@article {Santos700153, author = {Sean M. Santos and Mert Icyuz and Ilya Pound and Doreen William and Jingyu Guo and Brett A. McKinney and Michael Niederweis and John Rodgers and John L. Hartman IV}, title = {A humanized yeast phenomic model of deoxycytidine kinase to predict genetic buffering of nucleoside analog cytotoxicity}, elocation-id = {700153}, year = {2019}, doi = {10.1101/700153}, publisher = {Cold Spring Harbor Laboratory}, abstract = {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{\textquoteright} 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{\textquoteright},2{\textquoteright}-difluoro 2{\textquoteright}-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}, URL = {https://www.biorxiv.org/content/early/2019/07/22/700153}, eprint = {https://www.biorxiv.org/content/early/2019/07/22/700153.full.pdf}, journal = {bioRxiv} }