anti-tumor efficacy. Human deoxycytidine kinase (dCK) was conditionally expressed in the S. 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-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.AraCcytarabine; cytosine arabinosideCPPsCell proliferation parameters: parameters of the logistic growth equation used to fit cell proliferation data obtained by Q-HTCP. The CPPs used to assess gene interaction in this study were K (carrying capacity) and L (time required to reach half of carrying capacity) [7-9,38].DAmPDecreased Abundance of mRNA Production: refers to a method of making YKD alleles, where the 3’ UTR of essential genes is disrupted, reducing mRNA stability and gene dosage [291].dCKdeoxycytidine kinasedCMPdeoxycytidine monophosphateDEDeletion enhancer: gene loss of function (knockout or knockdown) that results in enhancement / increase of drug sensitivity [9].dFdC2’,2’-difluoro 2’-deoxycytidine, gemcitabinedNTPdeoxyribonucleotide triphosphateDSDeletion suppressor: gene loss of function (knockout or knockdown) that results in suppression / reduction of drug sensitivity [9].ESCRTendosomal sorting complex required for transportGARP complexGolgi-associated retrograde protein complex.gCSIThe Genentech Cell Line Screening Initiative: One of two pharmacogenomics datasets used in this study (https://pharmacodb.pmgenomics.ca/datasets/4).GDSC1000Genomics of Drug Sensitivity in Cancer: One of two pharmacogenomics datasets used in this study (https://pharmacodb.pmgenomics.ca/datasets/5)GOGene ontologyGTFGene ontology term finder: an algorithm to assess GO term enrichment amongst a list of genes; applied to REMc (clustering) results [41].GTAGene ontology term averaging: an assessment of GO term function obtained by averaging the gene interaction values for all genes of a GO termGTA valueGene ontology term average valuegtaSDstandard deviation of GTA valueGTA score(GTA value - gtaSD)HaLhematopoietic & lymphoid tissueHDACHistone deacetylase complexHLDHuman-like media with dextrose [8]: the yeast media used in this study.INTInteraction scoreNDKnucleoside diphosphate kinaseOESOverexpression sensitivity: refers to association of increased gene expression with drug sensitivity in pharmacogenomics data [33].PharmacoDBThe resource used for cancer pharmacogenomics analysis [33].PPODPrinceton protein orthology databaseQ-HTCPQuantitative high throughput cell array phenotyping: a method of imaging, image analysis, and growth curve fitting to obtain cell proliferation parameters [7,38].RefReference: the genetic background from which the YKO/KD library was derivedREMcRecursive expectation maximization clustering: a probabilistic clustering algorithm that determines a discrete number of clusters from a data matrix [40].RNRribonucleotide reductaseSDStandard deviationSGASynthetic genetic arraySGDSaccharomyces genome databaseUESUnderexpression sensitivity: refers to association of reduced gene expression with drug sensitivity in pharmacogenomics data [33].YKOYeast knockoutYKDYeast knockdown: DAmP allelesYKO/KDYeast knockout or knockdown